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Mining the Census Data for the Borough of Merton
An Interactive Qualifying Project Report
submitted to the Faculty
of the
WORCESTER POLYTECHNIC INSTITUTE
in partial fulfilment of the requirements for the
Degree of Bachelor of Science
by
A rlY\ Ian Ferguson
Rebecca Gougian 040 641, David Govonlu
Russell Souza
Date: February 28, 2004
his Professor Paul Davis 7&. Professor Kevin C ements Abstract
Having obtained 2001 census data, the London Borough of Merton desired a small area analysis of deprivation and improved data presentation. To achieve this, an Intranet website was created to display key statistics and analyses, including updated demographic information and a profile of poverty. In addition, the construction of data maps made possible the examination of the spatial dimensions of poverty. These accomplishments provided Merton's local authority with a detailed analysis of deprivation and a central location for census statistics.
ii TABLE OF CONTENTS ABSTRACT II TABLE OF CONTENTS III TABLE OF FIGURES V TABLE OF TABLES V EXECUTIVE SUMMARY VI CHAPTER 1: INTRODUCTION 1 CHAPTER 2: BACKGROUND INFORMATION 3 2.1 SPONSORING AGENCY 3 2.2 CENSUS 4 2.2.1 History 4 2.2.2 Population and Residents 5 2.2.3 Housing 7 2.2.4 Economics and Education 7 2.2.5 Transportation 8 2.2.6 Health 8 2.3 USES OF CENSUS DATA 9 2.3.1 Community Planning 9 2.3.2 Allocation of Services 9 2.4 DEFINING POVERTY 11 2.4.1 General Measures 11 2.4.2 Indicators in London 12 2.4.3 Multiple Indices of Deprivation 13 2.4.4 Distribution of Poverty 14 2.5 POVERTY MAPPING IN ECUADOR 14 2.6 GEOGRAPHICAL INFORMATION SYSTEMS 16 2.6.1 Objectives 17 2.6.2 Operations 19 2.6.3 Scaling Maps 20
CHAPTER 3: METHODOLOGY 22
3.1 PRESENTATION OF CENSUS DATA 22 3.1.1 Data Access System 22 3.1.2 What makes an effective GIS map layer? 24 3.1.3 Merton's Population Update 28 3.2 ANALYSIS OF POVERTY 29 3.2.1 Indicators of Poverty 29 3.2.2 Comparison Levels 30 3.2.3 Direct Observation 31
CHAPTER 4: CENSUS DATA PRESENTATION 33
4.1 DATA ACCESS SYSTEM 33 4.1.1 Other Methods of Displaying Census Data 33 4.1.2 Components 34 4.1.3 Technologies 35 4.1.4 Categorisation of Census Data 36 4.2 GEOGRAPHICAL INFORMATION SYSTEMS MAPS 37 4.2.1 Important Variables to Map 37 4.2.2 Map Colours 39 4.2.3 Map Scaling 39 4.2.4 Clickable GIS Maps 40 4.3 MERTON'S POPULATION UPDATE 41 4.4 CENSUS DATA 42 4.4.1 Design 42 4.4.2 Postcode Lookup 46 4.5 TUTORIAL 48 CHAPTER 5: ANALYSIS OF POVERTY IN LONDON 49
5.1 TECHNIQUES OF ANALYSIS 49 5.1.1 Indicators of Poverty 49 5.1.2 Analysing Indictors of Poverty 50 5.2 SELECTING INDICATORS OF POVERTY 51 5.2.1 Census Data vs. Other Types of Data 52 5.2.2 Availability and Validity of Indicators 53 5.2.3 Indicators for the Poverty Profile 53 5.3 COMBING DEPRIVATION INDICATORS 54 5.3.1 The London Index of Deprivation 54 5.3.2 An alternative index of deprivation 55 5.3.3 Results obtained using the census index 59 5.4 GEOGRAPHICAL COMPARISONS 62 5.4.1 London Level 62 5.4.2 Ward Level 65 5.4.3 Output Area Level 68 5.5 DIRECT OBSERVATION 72 5.5.1 Mitcham Centre 73 5.5.2 Haydons Road 74 5.5.3 Abbey 75 5.5.4 Affluent Neighbourhoods 76 CHAPTER 6: CONCLUSIONS 78
6.1 SINGLE RESOURCE FOR CENSUS INFORMATION 78 6.2 MAPPING STYLES AND DATA PRESENTATION 78 6.3 POVERTY ANALYSIS 79 REFERENCES 82 APPENDIX A — DEGREE, EXTENT, AND INTENSITY 85 APPENDIX B - COMBINED RATINGS FOR OUTPUT AREAS IN MERTON 97 APPENDIX C: WHAT IS AN INTERACTIVE QUALIFYING PROJECT? 110 DIGITAL APPENDIX D: POPULATION PROFILE 112 DIGITAL APPENDIX E: POVERTY PROFILE 112 DIGITAL APPENDIX F: GIS MAPS 112 DIGITAL APPENDIX G: MAPINFO WORKSPACES 113 DIGITAL APPENDIX H: IQP REPORT 113 DIGITAL APPENDIX I: WEBSITE FILES 113
iv Table of Figures Figure 1: Sample map layer of the Merton's wards and output areas. 18 Figure 2: Internet Survey 23 Figure 3: Indicator Evaluation Survey 25 Figure 4: Map Evaluation Survey 27 Figure 5: Planned Observation points 32 Figure 6: The main webpage for Merton's Intranet Data Access System 35 Figure 7: Sample page from the population census profile 41 Figure 8: Image from data access system describing ward selection process 43 Figure 9: Image displaying one section of table selection process 44 Figure 10: Image from data access system 45 Figure 11: Image form data access system 46 Figure 12: Image from data access system 47 Figure 13: Image from www.multimap.com from Postcode sw191ns 47 Figure 14: Ward level map of London using the census-based index 56 Figure 15: Ward level map of London using the London Index of Deprivation 56 Figure 16: Ward level map of London using the Indices of Deprivation 57 Figure 17: Unemployment Rate by ward for Greater London 63 Figure 18: Unemployment Rate by OA for Southwest London 63 Figure 19: Deprivation Index by ward for Greater London 64 Figure 20: Deprivation Index by output area for Southwest London 65 Figure 21: Unemployment Rate by Ward 66 Figure 22: Percentage of Households Experiencing Overcrowding by Ward 66 Figure 23: Percentage of Households without Sole Use of Toilet by Ward 67 Figure 24: Merton Deprivation Index by Ward 68 Figure 25: Merton Deprivation Index by OA 69 Figure 26: Percentage of Households without Sole Use of Toilet by OA 69 Figure 27: Unemployment Rate by OA 71 Figure 28: Percentage of Households Experiencing Overcrowding by Ward 71 Figure 29: Mitcham centre 73 Figure 30: Housing complex on Western Road 74 Figure 31: Haydons Road 75 Figure 32: Boarded up home on Gap Street 75 Figure 33: Industrial buildings on Jubilee Street in Abbey Ward 76 Figure 34: Residential locations in Abbey off of Liberty Street. 76 Figure 35: Affluent homes in the different boroughs of Merton. 77
Table of Tables Table 1: Census Variable Rankings 38 Table 2: Unemployment Rate Accuracy Comparisons 40 Table 3: Ward statistics summary based on income support data 60 Table 4: Degree, Extent, and Intensity of Deprivation based on pay check data 61 Table 5: Hotspots of Deprivation based on the pay check index 62 Executive Summary
Having recently received 2001 census results, Merton was faced with the challenge of incorporating this information into the decision-making processes of the various departments that make up the local authority. To achieve this, the WPI students created a website to display updated demographic information, a small area analysis of poverty, data maps of key statistics, and various tools to access and compare census data. The local authority can use these products to provide more effective policies and services for residents of Merton.
Population Update
Throughout the local authority, key demographic data from the census is used in a variety of ways. To facilitate access to these statistics, the data was divided into five categories: population and residents, economics and education, housing, travel, and health.
Updated tables and histograms were created using the recently compiled census data.
Poverty Analysis
Since poverty can arise from multiple causes, many analyses of deprivation use a
single numerical index that combines different measures of these various causes. Previous
analyses of deprivation have only made comparisons at the ward level. The Indices of
Deprivation 2002 and the London Index of Deprivation both rely on data that is usually not
available at sub-ward levels. To provide a more detailed analysis, this project has created an
index using data that is collected at the output area level. An output area is a census grouping
consisting of approximately 100 households. An output area analysis enables one to identify
pockets of poverty that would not be evident at the ward level.
vi Using this index, each output area in London was ranked according to its combined level of deprivation in five key areas: income, employment, housing, health, and education.
The degree, extent, and intensity of poverty were calculated using these rankings. These three additional measures assess the geographical distribution of poverty. In addition, the ten most deprived output areas in Merton were identified, using both the combined index and individual indicators. As a result of this new approach, small pockets of deprivation hidden at the ward level by surrounding larger areas of relative wealth were identified.
Mapping Census Variables
To analyse the spatial dimensions of census data, Mapinfo, a Geographical
Information System (GIS) was used to create data maps at the ward and output area levels.
These maps permit visual comparisons and offer an intuitive, geographic approach to
examining census variables. The maps were colours in schemes consistent with the five
categories of census data. The Natural Break scaling algorithm was used to maximize the
accuracy and to permit direct comparisons between maps at different levels of geographic
resolution.
Data Access System
In addition to containing the Population Update, Poverty Profile, and GIS maps, the
data access provides a method of comparing all census data at the ward and output area level.
Because there are over twenty-three distinct census variables and over six hundred output
areas, users could conceive of nearly endless variety of histograms. Hence, the Census Data
section allows the user to choose any number of wards and corresponding datasets before it
dynamically generates the required data display.
vii Another feature provided by the data access system is the ability to associate output area codes to postcodes. After using GIS maps and census data to identify areas of interest, the postcode lookup system can locate these neighbourhoods on a street map. This is
achieved by creating image maps, which are clickable GIS maps, and by providing links to postal codes in the Census Data section. This feature connects output area census data to
physical locations, allowing officials from various departments to focus on areas of specific
importance.
This project has produced four key products: Population Update, Poverty Profile,
Data Access System, and an atlas of GIS maps. The Population and Poverty Profiles provide
detailed demographic and deprivation analyses, while the Data Access System and GIS maps
can be used as tools to perform future analyses. This has enabled the local authority to
efficiently access and analyse census data and poverty.
viii Chapter 1: Introduction
For the past two years, the London Borough of Merton has received a rating of
"weak" on the Comprehensive Performance Assessment, administered by the Audit
Commission. To address this concern, the local authority has developed the Journey to
Excellence, a three-year plan to improve Merton's rating. Important aspects of the plan include improvements in education and the environment, as well as an increased understanding of the causes and distribution of deprivation in the borough.
Achieving these objectives requires a wide range of demographic information. When structuring and evaluating plans and decisions, changes in population composition must always be taken into account. Thus, accurate, up to date data is an important factor in forming long-term policies. The largest and most reliable source of demographic data continues to be the United Kingdom Population Census, conducted decennially by the Office of National
Statistics. Although the questions are limited in scope, the breadth and depth of the data
found on the census are unparalleled. Having recently received 2001 census results, Merton
was faced with the challenge of incorporating this information into the decision-making
processes of the various departments that make up the local authority.
Previous deprivation analyses, including the Indices of Deprivation and the London
Index of Deprivation, have only compared boroughs at the ward level. This ignores variations
in poverty between output areas, making it impossible to focus on improving the quality of
life in individual neighbourhoods. In addition, the geographical distribution of deprivation at
the output area level is unknown, overlooking areas of poverty that extend across ward
boundaries.
Furthermore, Merton lacks an effective way to access and present census data at the
ward and output area levels, making comparisons difficult. Additionally, the spatial
1 dimensions of the census data have not been thoroughly examined. These limitations prevent
Merton from fully taking advantage of the wealth of information provided in the census.
The results of this project provide ways for which Merton can improve its use of census data at a finer scale than the ward level. A small area analysis of poverty highlights differences between ward and output area deprivation levels (This can be found in Chapter
5). This analysis will aid Merton in convincing the Greater London Authority to look below the ward level when assessing the amount of poverty in each borough. By allocating resources based on the distribution of poverty at the output area level, small areas of deprivation will receive the required aid that they would otherwise not obtain due to being located in an affluent ward. In addition, Merton's local authority can use the small area analysis to focus aid and services to neighbourhoods most in need. Finally, with assistance from members of the local authority, a data access system was created to present the census information, data maps, and tools required to perform detailed analyses of Merton (these can be found in chapters 4.4, 4.2, 4.4.1 and 4.4.2, respectively).
2 Chapter 2: Background Information
On April 29, 2001, the United Kingdom conducted its national census. The information gathered from that census will be of high importance for city planning and urban development. The first step in taking advantage of these new data is to understand the purpose of the census, the questions asked of the people and the uses of census data. One aspect to take into consideration is what cities that have similar qualities to the borough of
Merton, based on their census results, have done in the past. By reviewing the current use of census data and city plans in the borough of Merton, and by comparing them with case studies and development theories, new plans for government policy and public services can be originated.
2.1 Sponsoring Agency
The Borough of Merton sits among the southwest boroughs of Outer London. Merton is composed of twenty wards and covers 14.5 square miles. The total population of Merton is approximately 190,000 residents. Our sponsor is the local authority of Merton, responsible for governing the surrounding area. The Merton Council is made up of sixty councillors; three from each ward. Our liaison is Mr. Ernest Obumselu, the research and community engagement officer for the Chief Executive's Department in Merton, London. His responsibilities include organizing wide consultations, including residents' panels, complaints
from the citizens and also provide consultation methods to the Chief Executive's Department.
A large part of Mr. Obumselu's job is to research the population and local government. Often
he receives requests for data and statistics about the London Borough of Merton.
3 2.2 Census
The census is an official count of all individuals in the population. It is a snap-shot in time of the people, their household situations, and the economy. Censuses have been recorded for thousands of years, but the questions asked on them have changed. However, one thing has not changed, and that is how valuable the results are to the community.
2.2.1 History
Early censuses, according to Lavin (1996), "usually counted all males at military age, heads of households and landholders, or similar selective groups; women and children were seldom counted."(p.5). In ancient Babylon, China, and Egypt, censuses were carried out to determine land value for taxation purposes (Eckler, 1996, p.4). Around 1500 B.C., a census was taken by the Hebrews in order to determine their military strength. The census is even mentioned in the Bible, in the Gospel of Luke. Jesus was born in Bethlehem because Caesar
Augustus ordered a census, and Joseph and Mary had to return to Bethlehem to be counted in
Joseph's home (Lavin, 1996, p.5). Also in 1086 in England, William I ordered a census after the Doomsday Inquest to assess the wealth of his newly conquered lands. The reasons why community leaders conduct a census have changed over time and so have the questions that are asked.
The first population census in the United Kingdom was done in 1801 (National
Statistics, October 2002, Year data first available). Since then, the census has been conducted
every 10 years. The most recent census was completed in April 2001. The questions asked on
the census were "age, sex, marital status, ethnic group, country of birth, economic activity,
travel to work, migration, religion, general health, limiting long-term illness, household size,
tenure and amenities" (National Statistics, October 2002, General Information). Among
those, one question, religion, was asked for the first time and was not required to be
4 answered. The information gained in the census of 2001 in the United Kingdom is very valuable to the community. The new information will be used to plan for providing public services and to better serve the people.
2.2.2 Population and Residents
A census is taken to learn more about a population. The demographic questions asked on the United Kingdom census include the resident's age, gender, marital status, relationship to the head of the household, and information about their race and religion.
Age and gender are part of a person's identity. A person's date of birth and gender are both characteristics which will not change. Age of the residents is important to know when planning future demand on health care systems, need for future schools and need for other age-based programs (Lavin, 1996, p.204). Age data are also used for planning by the military service (Trewartha, 1969, p.117)
Gender, specifically the male to female ratio, has an effect on the future of the population. Gender ratio affects the number of marriages and births in the population
(Trewartha, 1969, p.114). Wars can have an effect on this ratio because mainly men enlist to serve in the army. If many men die at war, there will be an increased number of spinsters and widows.
The economy can have an effect on the gender ratio as well, especially in areas such
as industry, mining and lumber that can attract young, unmarried men (Trewartha, 1969,
p.115). Gender can also help when doing analysis of the average income and occupation
types for men and women (Lavin, 1996, p.203). This information is used for economic
planning because knowing who is making money and what jobs they are taking can predict
future trends in employment and in spending.
5 The head of the household, relationship to the head of the household and marital status are asked on the census in order to learn about the family's living arrangements. Every individual, other than the head of the household, needs to be listed with their relationship to the head. This is to learn about families, how many people are living together, how many generations are under one roof, and to be able to study the different make up of families and households for social and economic reasons (Lavin, 1996, p.203).
Marital status is asked on the British census to indicate a change in living arrangements. The information gathered is also used to calculate age at first marriage, divorce rates and expected birth rates. The choices are married, widowed, divorced, separated or never married (Lavin, 1996, p.204). Age at first marriage differs over time and with respect to location (Trewartha, 1969, p.131). In less developed areas of the world people tend to marry earlier, while some postpone marriage in favour of education. The age at which couples marry affects their reproductive period, which in turn will affect how many children they can have and the future size of the population (Trewartha, 1969, p.131).
Often times, many members of a particular ethnic group may choose to reside in the same area. They may share the same country of birth or may just share the same language. It is important to know what ethnic groups reside in the community in order to develop city plans and policies that will benefit all the residents. The census data help in monitoring any economic and social differences between ethnic groups (Lavin, 1996, p.206). If members of an ethnic group are living in the same area and if they speak a language other then English, the borough can be made aware of the situation from the census. They may use the information to provide translators to help inform the people about the local government and elections (Lavin, 1996, p.208). This will be discussed more in the following sections.
6 2.2.3 Housing
There are questions on a census about the individual's household and the accommodations. It is valuable to know how many people own their home, how many rent and how many live in local authority housing for land use analysis and for zoning and transportation planning (Lavin, 1996, p.219). The size of the home and the number of rooms may be asked on a census to determine overcrowding rate and also to estimate property value in a neighbourhood. Also, information on accommodations such as plumbing for water and heating equipment are asked to evaluate the condition of the building for safety reasons. In studying poverty in Merton, we will want to focus on this information to help in locating troubled areas in the borough.
2.2.4 Economics and Education
Employment status is asked of all individuals of working age. The employment status and work experience are asked to determine the unemployment rate and to plan for job training programs (Lavin, 1996, p.211). Income, which is not asked on the census, is collected yearly by the Department for Workers and Pension. This information is important for the local authority because if households are struggling due to a bad job market or because they lack the skills to get a better job, aid needs to be available. The information from the census can be used to determine which households are near the poverty level (Lavin,
1996, p.215). If the average income drops, programs should be in place to provide monetary aid, public housing and medical care (Lavin, 1996, p.144). The community also needs to plan for how many people are expected to need help finding jobs and training.
Reasons for the education questions on the census are to find out what level of education the individuals in the household have achieved. In addition it can be used to identify areas in the city which have a larger number of high school dropouts. The dropout
7 rate is valuable information because it tells the city where to focus aid to help students stay in school and get their diplomas. Merton uses this data this data to determine where the most students are not completing school. From there, the borough can attempt to correct the problem by helping dropouts get back into school and thus increase the dropout's chances of getting better jobs and making more money.
2.2.5 Transportation
One of the most important reasons people use transportation for is their daily commute to work. Many individuals choose to drive themselves to work while others decide to take public transportation. In some cases, people use public transportation because they may not own a car. This may be the case if they can not afford a car or they are disabled and therefore unfit to drive. In other cases, people may choose not to own a car because the public transportation routes are convenient for their needs. The census information can be used for planning public transportation and for road maintenance, as discussed later.
2.2.6 Health
Services are in place in the United Kingdom to provide universal health care. Because of this, families will never run out of funds to support themselves and the ill person. The
National Health Service (NHS) provides many services to the public including free counselling, dental care, check ups and prescriptions (Citizens Advice, 2002, Health
Benefits). Health and disability questions are asked on the census in the United Kingdom for planning into the NHS and other health care systems. Policies need to be in place for people who have physical or mental disabilities (Lavin, 1996, p.209). Assistance programs to help families that have a member in poor health can be planned using the results from the census.
8 2.3 Uses of Census Data
Census data have a wide range of use in both the public and private sectors. The government is the only organization with the available resources and legal authority necessary to conduct these massive surveys (Lavin, 1996, p.28). As a result, the information of the census is unmatched in both breadth and depth. Although it is impossible to list all of the ways in which census data can and have been used, there are several aspects of the census that are particularly important. Demographic data pertaining to population and housing have especially broad use.
2.3.1 Community Planning
Changes in population and housing data are important tools used by governments for projecting future trends in communities in order to improve planning (Eckler, 1972, pp. 52-
53). Data pertaining to migration, population growth and density, and the distribution and affordability of housing units play key roles in the decision making process (Lavin, 1996, p.28). The more information the government has on its population, the better it can serve the needs of its citizens. School districts must estimate the size of future student bodies so that they can assess the need for expanding their facilities, obtaining additional funds, and providing new services. Overcrowding or under use of schools can be prevented by careful planning and attention to trends in fertility and migration; these principles can be extended to the development of an entire nation.
2.3.2 Allocation of Services
Census data also reflect the present demand for services, and can be used to predict the future demand (Eckler, 1972, p.53). This allows governments to better serve the needs of their citizens, and to focus resources where they are most needed. For example, demographic
9 data can help planners identify areas with high concentrations of poverty and the unemployed
(Lavin, 1996, p.28). Programs such as subsidized rent, health clinics, and adult education can then be implemented more effectively (Eckler, 1972, p.53). Bilingual education can be brought to areas with high concentrations of people who speak a foreign language. By focusing these programs where they are most needed, resources will not be wasted, and more people can be helped (Lavin, 1996, p.28).
Census results can also be used in planning for both private and public transportation routes (Lavin, 1996, p.40). Expected traffic volume is an important factor when planning how often a road will need repaving and what the posted speed limit should be. Traffic volume is not determined only by private citizens; local businesses use the roads for product movement and package delivery (Lavin, 1996, p.40). If there is a rise in activity in a particular business district, city planners may want to consider improving traffic flow and increasing the number of parking spaces.
Not everyone chooses or can afford to own a vehicle. A section of the population may require public transportation. The elderly and sick may require transportation to hospitals or doctors' offices, and students often ride busses to school. By providing information on the number of people that are likely to require public transportation, census data can be used to plan locations for bus stops, as well as the number of buses that should run on each route
(Lavin, 1996, p.40). In the case of Merton, not only are buses available, but there is a subway system in place as well. By estimating the number of people expected to use public transportation each day, Merton can make more informed decisions regarding these routes.
Also, if low-income families use these routes as their primary form of transportation, reduced rate fares may be needed.
10 2.4 Defining Poverty
2.4.1 General Measures
Before any real work can be done on analysing poverty in Merton, an important question must be answered: what defines poverty? The Bureau of the Census in the U.S.
Department of Commerce defines it in terms of cash income only (Frumkin, 2000, p. 244).
There is an income threshold that marks "subsistence living conditions," and any household that falls below this threshold is considered to be in poverty (Frumkin, 2000, p. 244). While the threshold takes into account members of the household, their ages, and number of children, it does not account for geographic differences.
Income is an important factor in defining poverty, and it can be readily computed from census data. However, it does not tell the whole story. In Squire's work (1993) on methods for fighting poverty, she discusses a different definition. Poverty is the "inability to attain a minimal standard of living, interpreted to include not only consumption of food, clothing, and shelter, but also access to education, health services, clean water and so on" (p.
377). The amount of money one earns does not necessarily affect the well being of that person. To only look at income is to overlook many factors that also contribute to well being.
"Some countries that have performed impressively in terms of growth in GDP and increases in the incomes of the poor are unable to point to comparable progress in other dimensions of well being" (p. 379). On the other hand, countries like Sri Lanka have low incomes and growth rates but are very impressive in terms of literacy and life expectancy (p. 379).
The Physical Quality of Life Index (PQLI) was developed by Dr. Morris D. Morris in an effort to create a composite index that was simple, not centred on the values of a particular
society, and relatively easy to apply for international comparisons (Morris, 1979, p. 21). Of the many possible indicators for the composite index, three were chosen: infant mortality, life
11 expectancy, and literacy. These three indicators show not the hopes or good intentions of programs design to improve life, but the results of such programs. And by not focusing on economic measures, the PQLI avoids the situation with countries like Sri Lanka, as mentioned above. The three indicators are transformed to the standard normal distribution
(the bell curve) and then averaged together to produce a single index score. The PQLI does not measure total welfare (p. 91). It is, however, a good starting point for analysing quality of life, and consequently, aspects of poverty.
2.4.2 Indicators in London
Several, more complex measures of quality of life have been developed for London.
The Office of the Deputy Prime Minister originally created the Index of Local Conditions
(ILC), which was changed to the Index of Local Deprivation (ILD), and which has now changed to the Indices of Multiple Deprivation (IMD). Each new form has been the result of further research into what causes poverty. In addition to this work, the Greater London
Authority (GLA) created its own measurement, the London Index of Deprivation (LID). The
LID uses twenty-three indicators with three extra indicators at the borough level. The LID was created in response to criticism of the IMD; it is alleged to be overly complicated, to the point where questions have been raised as to whether or not its complex system of weightings and standardization skew results in favour of certain indicators, while virtually nullifying the contributions of others (The London Index of Deprivation Consultation Document, p. 7).
Because it takes into account so many indicators, some of the less important variables have so little weight as to be rendered almost meaningless.
From the publication Monitoring poverty and social exclusion 2001, produced by the
Joseph Rowntree Foundation, there are fifty indicators of poverty in eight broad categories.
The categories are education, housing, social cohesion, crime, services, health, income, and employment. Eleven of the indicators are found from census data. In the education category, the level of education is an indicator. Indicators found under housing are workless households, households with half of the average income, and overcrowding. No data on social cohesion, crime, or services is included in the census data. Health indicators include illness and disability information, and households without proper amenities such as central heating. Income indicators are low income, low-income intensity, location of low income, and the gap between low income and the median income. Finally, unemployment rate is an indicator in the employment category.
2.4.3 Multiple Indices of Deprivation
The LID is an attempt to create a formula that is both simple and transparent (The
London Index of Deprivation Consultation Document, p. 5). Although it recognizes over twenty indicators of poverty, the LID chooses only one indicator to represent each of the seven aspects of poverty it has devised (p. 12). These "headline indicators" were chosen based on the strong correlation they exhibited with the other indicators in their respective category (p. 9). The indicators are then standardized to approximate the standard normal distribution, and then added together, using equal weighting.
Most of these indices are based increasingly upon data collected outside of the national census, and consequently the data used is not readily available. Therefore, an index of deprivation based solely on census data may be more effective. Another advantage of the census is that its data is broken up by output area (OA), while most other data can only be found at the ward level. An output area consists of four to seven postcodes, and is approximately twenty to thirty times as small as a ward.
1_3 2.4.4 Distribution of Poverty
In addition to ranking the boroughs and wards of London by a combined score, multiple indices of deprivation can also be used to measure the degree, extent, and intensity of poverty experienced in a borough or ward (The London Index of Deprivation Consultation
Document, p. 30). The degree of poverty for a particular geographic level is simply the average of the scores for each of its sublevels. For example, the degree of poverty for a borough is the average score of its respective wards. The extent of poverty attempts to gauge how widespread poverty is in a particular area. At the borough level, the extent of poverty is the proportion of wards within the borough ranking among the top 10% of most deprived wards in London. Finally, the intensity of poverty experienced in a borough is the average score of its three most deprived wards. If output area level data is available, the degree, extent, and intensity of poverty experienced by a particular ward can also be measured. The method of computation is similar; OA level data is used to calculate scores for each respective ward.
2.5 Poverty Mapping in Ecuador
Hentschel, Lanjouw, Lanjouw, and Poggi (1998) built a poverty map of Ecuador for
World Bank using census data and simple household surveys. Poverty maps, which detail both numerical data about poverty and show its geographical distribution, are a great boon to public policy makers. Such maps "provide the means to investigate the relationship between growth and distribution inside a country" (p. 1). Hentschel, Lanjouw, Lanjouw, and Poggi's work discusses techniques of using census data and surveys to approximate a poverty map, and explains shortcomings and possibilities for further work.
14 While poverty maps serve as an important research tool, developing them is difficult due to a lack of either depth or breadth of data (Hentschel, Lanjouw, Lanjouw, and Poggi,
1998, p. 2). Surveys measuring standards of living contain the detail necessary to construct poverty indicators, but the sample size of the survey is not large enough to construct a poverty map. On the other hand, census data span the entire region with a large sample size but usually lack the specifics on household resources.
Hentschel, Lanjouw, Lanjouw, and Poggi (1998) go on to compare the estimates of poverty based on a basic needs (BN) indicator. The basic needs indicator was constructed using five weighted variables that were then summed to form an index value. The variables were "access to water, access to waste disposal services, education, and number of people per bedroom" (p. 6). Another possible method to indicate poverty is consumption, the measure of the expenditure of household resources. Consumption is not a perfect indicator in this regard, as it is somewhat controversial. Still, Hentschel, Lanjouw, Lanjouw, and Poggi (1998) say that "a comprehensive measure of consumption comes reasonably close to the goal of capturing a household's achievement of well-being" (p. 5) The consumption data come from a household survey, the Ecuador Encuesta Sobre Los Condiciones de Vida (EVC) for 1994.
To compare the two methods, the total fraction of the population identified as poor is held constant while the definition of poverty switches from the BN indicator to the consumption survey results (p. 8). From the analysis of the data, rural areas seem to be poorer
than urban areas when the BN indicator is used instead of the consumption survey (p. 9).
Hentschel, Lanjouw, Lanjouw, and Poggi go on to show that over 50% of resources would go
to households that did not need them if such allocations were based on the BN indicator.
In order to avoid the subjectivity of the basic needs indicator, Hentschel et al. (1998)
looked into using census data and consumption levels to use as a basis for a poverty map.
Using survey data, models of consumption were developed that correspond to variables in the
15 census data (p. 13). To test if these models were accurate, Hentschel et al. put them through a check similar to the BN indicator versus the consumption survey. While the coefficients of determination remained "significantly lower than one," the targeting efficiency of allocating resources improved by "almost 50%" (p. 14). The models were then applied to the census data and the household's probability of being poor was estimated. The mean incidence of poverty was calculated over all households in a given region (p. 15). After analysing the new poverty data Hentschel et al. determined that while "the poverty rates calculated on the basis of consumption imputed from the census data are quite close to those based on the surveys"
(p. 19), the standard errors o\n the estimations are "clearly large enough to caution strongly against attempts to identify poor households...directly from the census data" (p. 21-22).
In their conclusion, Hentschel et al. (1998) consider applications of their work on poverty maps. While using census data can produce some rough estimates, the error is not
insignificant, making such maps a rough analysis at best. However, the mapping can be
compared to "other indicators of well-being, opportunity, and access" (p. 28). Using a
Geographical Information System (GIS), other information such as healthcare, education, or
sanitation could be overlaid onto a poverty map to look for possible indicators or causal
relationships of poverty.
2.6 Geographical Information Systems
A Geographical Information Systems (GIS) is a computer based software package
that combines information from several sources, stores these data, and produces geographical
maps. This is achieved , according to Easa and Chan (2000), "using the unique tools and
methods provided in GIS software packages for capturing, organizing, processing, and
analyzing spatially referenced data" (p.9). Another feature of GIS is that it provides a
framework for various types, sizes and quantities of data (Scholten and Stillwell, 1990, p.3).
16 2.6.1 Objectives
There are three major objectives that any properly designed GIS will implement
(Martin, 1996, pp. 58-59). The first objective is to provide a method for storage and retrieval of various types of data. The next step is to supply a means to manipulate the data and perform analyses. The final objective is to offer the ability to report information and create geographical maps. These three tasks provide the core functionality of current GIS systems, including the two popular software packages MapInfo© and ArcView©.
GIS, according to Scholten and Stillwell (1990), must provide a means for "the storage, management and integration of large amounts of spatial referenced data" (p.9). Since data can be obtained from several sources, one of the most important features of GIS is the ability to combine these data into a single database (p.4). Easa and Chan (2000, p.19) suggest these data are to be stored in a Database Management System (DBMS), which provides two important features: accessibility and validity. A DBMS can provide tools to ensure that the data being analysed are accurate, including topographical errors that could result in inaccurate analyses (p.19).
17 Figure 1: Sample map layer of the Merton's wards and output areas.
The most important aspect of GIS is the ability to perform analyses. The difference from traditional Computer-aided Cartography (CAC) programs and GIS is the ability to carry out mathematical analyses (Martin, 1996, p.57). GIS will provide a collection of utilities to perform these statistical calculations. These tools make GIS a powerful system for research and planning.
GIS can perform three levels of analyses with increasing degrees of complexity
(Scholten and Stillwell, 1999, p.4). A basic analysis includes several variables with an overlay analysis. Although this is a very powerful strategy, it is not very difficult for GIS to achieve. A more complex strategy is to use statistical analyses to determine a geographical relationship between data points. "The most sophisticated analysis occurs when modelling is introduced" (p.4). With modelling techniques, it is possible to predict future results of certain events. For example, one can model effects of biological weapons with respect to certain areas located on a GIS map.
18 The final objective for GIS is to output the data, usually onto detailed data maps. It is at this stage that the data are exported into a form that is understandable by either a computer or human (Martin, 1996, p.58). The techniques "involved here include many of those of conventional cartography, which seek to maximize the amount of information communicated from the map maker to the map reader" (p.58). Unlike regular geographical maps which are created with all data on a single layer, these maps are produced with several layers. This allows for easy removal of data layers and the addition of others.
2.6.2 Operations
There are four essential operations that a GIS is able to perform. As stated by Martin
(1996), "These 'classes of analytical operations' are divided into reclassification, overlay, distance/connectivity measurement and neighbourhood characteristics" (p.59). These operations apply to the GIS objective of data manipulation and analyses.
Martin (1996) says that, "[reclassification] may be thought of as a simple
`recolouring' of features in the map" (p.59). For example, if you had a map of population density, you may reclassify the information into different categories, such as high and low populations. One feature of reclassification is that is does not rely on any other data besides what it included in the original map.
According to Easa and Chan (2000) "GIS excels at answering analytical questions about complex relationships between [multiple layers]" (p.20). Instead of creating complex maps with a lot of information, a typical analysis using GIS will involve creating each layer individually. Then, these layers can be placed on top of each other, forming patterns and trends (p.20). When layers are combined, boundaries may need to be adjusted based on the level of detail of the data that was collected (Martin, 1996, p.59). Redefining boundaries can modify the meaning of the original layers, which can cause complications in analyses.
19 When analysing certain variables, such as transportation, it is useful to determine the average distance between several points (Martin, 1996, pp.59, 60). GIS provides operations to perform calculations that can accomplish this task. The complexity of the operation may vary from measuring the distance between two data points to creating zones to determine transportation fares.
Another operation that can be performed by GIS is defining the boundaries of a neighbourhood. Statistical tools to find mathematical calculations such as mean and variance are used to determine areas that are similar. Then, GIS can apply filters to smooth out the
edges of the boundaries to create clear areas with similar characteristics (Martin, 1996, pp.59,
60).
2.6.3 Scaling Maps
The natural break scaling method (Caspall and Jenks, 1971) is a built in function of
Mapinfo. The software uses an iterative process to determine the ranges of data that produce
the most accurate representations of the data. A map that was one hundred percent accurate
would match a unique colour to each data point value. This becomes impractical with most
datasets. In order to simplify the map, a small number of colours are chosen to represent
ranges of the data. Each colour can be thought of representing the mean value of all the data
points inside the range assigned to that colour. The error of these ranges is determined by
summing up all the absolute values of the differences between the data points and the
averages that represent the actual data. The natural break algorithm computes ranges that
minimise the total error.
To compute the percent error of any set of ranges, the maximum possible error must be
determined. The maximum error for any set of data is the error when there is only one range
of values in the scale. In other words, the map is composed of only one colour. First calculate
20 the mean of all the data values. Then sum the absolute values of the differences between each data value and the mean. This is the maximum amount of error possible for these values.
Once this is done, percent error for any arbitrary set of ranges can be found. Divide the error of the ranges by the maximum error to obtain the percent error. Conversely, the accuracy percentage can be calculated by subtracting the error percentage from one.
21 Chapter 3: Methodology
3.1 Presentation of Census Data
Census data are collected for a wide range of variables and are stored at several levels: borough, ward and output area. While the size of an output area varies, the average is
100 households. Merton has six hundred and thirty-one output areas and over twenty-five census datasets therefore it is important to determine the most effective method to display this information.
3.1.1 Data Access System
Developing an effective solution to access to a variety of data and analyses provides the local authority with a tool for future planning and policy designing. Interviewing two members interested these fields assisted in identifying which information is useful. Ernest
Obumselu and Steve Cardis were interviewed with the following protocol:
• How do you plan on using the website? • What data would be useful for you to have access to on the Intranet? • Would OA level data be useful to have in tabular and graphic format? • What type of questions do you get asked? • What types of questions would you like for the website to answer?
Displaying census data is a challenging task since there are several different methods and technologies available. Researching other website developed by boroughs in London provided insight into useful techniques. Below is a list of boroughs researched:
• Brent — www.brent.gov.uk • Camden — www.camden.gov.uk • Croydon — www.croydon.gov.uk • Lewisham — www.lewisham.gov.uk • Merton — www.merton.gov.uk • Wandsworth — www.wandsworth.gov.uk
22 A list was produced detailing how each borough approached the complicated topic.
There are a lot of technologies that can be utilised to display dynamic information via a website. Len Buckley, a member of the web development team, was interviewed to determine the best approach. A protocol, consisting of six questions, was developed:
• What database software can we access? • What programming languages are supported? • Are there any templates for the general layout? • Are there any programming style guidelines or rules? • Are there standard Cascading Style Sheets that we must use? • Are there any restrictions or limitations with file sizes?
A short electronic questionnaire was developed to help improve the website. The questionnaire was mailed to twenty people and responses were received via email. This method of a survey allowed for quick results and faster development.
Thank you for taking the time to improve this website.
Name* ,Vas the information useful? 0 Yes Why? Was the website presented clearly? Yes No
Why? Was the website easy to navigate? Yes No
Why? Suggestions:
submit Clear
*This may be left blank for confidentiality Figure 2: Internet Survey
23 3.1.2 What makes an effective GIS map layer?
In addition to standard statistical techniques, the GIS software MapInfo has been used to produce data maps that illustrate census variables spatially. Instead of relying on pie charts, histograms, or simple columns of numbers that merely show the magnitude of the variable, the use of GIS allows one to view not only the value of the data point, but the location as well. But GIS is just a tool, not an end in itself How GIS maps can be used to make geographic comparisons for Merton must be determined.
The effectiveness of a GIS map layer can be divided into two categories; the importance of the variable that has been mapped and the style of visual presentation. Since there is so much information contained in the census, knowing where to start and what to
focus on is of primary concern. A short, informal survey will be taken to provide detailed in
this area. Meeting with the key users of the maps and discussing what is helpful to them will
provide information on the types of visual styles to use.
In order to create useful GIS maps for the Borough of Merton, the variables contained
in the census data that indicate deprivation must be determined. During meetings with Susie
Skipper from the Crime Department and Michael Sutherland from the Education Department,
the goal and objectives for the project were presented. They were asked what data from their
department would be useful in looking at deprivation. They were also asked for data that is
not contained in the census that their department has access to. They were able to provide this
information at the ward level.
A survey on the relative importance of different census variables was given at a
meeting with Joanna Switalska from the Neighbourhood Renewal Department, Ernest
Obumselu from the Chief Executive's Department and Steve Cardis from the Environmental
Department. The survey asked the taker to rank twenty-four census variables as very
important, important, not very important, or not needed for their department. They were also asked to list any other variables that they felt were important in understanding the spatial dimensions of deprivation. Extra surveys were delivered to members of the Neighbourhood
Renewal Department and to members of the Primary Care Trust, other interested parties. The census variables were then compiled into a list, with the most important variables at the top
(see table 1 for results). The variables were then mapped in order of importance. These responses were the primary source of information for determining the first variables to be mapped.
Please rank the following variables as very important (1), important (2), not very important (3), and not needed (4) based on how useful it would be for these variables to be displayed on a map of Merton.
Variable Rank Population Density People Living in Communal Establishments Students Away from Home Population Age Breakdown Living Arrangements (Married, Cohabitating) Country of Birth Ethnic Group Breakdown Religious Breakdown Long Term Limiting Illness Health Levels Unemployment Rate Long Term Unemployed Persons Industry of Employment Occupation Groups Education Qualifications Socio Economic Classification Travel to/from Work Household Type Vehicles per Household Household Tenure Household Characteristics Household Composition Migration Statistics
Please list any other useful or important variables here:
Figure 3: Indicator Evaluation Survey
25 Also at this meeting, several design choices for the GIS layers were presented to the attendees. All maps shown were based on the ward and OA level unemployment data. First, the attendees were asked to express their opinions on the clarity of the text labels on the maps. Two were presented, both of the unemployment rates at the OA level. The first contained ward labels that were bolded and had a white halo surrounding the letters. The second map contained labels that consisted of regular text surrounded by a white box. The attendees were asked which label style they felt was more clear and easy to read.
Next, the attendees were presented with four maps of the unemployment rate at both the ward and OA level. For each of these, the scales between the ward and OA level were the same, but the overall colour schemes were varied. The colour schemes displayed were green to red, white to red, blue to red, and blue to yellow. These were picked as being representative of the major styles of GIS map layers. After examining the maps, the attendees were asked to rank them based on the following criteria:
• Are the colours easily distinguishable?
• Do the colours communicate the nature of the information?
• Which colour scheme do you personally like?
The responses were recorded for later analysis.
Finally, based on what colour schemed seemed to be the highest ranked, the attendees were
then shown two additional maps in that colour scheme. Again, unemployment rates by ward
and by OA were displayed, but in the first map the scales were the same, while in the second
the scales were different. The attendees were then asked to choose which of the two scales a)
was easily comparable, and b) provided the level of detail that most preferred. The results
were recorded, and all of the information gained from this meeting was compiled into a set of
guidelines for generating GIS layers. Colours: For the coloured map sets 1 a, 2a, 3a, and 4a, please rank them based on the following criteria:
Clarity: (Can you distinguish values easily?)
Communication: (Is the map easy to understand?)
Visual Appeal: (Do you like the look of the map?)
Scale: For the scaled map sets 'a' and 'll', please indicate which value scale you feel is better based on the following criteria:
Comparability: (How easy is it for you to compare the two maps?)
Level of Detail: (Do you prefer detail on the ward level as well as the OA level, or just the OA level?)
Other: Of all the map styles you have seen, which do you feel would be most useful to your department?
Which style do you prefer?
Any other questions or comments?
Figure 4: Map Evaluation Survey
While some of the questions asked are very subjective ones, the goal of this meeting
was not to obtain results that could be generalized, but to determine what styles would be the
most effective in communicating to the users of the GIS maps inside the local authority of
Merton.
27 3.1.3 Merton's Population Update
The analysis of the 2001 population census was done after asking the employees in the department, after the first presentation, what they would like to see done with the census data. Mr. Ernest Obumselu suggested production a population profile, similar to one the department had produced after the 1991 census. The employees could use the profile when doing research as well as a handy source of information when it is requested by the public.
The 2001 population profile was developed using the 1991 document as a template, by using analysis techniques and was revised after review of the department to best suit their needs.
The past profile included numerous charts and some tables of the census data from each category on the census. The first step was to use the older profile as a template for the new profile. That method worked except for a few cases where new questions were added on census questionnaire in 2001 that were not present in 1991. In addition some questions were rephrased and no longer were asking the same question as they were in 1991. In order to organise the data in a useful fashion, as well as to keep an organization standard, a new categorisation standard was developed to be used on all census data.
The data in some cases required additional analysis and grouping before it could be entered into a table or chart. For this we used Excel as an analysis tool. Excel was the obvious choice since the census data is currently stored in spreadsheets, it is capable of doing data analysis and it has built in functions for producing charts. Using Excel, any calculations which were required for analysis could be easily done within the software. From there a chart could be made by using the chart wizard. All of the charts produced were gathered and
entered into the 2001 population census document along with the tables of data used to
produce the chart, such that anyone viewing the document could easily access the exact
numbers as well as view the material in the graphical form. This was an improvement from
28 the past profile because it mainly contained only the graphical data and left out the tabular data.
Finally, once the document was completed, in order to find out if employees like the profile and to find out if there was anything else they would like to see done, the profile was passed on to Mr. Ernest Obumselu to share with his colleagues. The profile was given to him in both digital format and in hard copy to share. In addition it was sent to the Greater London
Authority (GLA) for verification. Using the advice from him and his colleagues, the profile was then edited to include any of the new material requested. In addition, corrections were made to make charts easier to read and grammatical errors were fixed.
3.2 Analysis of Poverty
3.2.1 Indicators of Poverty
Before deprivation in Merton could be analysed, indicators of poverty had to be
chosen. An archival analysis of the available literature was conducted in order to discover
what indicators are used in London to measure poverty. These resources included Monitoring
Poverty and Social Exclusion 2001, which lists over 50 indicators of poverty used in the UK;
London Divided, a study of poverty in the capital; and Poverty Profiles created by the
boroughs of Barking & Dagenham and Croydon.
Following this research, two techniques employing methods of multiple deprivation
analysis were examined. The Indices of Deprivation 2002 Consultation Stages 1 and 2, as
well as the London Index of Deprivation Consultation, were each studied, both in order to
asses methods of combining indicators of poverty into a single variable, and as sources of
additional indicators.
This archival research was supplemented by holding meetings with key officials from
the local authority of Merton. A meeting was held with Susie Skipper, a crime data analyst
29 for the local authority, in part to discuss crime related indicators of poverty. Correlations between poverty and education, among other topics, were discussed with Michael Sutherland, from the Education Department. During these meetings, requests for specific data used in the
LID and ID2002 were made. In a meeting with Joanna Switalska, indicators of poverty and multiple deprivation analyses were discussed.
Besides providing a list of the various indicators of poverty used in London, these meetings and archival studies were important resources used in selecting which indicators to use for Merton. At the three meetings referred to above, the relevance of particular indicators was discussed. The choices of indicators in the Barking & Dagenham and Croydon Poverty
Profiles, as well as London Divided, were each examined.
In pursuing specific data, archival methods have yielded sources for much of the information in question. Emails were sent to many of these sources, usually government departments or private organizations. A request was made to the Department for Workers and
Pensions for data pertaining to Income Benefits and Incapacity Benefits. An email was sent to the Home Office requesting crime data. As a result of a meeting with Eileen Howes, a member of the Data Management and Analysis Group at the Greater London Authority, income data for Merton at the postcode level was successfully obtained.
3.2.2 Comparison Levels
Once the information to map and the way in which to best represent that information were decided upon, the GIS map layers were created and the data was then compared. Three
different resolutions of the data were examined. The first was at the ward level, looking at
Merton's twenty wards to see a broad distribution. The second was the output area, a very
detailed look at Merton and the wards themselves. The third was at the borough level,
30 comparing Merton as a whole with the other boroughs. In addition to comparing the regions inside each level of detail to each other, the levels themselves were compared to show how the resolution of data used in an analysis can affect the results.
The first maps to be made were the maps of the ward and output area level data.
Using style guidelines developed from the usability research, each variable was mapped at the ward level and then the output area level. Both levels used the same scale so comparisons could be made immediately. The comparisons themselves were nothing more than visually scanning the map to notice the differences between the larger ward regions and how the data broke down into the output areas inside each ward. In order to facilitate comparisons, the ward boundaries were displayed on top of the output area maps.
Upon mapping all census variables and the deprivation index at the ward and output area levels for Merton itself, the individual indicators and the deprivation index was mapped for other boroughs. A GIS map of London at the ward and output area levels was obtained
from the GLA. First, the five indicators and the index were mapped by ward for all of
London, with Merton's wards being outlined in order to more easily compare them to the
rest. Since Merton is most often compared to its neighbouring boroughs, a map of Southwest
London was produced and the same variables were mapped by ward and output area for just
Merton, Croydon, Lambeth, Wandsworth, Kingston upon Thames, Richmond upon Thames,
and Sutton. Again, Merton's boundaries were highlighted to provide for easier comparisons.
3.2.3 Direct Observation
The poverty profile was checked for accuracy using direct observation. The six most
deprived output areas and six least deprived output areas were selected to be observed. Using
the postcode lookup, street names and intersections were determined for the output areas
chosen for the observation. Figure 5 shows the locations of the chosen output areas against
31 the map of the deprivation index. Majority of the locations were close enough to walk to, however a few required bus transportation.
Deprivation Index By OA II 3.18 to 7.86 (38) • 1.91 to 3.18 (54) 1.03 to 1.91 (76) ▪ 0.42 to 1.03 (93) 03 0.06 to 0.42 (103) q 0 to 0.06 (267)
Lavender Fields
Pollards Hill
ricket Green
f1 Lower tvlorden
Figure 5: Planned Observation points. The red X's mark deprived areas of interest and the blue O's mark the affluent areas of interest.
Once on site, photographs were taken of the homes, businesses, schools and churches.
Street names were recorded to compare back to the maps along with landmarks including park names, Merton owned buildings and train/tram stops. After all observations were
completed, the actual observed results were compared to the expected results determined
from the poverty profile. In addition, the areas observed were compared to each other to
establish if the regions hypothesized to be in the greatest deprivation actually appear as such,
or whether other factors may be present in areas making the areas appear to be in greater need
of aid.
32 Chapter 4: Census Data Presentation
4.1 Data Access System
The data access system is a comprehensive website containing statistics and analyses from the 2001 census data and income data provided by the Greater London Authority and the Department for Work and Pensions. The main objective of this system is to provide a central location to easily obtain information. Utilising data at borough, ward, and output area levels allows for a large variety of comparisons ranging from boroughs to neighbourhoods.
4.1.1 Other Methods of Displaying Census Data
The Primary Care Trust, a department of health services, has created a website to display census data. However, this website only displays a single dataset and a single ward, which is useful in assisting residents in Merton who want to know more about their community. This website can not display output area census data, postcodes relating to these areas, multiple wards, or maps produced from the Geographical Information System. This limits the detail of comparisons between datasets.
There are three distinct ways that other boroughs in London use to present census data on the Internet. The first method, used by the London Borough of Croydon (2003), is to
create spreadsheets to navigate through census data (p.1). Another method, used by the
London Borough of Lewisham (2003), is to develop statistical documents that contain key
analyses Page (p.1). However, these documents do not allow alternate analysis since the raw
data is not shown. The final option, used by the Borough of Brent (2003), is to create tables
on a webpage with only selected information presented (p.1).
33 4.1.2 Components
The data access system has all the capabilities provided by other boroughs, as well as new, more powerful features. It contains six major components: Merton's Population Update,
Census Data, Poverty Profile, GIS maps, Postcode lookup, and a Tutorial section. These six sections produce a wide range of resources that can be utilised. Figure 5 shows the web browser screen from which they can be accessed.
Merton's Population Update compares census data at the borough and ward level. The
Poverty Profile contains a detailed analysis of deprivation and affluence, based on several indicators from the census and income data. The section on Census Data allows any number
of wards and tables to be displayed, with either ward or output area level data, and histograms
can dynamically be created. The GIS section contains maps at ward and output area level that
can be easily navigated. The postcode lookup feature has several methods to find the
postcode according to the output area code or vice versa.
34 This is a collection of several data sources ranging from maps produced by a Geographical Information System to tabular data from the 2001 census. It is the intention to provide a wide range of information that can be utilised when producing future policies. There are four main sections that you can access:
Merton's Population Census Data
Poverty Profile GIS Maps
Postcode Lookup Tutorial
Figure 6: The main webpage for Merton's Intranet Data Access System
4.1.3 Technologies
Since the servers in the Merton Civic Centre operate with Microsoft Windows, the appropriate programming language is Microsoft Active Server Pages (ASP) and the database software is Microsoft Access. This database software easily reads the census datasets that are stored in Microsoft Excel spreadsheets.
To create a standard in the presentation style, such as text font, text size, headings, table backgrounds and footnotes, Cascading Style Sheets (CSS) were utilised. CSS allows the
35 definition of style attributes to be separated from the webpages. For example, a paragraph named heading could be described in an external document and then used in any web document. This allows the web development team to easily redesign the site to conform to any new standards without any knowledge of the code within the data access system.
4.1.4 Categorisation of Census Data
Categorising census data facilitates access. From several interviews with Ernest
Obumselu, five main categories were chosen to completely describe all census data. The categories are Population and Residents, Housing, Economic and Education, Health and
Transportation. The most important aspect of these categories is that they completely describe the data contained by the title.
The category Population and Residents contains demographic information about the citizens. The tables in this section are population, population by age, living arrangements, birth location, martial status, ethnicity, and religion.
The category Economic and Education contains information pertaining to the
economic status. Tables in this category include type of employment and occupation.
Information about economically active people and the number of hours worked per week are
also in this category. The final topic in this category is the qualifications of students and their
economic status. One item that is not contained on the United Kingdom census is the average
income per household or person; therefore the data access system does not contain this
information.
The other categories contain fewer tables but are no less important. Housing contains
information about the type of households as well as information about the number of people
in each household. The health category contains a table about people with limiting long term
illness and provision of unpaid care numbers. Finally, information about how people are
36 travelling to work and the number of vehicles per household are contained in the travel category.
4.2 Geographical Information Systems Maps
To be effective, the GIS map layers must be presented in a visually appealing and accurate style and they must display valuable information. The map must use a colour scheme and a scale that accurately display the data, that permit detailed analyses, and that allow multiple resolutions of the data to be compared effectively. Prior to analysing the actual maps, the first data to be collected dealt with determining how to effectively use the GIS software.
4.2.1 Important Variables to Map
Parties interested in the poverty profile, such as the Neighbourhood Renewal
Department, were asked to rank the importance of the variables found within the census data are to their departments. The results from this survey determined the priority of GIS mapping.
In general, all census variables would be useful to map with GIS because it is hard to tell what will or will not be useful when looking at deprivation. Even when the data does not contribute to poverty, it may be strongly correlated with deprivation nonetheless.
The most important variables to map include household composition, unemployment rate, education levels, ethnic groups, and age breakdowns. The second level of importance included variables such as occupation and industry type, household characteristics, and other demographic data like religious affiliation and country of birth. Variables ranked as not
important included vehicles per household, students away from home, residents living in
communal establishments, and living arrangements.
37 The importance of the variables is based mostly on common sense as to what factors would contribute to poverty or would be useful to see correlated with poverty. For example, ethnicity would not contribute to poverty but certain ethnic groups may experience higher levels of deprivations so it was deemed important to map. Table 1 gives the overall results of the survey.
Variable Rank Education Qualifications 1 Ethnic Group Breakdown 1 Household Composition 1 Long Term Limiting Illness 1 Long Term Unemployed Persons 1 Population Age Breakdown 1 Unemployment Rate 1 Country of Birth 2 Health Lewis 2 Household Characteristics 2 Household Tenure 2 Population Density 2 Religious Breakdown 2 Socio Economic Classification 2 Trawl to /from work 2 Household Type 3 Industry of Occupation 3 Living Arrangemnets (married, cohabitating, etc.) 3 People Living In Communal Establishments 3 Vehicles per household 3 Students Away From Home 4
Table 1: Census Variable Rankings
Maps of most variables are useful to have, and the maps are relatively easy to produce
once guidelines for their construction have been set down. Of the twenty-two variables in the
list, seven had already been mapped by the time the survey was returned. Three variables
listed were not mapped at all. The number of students away from home and the number of
people living in communal establishments were so small that useful GIS maps could not be
constructed. There were too many different industries listed to make mapping them feasible.
Joanna Switalska agreed that there variables did not need to be mapped. The only data
mapped from outside the census were income support and incapacity benefits.
38 4.2.2 Map Colours
Different data ranges must be distinguishable when displayed in colour or greyscale, and after multiple photocopies have been made. When blues and reds are used on the same map, the regions are hard to distinguish in colour and impossible to distinguish in black and white. Single colours work well in colour and black and white. Photocopying the map increases the darkness of the areas, so with only one dark colour the map is still readable.
Several colour schemes were presented to key users of GIS maps. While most people liked the look of the green to red scheme, by far the most popular was the single colour choice. A single colour allows the reader to keep focused and not become confused.
Everyone can readily understand that a single colour progressing from light to dark means that the variable moves from a low concentration to a higher one, and the use of a single colour allows the analyst to associate a particular colour with a certain category of variables.
4.2.3 Map Scaling
One issue encountered when beginning the mapping process is that of scaling the data
ranges. If the colour scale of the ward level maps is different from that of the OA maps, then
comparison between the two is very difficult. If, on the other hand, the colour scale is
identical between wards and OAs then detail at the ward level is lost. This leads to an
extremely biased view of the borough, especially if the absolute numbers are not kept in
mind. Members of the local authority of Merton that we talked to wanted detail and
comparability. Easy to understand scales were not brought up. Ernest Obumselu mentioned
that he wanted maps that did not require a scale to successful understand the map.
The natural break scaling technique (Caspall and Jenks, 1971) can provide
comparability, detail, and accuracy at both the OA and ward levels. The following table
compares the accuracy percentage of the natural break with the equal data ranges for
39 unemployment data. The same ranges were used for both the ward level and the output area level. The natural break ranges were calculated for the output area and then used for the ward level. Even though the ranges were optimized for the output area level, they still produce very accurate results at the ward level.
Ward Level Accuracy Difference Natural Break 70.88% 31.07% Equal Ranges 39.81%
OA Level Natural Break 76.51% 44.64% Equal Ranges 31.87%
Table 2: Unemployment Rate Accuracy Comparisons
As the above table illustrates, the natural break algorithm produces much truer representations of the data points. Thus the ranges are not intuitive but highly accurate and it has the beneficial side effect of creating ranges that give detail at both the ward and OA
levels. As the users of the maps never mentioned intuitive scales as part of their requirements,
but would rather be able to look at the maps and not use the scaling numbers, this is the most
effective method of scaling for Merton.
4.2.4 Clickable GIS Maps
Matching a physical location to an output area is a large problem with all GIS maps.
The GIS map shows the physical distribution of the data, but naming of the output areas do
not correspond to any street locations. Each output area is formed from two or more
postcodes and each postcode relates to a block of street addresses. Given a particular output
area code, the associated postcodes can be found. To facilitate this process, the GIS software
was used to generate clickable GIS maps for all output area level data. Each output area in the
picture is divided into distinct, clickable regions. When an individual region is clicked on any
40 of those maps, the computer generates a new webpage that lists the postcodes contained within the selected output area.
4.3 Merton's Population Update
The Chief Executive's Office in Merton requested an effective display of the 2001 census data, and to solve this, the updated population profile was created. It was developed following the recommendations and preferences of the staff in the Chief Executives Office.
Using the suggestions, the new population profile was developed using 1991 census profile as a template. Histograms were the preferred chart type for data display. In addition, tables of data were included along with the charts. Below is a sample page in the profile developed.
Household Tenure in Merton, Outer, Inner and CI eater London 2001
Greater London
Inner London
Outer London
Merton
0% 20% 40% 60% 80% 100% nwrier Occiied a LAMA • P rivate qOther
Ai ea Ownei Occupied LA. HA Piivate ()the' Merton 68.85% 14.23% 14.31% 2.62% Outer London 67.96% 18.18% 11.33% 2.53% Inner London 39.69% 38.02% 18.77% 3.52% Greater London 56.52% 26.21 % 14.34% 2.93%
Source: GM Census 2002
Figure 7: Sample page from the population census profile
The 2001 population profile update was then distributed to other members of the local
authority of Merton and to the Greater London Authority for review. One comment was
41 received was to graph more data relating to age and gender to learn more about changing proportions of elderly individuals to which Merton provides services. The comments from these individuals were then used to make updated to the document.
4.4 Census Data
The next section of the data access system provides a method to display census data and histograms. There are twenty wards, each containing around thirty-one output areas, and twenty-three unique datasets, some containing three separate tables. To produce histograms of each table for each ward and output area would be an impossible task. Also, creating histograms of a select few wards or output areas that are currently identified in deprivation is not useful for future analyses. This established the need for dynamic data selection and histograms.
4.4.1 Design
Three databases were utilised throughout the census data section to allow for dynamic webpages. The first database was created with ward level datasets, as a single table. A second
database, created identically to the ward level statistics was created with data from the output
area datasets. The final database created contained only a single table, which stored the
pairing of all output area codes to postcodes and ward names; this data was obtained through
the Greater London Authority.
The largest problem in creating dynamic data is the size of the tables and histograms
that are produced compared to the maximum width allowed for webpages. The web
development team does not design webpages for monitors displaying a resolution greater than
800x600 pixels. Also, there are navigation bars along the top and side of all webpages on
their Intranet. This means that the creative area is only 650 pixels, a limited amount of space to display ten or more columns from a dataset. To overcome this problem, histograms are displayed with only eight bars are placed per row. This will ensure that everyone will be able to see the items and they will be displayed correctly.
The main page for the census data section uses two sets of checkboxes, one for ward names and the other for census datasets. The wards are listed alphabetically:
Select Wards
E Abbey E cannon Hill E Colliers Wood 1 Cricket Green E Dundonald
3 Figge ' s Marsh 17, Graveney E Hillside [ Lavender Fields E Longthornton E Lower Morden I— Melton Park E Pollards Hill T Ravensbury E Rayne s Park E St Helier E Trinity F7 Village E We st B arne s I— Witrible don P ark
Figure 8: Image from data access system describing ward selection process
However, since there are a lot of tables that can be selected, the categorisation of census data, described in section 4.1.2, was used to organise the tables.
43 Select Tables
Housing Travel 17 Birth Location 3 A c comp dation Type r Number of Vehicles 1— Ethnicity 3 Household Tenure 17 Travel to Work
1— Living Arrangements 1— Rooms Amenities
I— Marital Status I— Household Composition Health
I— Population by Age 9. Household Composition One I— Long Term Illness F
1— Population Total 1— Loan Parents r Health & Provision of Unpaid Care Religion I— Communal Establishments
and Educ at - Economic Activity Total 3 Industry Total r Occupation Total I— Economic Activity Male 1— Industry Male r Occupation Male I— Economic Activity Female 3 Industry Female r Occupation Female 17 Unemployment Total 1— Profession Total r Hours Worked 17 Unemployment Male 3 Profession Male r Qualifications 1— Unemployment Female 3 Profession Female
Figure 9: Image displaying one section of table selection process
All information is processed by an ASP document where Structured Query Language
(SQL) requests are sent to the database containing ward level statistics. The result of this process is a dynamically generated table. All column headings are made into links that produce histograms of that specific column. In addition, the ward names are also made into links that produce histograms of all data in that row. Below the ward names is a link to the
OA data that produces similar tables. These dynamic histograms allow the local authority to perform analyses on any dataset. When multiple tables are selected, each one will be
displayed separately.
44 Marital Status
Ingle2 Itmissi Ile-married Separated Divorced Widowed
Abbey 4034 2662 256 169 551 499 OA Data
Cannon Hill 2449 3615 398 121 400 546 glai_DILLa
Colliers Wood 3935 2447 240 242 540 367 OA Data
Cricket Green 2830 2813 429 297 703 658 OA Data
Dundonald 3658 2831 256 138 488 433 OA Data
West Barnes 2513 3741 305 127 448 533 1 0A Data Click Here to return
Figure 10: Image from the data access system — Ward Level Census Data displaying information of wards
Marital Status
—.. — . Re-married Separated Divorced Widowed ._...
Cannon Hill 151 128 14 7 15 21 00BAFY0001 h"--
Cannon Hill 134 135 15 0 9 17 00BA FY0002
Cannon Hill 128 129 18 4 10 14 00BAFY0003
Cannon Hill 168 135 15 7 7 18 00BAFY0004
Cannon Hill 129 142 17 5 16 17 00BAFY0005
Cannon Hill 164 156 20 6 17 13 00BAFY0006
Cannon Hill 131 157 10 0 12 18 00BAFY0007
Figure 10: Image from data access system — Output Area Census Data displaying information at OA Area about Cannon Hill
45 Cannon Hill
NM= 2449 3615 398 121 400 546 Simla Married Re-married Separated Divorced Widowed
Close Window
Figure 11: Image from Data Access System — Ward Level Census Data Histogram of the row data of Cannon Hill
Single
131 110 170 120 158 172 143 131 147 Abbey Abbey Abbey Abbey Abbey Abbey Abbey Abbey Abbey 00BAFX0001 , 00BAFX0002 goBArzoom 00BAFX0004 00BAFX0005 gOBAFX0006 00BAFX0007 00BAFX0008 POBAFX0009
Figure 11: Image form data access system — Output Area Census Data Histogram of column data
4.4.2 Postcode Lookup
Displaying output area statistics is useful if the areas can be located on a street level map. Therefore, on the tables produced with OA data, a link is provided to determine the
46 postcodes that are associated with a particular code. A SQL query was designed to find all postcodes that applied and a new web page was displayed with the items. The postcodes are converted into links that are directed to a map generated by www.multimap.com . Multimap provides street level maps of anywhere in Great Britain.
S -V,T 1 9 lITS SW1 9 1PF SWI 9 1PH S'..711 9 1PN S .T.Ar 1 9 1PP
Figure 12: Image from data access system — Output Area Census Data Postcodes produced from 00BAFX0001
Figure 13: Image from www.multimap.com from Postcode sw191ns
47 4.5 Tutorial
There are six sections in the Data Access System. Since not everyone in the local authority is proficient with computers, a tutorial has been created. It is divided into the four main categories: Census Data, GIS Maps, Postcode Lookup and Terminology. The other sections of the data access system do not require user input and ca not generate errors therefore a tutorial section was not created. Each section, besides terminology, provides a complete walk through of the process with pictures at each step.
48 Chapter 5: Analysis of Poverty in London
5.1 Techniques of Analysis
No consensus has been reached in regards to the measurement of deprivation in
London. Various organizations have attempted to define poverty, using both past experience and existing theories of deprivation. Almost every effort to compute levels of poverty has been met with some degree of criticism. Research into the causes of deprivation may eventually bring about a more universally accepted definition of poverty. Until that time, however, the selection, measurement, and combination of variables thought to indicate poverty remain controversial topics.
5.1.1 Indicators of Poverty
There are several important steps in forming a definition of poverty in London. First,
a decision must be made on whether to define poverty in absolute or relative terms. Absolute
poverty, the inability to afford basic, life-sustaining necessities, is quite simple to grasp.
Relative poverty, however, is more complicated. It involves quantifying somewhat abstract
concepts, such as inequality and dependency. Each philosophy affects the selection and
measurement of poverty indicators. The next step is to choose different aspects that affect
poverty, such as income, housing, and health. Although there is general agreement, within the
national and local authorities of London, on which aspects of poverty are important, two new
categories, crime and the physical environment, are somewhat controversial. Inserting these
factors generally favours the Inner Boroughs, where there is more crime, and poorer living
conditions.
From within these categories, specific indicators must then be selected, which will
represent each particular aspect of poverty. For example, several indicators could be chosen
49 to represent health deprivation, such as standardized mortality rates, long-term illness, or general health. Some of the factors involved in choosing which indicator to use include the availability and level of detail of the data, as well as the correlation between a particular indicator and other indicators from the same category.
5.1.2 Analysing Indictors of Poverty
Once indicators of poverty have been chosen, there are two ways in which they can be analysed. First, each indicator can be presented separately. Thus, each aspect of poverty is analysed individually, assuring clear and straightforward conclusions. However, there is no effective way to consider more than one indicator of poverty at a time. Although this method provides an efficient examination of single indicators and individual aspects of deprivation, it is not sufficient when attempting to gain a broader understanding of poverty.
Alternatively, multiple indicators can be analysed simultaneously, in hopes of gaining a more encompassing definition of poverty. The strength of multiple deprivation analysis lies in the fact that it addresses the multi-faceted nature of poverty, instead of focusing on only
single indicators. However, there are also weaknesses associated with the combination of deprivation indicators. Just as there is no accepted set of indicators of poverty, there exist
several techniques of multiple deprivation analysis, none of which is clearly superior to the
others.
In any method of combing indicators, there is a trade-off between transparency and
accuracy. Formulas that attempt to include a wide range of indicators can become overly
complicate and confusing. For example, the Indices of Deprivation 2002 plan to combine 33
indicators, using a complicated system of standardization and weighting. Increasing the
amount of indicators and the complexity of the techniques used also may introduce a higher
degree of error, possibly threatening validity.
50 On the other hand, less complicated formulas, that combine fewer indicators, are much easier to interpret. An example of a formula designed with maximum clarity in mind is the London Index of Deprivation, which combines seven indicators, without weighting variables. However, there is concern that formulas such as the LID do not address poverty with the same degree of detail as found on, for example, the Indices of Deprivation.
The desired result of multiple deprivation analysis is to be able to rank boroughs, wards, or output areas by level of poverty. The simplest way to do this is to order each region
by the combined scored of the standardized indicators, regardless of which methods of
standardization and weighting are employed. More useful, perhaps, are three additional
rankings, which identify three different aspects of poverty: the degree of poverty, the extent
of poverty, and the intensity of poverty. These measures can be applied at the borough or
ward level, and differentiate between the different types of poverty found throughout London.
5.2 Selecting Indicators of Poverty
In choosing variables that are highly correlated with poverty and deprivation, it is
important, as much as possible, to capture a broad range of categories. Although the details
surrounding the measurement of poverty in London continue to be a topic of debate, it has
been generally agreed that there is more to poverty than simply a lack of money. In defining
poverty, it has become evident that those who study deprivation, including both officials
inside the Local Authority of Merton and outside of it, view poverty as a condition that is
relative, and not absolute. Thus, inequalities in education, health, the physical environment,
and other areas all must be included in any definition of poverty in London.
51 5.2.1 Census Data vs. Other Types of Data
Although the census covers a broad range of data, there has been a general trend in the last ten years to decrease reliance on the census. More often, definitions of poverty use data obtained from the records and surveys of various organizations, both governmental and private. There are several reasons for this. First, the census is only conducted every ten years,
causing its data to rapidly become obsolete. Much of the data obtained by other organizations
is collected more frequently, often annually. Second, and perhaps more importantly, many
feel that the data found on the census is not sufficient to measure poverty. For example, the
census contains no information concerning income, an important indicator of deprivation.
However, the Department for Work and Pensions (DWP) keeps records of the number of
citizens collecting income support each year. Thus, it is important to include non-census data.
There is one significant advantage to using census data, however. The census is one of
the only sets of data available at the sub-ward level. For a variety of reasons, including
logistical and confidentiality issues, much of the data collected by the DWP, the Department
of Health (DoH), and other departments, can only be accessed at the ward level. In addition,
education data, environmental data, and crime data often cannot be easily aggregated into
individual boroughs or wards. Key Stage 2 test scores collected by the Department for
Education and Skills (DfES) are broken up by school district, while crime data is collected by
local police departments, and reported by the Home Office by police precinct. Air quality
samples are collected at various points throughout London, and do not coincide with local
authority boundaries. Census data, however, is collected by household, and thus can be easily
aggregated into ward and output area groupings.
52 5.2.2 Availability and Validity of Indicators
Another factor involved in selecting indicators is the availability of data. Although the
2001 census results are easily accessible, other data must often be sought out. Because much
of the desired data were collected by a variety of sources, it has been necessary to conduct numerous investigations for information. In many cases, requests and searches for data have been unsuccessful. Thus, some indicators that may have been ideal measures of poverty have
been omitted. To compensate for such omissions, there has been an attempt to replace these
indicators with ones that are strongly correlated with the missing data.
In order to produce work that will be more readily accepted by officials within
Merton's local authority, as well as by fund allocation administrators at the Greater London
Authority, the use of popularly accepted indicators in other poverty profiles has influenced
the choices made in this report. By striving to conform, whenever possible, to established
indicators of poverty, any results obtained from this analysis will be more likely to be
considered valid, and more easily comparable to other methods of measuring deprivation.
5.2.3 Indicators for the Poverty Profile
In regards to the specific indicators selected to measure poverty, all available data that
has been used by other organizations as an indicator of poverty have been considered. The
majority of these variables have been drawn from the census, although there are a few
important exceptions. Income Support and Incapacity Benefit data have been chosen,
primarily because of their inclusion in the LID. Standardized Mortality Rates (SMR)
information was also selected, as this data is generally accepted as the most accurate measure
of health deprivation. Although the process for selecting indicators to be combined using
techniques of multiple deprivation analysis will be quite rigorous, the goal of the poverty
53 profile is to present the different aspects of deprivation using as many measures as possible; this is done to gain a broader understanding of the numerous dimensions of poverty.
After aggregating and reviewing the available data, grouping the indicators into general groupings proved to be a fairly intuitive process. Most indicators of poverty fell naturally into the categories of income, employment, housing, health, and education. Due to a lack of data, crime and the physical environment could not be analysed.
5.3 Combing Deprivation Indicators
There are several techniques that can be used to combine indicators of poverty. The two most recent attempts are the Indices of Deprivation 2002 (ID2002), and the London
Index of Deprivation (LID). In trying to replicate these methods in order to compare Merton with the rest of Greater London in terms of deprivation, it was discovered that the ID2002 is still in the consultation stage, and has not been finalized at this date. Furthermore, the GLA is delaying completion of the LID until after the release of the ID2002.
5.3.1 The London Index of Deprivation
Although it has not yet been finalized, the London Index currently identifies seven aspects of deprivation: poverty in, and exclusion from, the labour force; dependency; health; education; housing; crime; and the environment. Each aspect can be defined by several indicators, most of which are derived from sources other than the census. However, the
London Index will only use one indicator to represent each of the categories. This is done to retain clarity. The choices for these "headline indicators" are made according to correlations between indicators within a category. Consider housing, for example. It can be shown that among all possible indicators of housing deprivation, overcrowding is more highly correlated with the other housing variables than any other housing indicator is. Once the categories and
indicators have been selected, each indicator is standardized to approximate a normal
54 distribution, in order to be able to compare variables with different means and variances.
After standardization, the variables are simply added together. This sum makes up the score for each borough or ward.
The standardized value of each variable is called its z-score. Because the standard normal distribution has mean 0, approximately half of the standardized data will have negative z scores, indicating a lower than average degree of poverty for a particular indicator.
Under this index, negative z-scores are recorded as 0, so that these scores will not cancel positive scores, thereby nullifying a portion of a particular area's score for poverty.
In order to perform an analysis of poverty using the LID, certain sets of data were needed. Specifically, the headline indicators used with the LID are Income Support,
Incapacity Benefit, Key Stage 2 test results, Years of Life Lost, Overcrowded Households,
Domestic Burglaries, and Air Quality. Because these data were not all available, an analysis
of poverty using the LID was not possible.
5.3.2 An alternative index of deprivation
The strength of the LID lies in the authority it carries as a product of the Greater
London Authority (GLA). However, it focuses only on comparisons between boroughs and
wards, and is not able to provide analyses at the output area level. To fill this gap, this project
has created a second index, using predominantly census data. In order to gain the support of
the Merton local authority and the GLA, the census-based index has been designed to
correspond with the LID at the ward and borough levels. Because this is the first index
designed to correspond to the new ward boundaries created after the 2001 census, statistical
comparisons to the ID2000 and LID are not possible. However, when ward level maps of
London using each index are compared, the patterns of deprivation are similar. Thus,
conclusions drawn from the census index at the output area level can be used to gain insight
55 into what might be revealed by a small area analysis using the liD2002 or LID, were such a
detailed analysis possible.
Deprivation Index By Ward
▪ 3.18 to 11.3 (163) II 1.91 to 3.18 (70) ▪ 1.03 to 1,91 (83) IN 0.42 to 1.03 (93) ig 0.06 to 0.42 (62) q 0 to 0.06 (154)
Figure 14: Ward level map of London using the census-based index
The LoP., c,, . ndt. Cvnvzsite Sew n 20% roa eVezir. +me: 052) 1,.152) 0 52) ( 1 5.2) 20, le. dep- , ved ward, 0 52:
Figure 15: Ward level map of London using the London Index of Deprivation
56 *ID Score
n 2:5., most deprred wads nsn (15) (152) q (152) D tOSICCPlived wads (1521
Figure 16: Ward level map of London using the Indices of Deprivation Since the range of poverty related categories from the census is limited, the choice of indicators used on the census-based deprivation index is restricted. Following the models set forth by the Indices of Deprivation and the London Index, the census-based index combines
five distinct aspects of poverty: income deprivation, unemployment, housing, health, and
education. A single indicator represents each category.
The percentage of people receiving income support is used to signify income
deprivation. Although income support data is only available at the ward level, it is used in the
census-based index because income deprivation is perhaps the most important measure of
poverty. In order to include income support in the index, the percentage of people receiving
benefits for each ward is used as an approximation for each output area. It should be noted,
however, that output area income data, in the form of pay check statistics, were available for
Merton. Although not useful in comparing Merton to other boroughs, the pay check data was
57 used instead of income support to compare wards and output areas within Merton to each other. All other comparisons use income support.
The percentage of people who are unemployed is used to represent deprivation in the workforce. It should be emphasized that unemployment excludes people who are economically inactive. Thus, people who are retired, or not seeking work for another reason, are not considered unemployed.
To represent health related aspects of deprivation, the percentage of people with a limiting long-term illness has been included in the census-based index. Although standardized mortality rates are commonly used to denote inadequate health care, complete data at the output area was not available. The percentage of people reporting poor health was rejected due to a lack of an obvious connection to poverty. A limiting long-term illness, however, by definition prevents one from working, and can thus can be considered an indicator of poverty.
The LID represents education deprivation by using results from Key Stage 2 exams,
given to primary school students to assess educational progress. However, this data is
unreliable at the output area level because it is collected by school district, and scores cannot
easily or accurately be attributed to any particular output area. Thus, the percentage of people
with no qualifications has been chosen for the census-based index instead. A lack of
qualifications limits the career choices one has available, and can lead to unemployment or
low income.
Finally, the percentage of houses that are overcrowded has been chosen to represent
inequality in housing. Small houses and large families are both characteristics associated with
poverty (London Divided, p. 89).
After selecting the indicators, the five data sets were put into a standard form by
computing z-scores, and combined. Similar to the LID, the census-based index omits
58 negative z-scores. The reason for this is related to the question of absolute versus relative poverty. When negative z-scores are taken into account, neighbourhoods that are doing well in some areas will receive negative scores for certain indicators, which will cancel positive scores received for by below average poverty under other indicators. Thus, households with above average education, but substandard living conditions, will not be judged as deprived.
However, using negative z-scores when combining indicators conflicts with the definition of relative poverty. Although in this example there is adequate education, there is an inequality in housing. Relative poverty attempts to measure inequality in all areas, and not an overall lack of necessities. By disregarding negative z-scores, the composite score for each borough or ward reflects the level of relative poverty experienced. No formula of weighting was used, because there is no consensus as to which indicators may be more important than others.
5.3.3 Results obtained using the census index
Having selected indicators and a method for combing them, a score for each borough, ward, and output area in London was computed. In order to give meaning to these scores, each borough, ward, and OA was ranked in order of decreasing deprivation levels. Table 3
shows the score, ranking, and percentile for all of the wards in Merton. The first three
columns give the total score for each ward, where it ranks among all London wards, and its
percentile. Higher scores indicate greater poverty, as do lower ranks and percentiles. For a
complete list of wards in London, see Appendix A.
59 Ward Score Rank Percentile Deg.% Ext.% Int.% Abbey 0.00 No rank No rank 80.1 No rank 70.3 Cannon Hill 0.00 No rank No rank 90.3 No rank 90.5 Colliers Wood 0.00 No rank No rank 81.5 No rank 87.5 Cricket Green 2.93 174 27.80% 25.6 32.8 31.2 Dundonald 0.00 No rank No rank 99.7 No rank 99.7 Figge's Marsh 1.11 311 49.68% 42.6 No rank 55.2 Graveney 0.00 No rank No rank 89.7 No rank 95.6 Hillside 0.00 No rank No rank 91.5 No rank 87.3 Lavender Fields 0.56 387 61.82% 51.3 No rank 60.8 Longthornton 0.00 No rank No rank 79.1 No rank 90.2 Lower Morden 0.42 412 65.81% 82.3 No rank 95.4 Merton Park 0.00 No rank No rank 99.2 No rank 99.2 Pollards Hill 0.75 357 57.03% 55.4 No rank 77.4 Ravensbury 1.98 228 36.42% 45.3 No rank 66.6 Raynes Park 0.00 No rank No rank 83.9 No rank 84.0 St Helier 2.53 194 30.99% 44.9 No rank 63.6 Trinity 0.00 No rank No rank 82.6 No rank 80.7 Village 0.00 No rank No rank 98.6 No rank 96.2 West Barnes 0.00 No rank No rank 99.5 No rank 99.1 Wimbledon Park 0.00 No rank No rank 97.3 No rank 94.3 Table 3: Ward statistics summary based on income support data
As the table shows, Cricket Green ranked as the most deprived ward in Merton.
Thirteen of Merton's wards received scores of 0, meaning that they had below average levels of poverty for each category. All of the wards that received positive scores are in the eastern half of the borough, which corresponds to the general east-west pattern of deprivation found at the ward level.
Using the calculated scores for the ward and OA levels, each borough and ward, respectively, can be assessed in terms of degree, rank, and intensity of poverty. Compared to the other London boroughs, Merton ranks 28 in degree of poverty and 24 in intensity, where a ranking of 1 signifies most impoverished. Because none of its wards received scores among the 10% most deprived in London, Merton has no ranking for extent of poverty. In general, outer boroughs rank higher in terms of extent of poverty, while inner boroughs are ranked
60 higher for intensity of poverty. Interestingly, in this case Merton behaves more like an inner borough.
The following table shows the degree, extent, and intensity of poverty in the wards within Merton. These three measures illustrate the distribution of poverty found in each ward.
The last three columns of Table 3 show ward level scores for Merton. The pattern of low
extent but higher intensity holds true at the ward level as well. It is interesting to note that
most of the wards have a higher degree of poverty than intensity. This is especially true for
the eastern wards, such as Figge's Marsh, Pollards Hill, and Raynes Park. This indicates
somewhat uniform levels of poverty across these output areas. There are several exceptions in
the western half of the borough, most notably Abbey and Hillside. Lower percentiles for
intensity of poverty imply that there are a small number of output areas experiencing much
more deprivation than the rest of the ward. This is confirmed in Section 5.4.2.
Using pay check data in the index, each of Merton's wards were ranked among
themselves for degree, intensity, and extent. These rankings are shown in Table 4.
Ward Degree Rank Extent Rank Intensity Rank Abbey 1.865 9 13.89% 5 8.490376 3 Cannon Hill 1.179 14 0.00% No rank 4.48274 12 Colliers Wood 1.964 8 9.38% 9 6.121901 10 Cricket Green 5.132 1 41.18% 1 9.66889 1 Dundonald 0.310 20 0.00% No rank 1.258595 20 Figge's Marsh 4.032 2 31.25% 2 9.566823 2 Graveney 1.621 10 0.00% No rank 3.33321 16 Hillside 0.998 15 2.78% 13 4.253106 13 Lavender Fields 3.399 5 21.21% 4 7.815791 5 Longthornton 2.122 7 3.33% 11 4.685497 11 Lower Morden 1.350 12 0.00% No rank 3.541978 15 Merton Park 0.488 17 0.00% No rank 1.933955 18 Pollards Hill 3.232 6 23.33% 3 7.604072 6 Ravensbury 3.482 4 9.68% 8 6.33152 8 Raynes Park 1.404 11 11.76% 7 6.729859 7 St Helier 3.908 3 12.90% 6 7.826654 4 Trinity 1.232 13 8.82% 10 6.162234 9 Village 0.332 19 0.00% No rank 2.301081 17 West Barnes 0.433 18 0.00% No rank 1.720847 19 Wimbledon Park 0.550 16 3.23% 12 4.048837 14
Table 4: Degree, Extent, and Intensity of Deprivation based on pay check data
61 Again using pay check data, Merton's output areas were given ranks. Table 5 shows
the top ten most deprived output areas in Merton. A complete list of output area scores
can be found in Appendix B. This data is mapped in section 5.4.2.
Ward OA Combined Score Cricket Green 00BAGA0033 10.63 Figge's Marsh 00BAGC0025 10.26 Figge's Marsh 00BAGC0020 9.37 Cricket Green 00BAGA0018 9.28 Cricket Green 00BAGA0026 9.10 Figge's Marsh 00BAGC0018 9.07 Abbey 00BAFX0027 9.04 Cricket Green 00BAGA0019 8.90 Cricket Green 00BAGA0020 8.78 Cricket Green 00BAGA0022 8.75
Table 5: Hotspots of Deprivation based on the pay check index
5.4 Geographical Comparisons
GIS can be used to make effective comparisons between output areas, wards, and borough in London. The GIS software displays the geographical dispersion of census data at the output area, ward, and borough level. By using the same colour schemes and scales for each level of detail mapped, visual comparisons of data at different levels can be extremely efficient.
5.4.1 London Level
Inner London tends to have higher deprivation indicator levels than Outer London. On all five indicator maps this pattern is followed. Figure 17 is a map of unemployment rates for
Greater London at the ward level. The unemployment rate is highest in the centre of London and generally decreases towards the outer boundaries. There are outer wards that have high unemployment rates, but the map shows that efforts to stimulate the job market should be focused on inner London.
62 Unemployment Rate By Weird a 5.7 to 12.3 (141) la 3.9 to 5.7 (196) 2.8 to 3.9 (145) O 2 to 22 (118) q 1 to 2 (24) q 0 to 1 (1)
Figure 17: Unemployment Rate by ward for Greater London
Percentage of Residents with No Qualifications By OA
▪ 32.8 to 72.1 (542) la 25.4 to 32.8 (807) ▪ 18.9 to 25.4 (1085) O 13.2 to 18.9 (1030) q 8.5 to 13.2 (750) q 0 to 8.5 (501)
5,,,..4064 owe, -16
44,410k, fitip,4114, -11
Figure 18: Unemployment Rate by OA for Southwest London
Figure 18 shows that in southwest London at the output area level, the situation is different. It is clear that in Outer London there are pockets of high unemployment rates that
63 are comparable to Inner London. This pattern shows up for the other four individual indicator variables and in the deprivation index itself. Figure 19 shows the deprivation index for
Greater London. The east of Merton is comparable to Inner London, although the rest of the outer boroughs appear to be much less deprived than Inner London. A more interesting view however, is the output areas of southwest London. Figure 20 shows that not only do some
output areas have the same deprivation index score as Inner London output areas, but that there is actually a very large pocket of deprivation that contains part of southeast Merton,
north Sutton and northwest Croydon.
This mega pocket does not show up on other maps because even though it is the same
physical size as most wards, it is composed of the output areas of several wards. The small
areas of deprivation that make up the much larger one are part of wards that have output areas
with much lower deprivation scores so the intense scores get washed out when the data is
averaged to produce the ward level numbers. More GIS maps of deprivation indicators and
the deprivation index itself at the London level are contained in digital appendix D.
Deprivation Index By Ward
• 3.18 to 11.3 (163) II 1.91 to 3.18 (70) • 1.03 to 1.91 (83) III 0.42 to 1.03 (93) q 0.06 to 0.42 (62) q 0 to 0.06 (154)
Figure 19: Deprivation Index by ward for Greater London
64 Deprivation Index 8y OA 3.18 to 17.6 (616) 1.91 to 3.18 (519) 1.03 to 1.91 (582) 0.42 to 1.03 (680) el 0.06 to 0.42 (685) El 0 to 0.06 (1633)
Figure 20: Deprivation Index by output area for Southwest London
5.4.2 Ward Level
In Merton, there are higher concentrations of indicators of poverty on the east side than the west side. This is common knowledge among the members of the local authority of
Merton, and the GIS maps that have been produced confirm it. Figure 21 is a GIS map of the unemployment rate at the ward level. East of Abbey and Ravensbury are the wards with the highest rates of unemployment. The four wards down the centre of Merton fall into the midrange of unemployment, and the wards west of them have the lowest rates. This east to west, highest to lowest pattern can be seen again and again.
Figure 22 is a ward level map of overcrowding in Merton. In general, households in the east of Merton experience more overcrowding than households in the west of Merton.
Households in Raynes Park and Hillside are notable exceptions.
65 Unemployment Rate By Ward
5.7 to 12.3 (0) El 3.9 to 5.7 (7) q 2.8 to 3.9 (4) q 2 to 2.8 (9) to 2 (0) Viiirribleoon F ark q 0 to 1 (0)
Village
Colliers Wood
Abbey Lavender Fields Raynes Park
Merton Park Figges Marsh
Cannon Hill Pollards Hill West Barnes
Cricket Green
Lower Morden
Figure 21: Unemployment Rate by Ward
Percentage of Households Experiancing Overcrowding By Ward • 24.7 to 49.6 (0) q 16.5 to 24.7 (3) q 11.5to 16.5 (9) q 7.5 to 11.5 (5) Wimbledon Park 1:1 4.2 to 7 5 (3) q 0 to 4.2 (0)
Village
Hillside Graveney Dundonald
Abbey Raynes Park Lavender Fields Longthornton
Merton Park Figges Marsh
Cannon Hill Pollards Hill West Barnes Ravensbury Cricket Green
St Helier
Lower Morden
Figure 22: Percentage of Households Experiencing Overcrowding by Ward
Examining ward level data makes it appear as though Merton's overall deprivation is lower than the rest of London. For example, figure 23 shows the percentage of households in
66 Merton that share a toilet at the ward level. All but one ward has less than two percent of households without sole use of the toilet.
Percentage of Households without Sole Use of Toilet By Ward • 8 6to 14.9 (0) • 5.7 to 8.6 (0) O 3.3to 5.7 (0) q 2.5to 3.3 (0) q 1.9to 2.5 (1) q 0 to 1.9 (19) Wimbledon Park
Village Hillside
Colliers Wood Dundonald Graveney
Abbey Lavender Fields Raynes Park Longthornton Figges Marsh Merton Park
Cannon Hill Pollards Hill West Barnes Ravensbury Cricket Green
St Helier
Lower Morden
Figure 23: Percentage of Households without Sole Use of Toilet by Ward
The deprivation index of Merton at the ward level as shown in figure 24 indicates that
the highest score is 2.88, while the average is only 1.3. These and other maps at the ward
level mask any intense pockets of deprivation by averaging out the high and low values.
More GIS maps of census data at the ward level are contained in the digital appendix D.
67 Deprivation Index By Ward
• 3.18 to 7.86 (0) • 1.91 to 3.18 (3) II 1.03 to 1.91 (1) ▪ 0.42 to 1.03 (2) la 0.06 to 0.42 (1) q 0 to 0.06 (13)
Figure 24: Merton Deprivation Index by Ward
5.4.3 Output Area Level
When census data is displayed at the output area level, it reveals that Merton has high concentrations of deprivation indicator data even among the wards that appeared to have the lowest levels. When determining where to allocate funds and how much to give, the Greater
London Authority only analyses the ward level data. At first, this seems reasonable, but after mapping the census data at the output area level, it becomes evident that the ward level maps do not tell the whole story. When the data displayed in figure 25 is mapped at the output area level, it is obvious that Merton looks very different. Figure 26 is a map of the percentage of households without sole use of a toilet. While the percentage of households seldom exceeds two percent, many output areas have much higher percentages.
68 Deprivation Index By OA II 3.18 to 7.86 (38) II 1.91 to 3.18 (54) II 1.03 to 1.91 (76) III 0.42 to 1.03 (93) El 0.06 to 0.42 (103) 0 to 0.06 (267) , Village tHillside ? ilvo ., riii. $0„....,_ .4. fi ‘. Afii ik it 10)-4:4-4% Ay' licorkviiiir4 N 0 raveney Dundonald .Vfintilk 11,-
`% AbbeyAbbey g4 4$44. ' I .4 A*414 Lavender •• -• \Fields Raynes Par AA 0 ii ia tip, ri 4.s 10 1-04( ',MO JR Figges Marsh 4 4k,, Merton Par Itir_ i t .44 i Cannon Hill
est Barnes Ravensbu
MOE
Figure 25: Merton Deprivation Index by OA
Percentage of Households without Sole Use of Toilet By OA • 8.6 to 14.9 (10) 1111 5.7 to 8.6 (6) 3.3 to 5.7 (22) q 2.5 to 3.3 (42) q 1.9 to 2.5 (74) q 0 to 1.9 (477)
Figure 26: Percentage of Households without Sole Use of Toilet by OA
69 In one output area, fifteen percent of its households share toilet facilities.
While at the ward level (figure 24) it appears that the most deprived areas of Merton are all in the east, the output area map (figure 25) shows that the most deprived areas are actually scattered throughout the borough. The reason that the ward level maps look so different from the output area maps is that the ward level data is really just an average of all the output area data that is contained by that particular ward. Thus, a highly deprived output
area surrounded by low deprivation output areas can be overlooked at the ward level because
the low deprivation areas decreases the overall average. Ward level data can mask the
existence of a problem in a specific output area, or group of output areas. This could lead to a
misallocation of funds.
At the ward level the west appears completely affluent while the east appears terribly
deprived. However, at the sub-ward level it can be seen that the west has some of the most
deprived output areas while the east has some of the least deprived output areas. Figure 24
shows the unemployment rate by output area. While the eastern half of Merton experiences a
higher unemployment rate than the western half, some areas in the western half do have
higher rates of unemployment. These areas include the area where the wards of Hillside,
Wimbledon Park, and Trinity meet, as shown in figure 27.
70 Unemployment Rate By OA • 5.7 to 12.3 (64) • 3.9 to 5.7 (146) Eg 2.B to 3.9 (126) Q 2 to 2.8 (120) q 1 to 2 (149) q o to 1 (26) si
aLti-N4/4tr'144' AO\ 4 40100 „ 10‘, 1114101n 111101 le r Wood 111,1 t i• 111E--14 vst, rior A Lavender Fields
Figure 27: Unemployment Rate by OA
Percentage of Households Experiancing Overcrowding By OA 24.7 to 49.6 (49) q 16.5 to 24.7 (112) q 11.51°16.5 (137) q 7.5 to 11.5 (135) q 4.2 to 7.5 (122) q 0 to 4.2 (76)
Colliers Wood VEW"
144 avender Fields 4Y".",%,
Figure 28: Percentage of Households Experiencing Overcrowding by Ward
71 Figure 28 shows the percentage of households experiencing overcrowding by output area. The majority of output areas experiencing high levels of overcrowding are in the east but not exclusively, as the small dark band across the wards of Raynes Park, Hillsides, and
Wimbledon Park shows. These maps reinforce the statistical evidence that focusing solely on data at the ward level can ignore smaller pockets of poverty. Even the most affluent wards have areas that are comparable to the most deprived wards. More GIS maps of data at the output area level are contained in the digital appendix D.
5.5 Direct Observation
All twelve of the sites planned for direct observation were visited. The output areas
matched what was expected from the calculation in all except one case. The site in St. Helier
appeared to be in good condition, despite the fact that the maps produced indicated a high
level of deprivation in that area. One possible reason for this is that the homes may have a
higher number of individuals with long-term limiting illness or a higher level of
overcrowding. These two indicators can be difficult to observe during the day, when the
families are at work and at school.
Based on the direct observation, 11 out of the 12 sites observed appeared to fit the
expectations. The three most convincing areas which showed signs of deprivation were
Mitcham town centre, Haydons Road, and Abbey. In contrast, many areas in Merton appear
to be affluent, including those surrounding these problem points, which support the claim that
there are pockets of deprivation.
72 5.5.1 Mitcham Centre
The town centre of Mitcham is a concern for the borough of Merton. The individuals within Merton interested in Neighbourhood Renewal recognize Mitcham as an area requiring aid. The deprivation index confirmed what those individuals believed, and visiting Mitcham was a high priority. Mitcham centre and the surrounding areas were identified by the deprivation index to be part of a large pocket of deprivation spanning across eastern Merton and into the surrounding boroughs of Croydon and Sutton.
Mitcham centre in general shows signs of being run-down and in need of renewal.
Many of the homes appear to be deprived and in of need repairs. Figure 29 depicts two of the many locations in the centre of Mitcham. The example on the left is from a residential area, while the photograph on the right is from a business sector. These areas could benefit from neighbourhood renewal.
Figure 29: Mitcham centre. Left: Photograph taken on Love Lane. Right: An alley way between two strips of businesses in the centre of Mitcham
Figure 30 shows a housing complex on Western Road, in the centre of Mitcham.
There are two other large buildings next to the pictured building that share the same design,
state, and condition. The buildings themselves are dirty, and some of the doors and windows
are boarded up, suggesting they are uninhabitable. But the parking lot was filled with cars,
73 indicating many individuals and families are residing there in spite of the dilapidated conditions.
Figure 30: Housing complex on Western Road
5.5.2 Haydons Road
Haydons Road runs through the Wimbledon ward and terminates near the South
Wimbledon Underground Station. The road itself runs through an affluent area; however,
businesses and homes along the road show visual signs of depression. Many business
locations are boarded up and the windows are painted over, especially in one area where there
is a tram line running under the road. In Figure 31, the two photographs are of the business
district on Haydons Road. As can be seen, this area would be an ideal candidate for
economic renewal, since there are many empty business locations.
74 Figure 31: Haydons Road top: Empty business locations
The main road is not the only depressed location. Streets off of Haydons Road also show signs of need. Figure 32 is a home which has been abandoned.
Figure 32: Boarded up home on Gap Street, which is located off of Haydons Road
5.5.3 Abbey
Abbey ward is located near South Wimbledon Underground Station. Abbey is a centre of industry, with a long strip of manufacturing companies. Figure 33 shows two of the many industrial buildings; some of the buildings are very well kept, while others could use
some work. The industrial areas themselves did not appear to be the concern. Instead, the
homes in the surrounding area seemed to be in need.
75 Figure 33: Industrial buildings on Jubilee Street in Abbey Ward.
The homes in general appeared to be decent buildings, but the area was very overgrown and dirty. Figure 34 is a photograph of a run-down street in Abbey taken near the
industrial area. The property is not well kept and is cluttered, giving it the appearance of a
deprived community.
Figure 34: Residential locations in Abbey off of Liberty Street.
5.5.4 Affluent Neighbourhoods
In addition to visiting the hot spots for deprivation, homes were also viewed in the
areas calculated to be more affluent. Figure 35 shows homes from different areas in the
borough. Most of the areas in Merton have multi-family houses; however, in the richer areas,
such as Village, very large single family homes are not uncommon.
Three out of the four photographed homes are in areas bordering the identified output
area in the poverty profile. These homes all appear to be in excellent condition, and free from
76 visible signs of depression. This supports the ideas of the individuals working in the neighbourhood renewal department in Merton, which was that the poverty is in pockets.
Figure 35: Affluent homes in the different boroughs of Merton.
Top Left: Abbey Top Right: Village Bottom Left: Colliers Wood Bottom Right: St. Helier
77 Chapter 6: Conclusions
6.1 Single resource for census information
A single resource for census data that can be accessed via an Intranet is a valuable tool for the local authority. The Intranet data access system incorporates three commonly used presentation styles:
• Tables and Histograms at both the ward and OA level
• A Geographical Information System to create maps.
• Written documents that provide key comparisons and analyses
The histograms are generated dynamically, unlike such resources as the National Statistics website. That is, users of the Merton Intranet census data system select just the datasets and the wards or output areas that interest them. They are not restricted to a predetermined set of comparisons or charts.
In addition, the data access system provides multiple methods to identify which postcodes are associated with an output area. A user can click on any region in a GIS map and automatically obtain the correct postcodes for that area, thereby precisely locating
"hotspots" on a particular data map. From the postcodes, another click brings users to street maps (provided by www.multimap.com ). These features take users from a geographical representation of the data to an exact physical location.
6.2 Mapping Styles and Data Presentation
Single colour GIS maps provide the clearest view of the census data because the data
is mapped by the amount or concentration of that particular variable in the geographical
areas. A single colour that starts in a pale shade and gradually darkens for more concentrated
areas is instantly understandable and can be associated with a broad category of census
78 variables. This is what has been done with this project: blue denotes general demographic maps, green shows economic and education maps, red signifies health statistics, gold represents housing, and greyscale corresponds to transportation. It is recommended that these colour schemes are kept when future maps are made of census data in order that they may be easily compared to the maps produced here.
The natural break scaling technique does not produce the most intuitive range of values, but it provides the best accuracy and the most direct comparisons between the ward and output area levels. It is hoped that the scale ranges will not even be needed to examine the map, except to see what the minimum and maximum data values are. It is recommended that future maps use exactly the same scale as their counterparts here use so as to provide for
maximum comparability between different data sets.
Maps of census data at the output area level are necessary in understanding the state
of affairs in Merton. To avoid the possibility of ignoring troubled neighbourhoods, all future
work with census data should be done at the output area level. Any analysis should be done at
the highest resolution as possible in order to increase the accuracy and efficiency of the
analysis.
6.3 Poverty Analysis
In examining poverty in Merton, this project has created an index that, while
replicating on the ward level the results of the Indices of Deprivation and the London Index
of Deprivation, provides a more detailed analysis by considering patterns found at the output
area level. The more specific conclusions that follow illustrate the importance of performing
a small area analysis of deprivation, rather than basing decisions on ward level statistics. This
principle holds true whether one is using single indicators or a combined index.
Examination of individual indicators has shown that different aspects of deprivation
are more prevalent in some wards than in others. For example, although output areas in
79 Cricket Green generally rank as the most impoverished in Merton, none of its output areas are particularly overcrowded. Using only overcrowding to measure poverty would wrongly characterize Cricket Green as not deprived. Even wards experiencing similar overall levels of deprivation often suffer for different reasons. Three output areas in Abbey are among the five highest in Merton in terms of the percentage of people earning below £20.000. However, none of these OAs can be found in any of the other top ten lists. On the other hand, output areas in Figge's Marsh and Cricket Green suffer from of a variety of aspects of poverty.
Overall, however, Abbey's most deprived OAs have similar scores to those of Figge's Marsh and Cricket Green.
Combing indicators of poverty into a single index has shown that, although the overall level of poverty found in Merton seem low at first glance, a closer look shows that there are areas within the borough suffering great poverty. While not ranking extremely high in any single category, Cricket Green has one output area that is among the most deprived 2% of
OAs in Greater London.
Forming a combined index of deprivation helps to explain the nature of poverty in
Merton. Although Merton ranks low in terms of degree and extent of poverty, several of the wards received higher rankings for the intensity of poverty suffered. Interestingly, high scores for extent of poverty are generally found in rural areas, while high scores for intensity are more of an urban phenomenon. The fact that poverty in Merton follows the urban model is an
example of how it is similar in many ways to the Inner Boroughs.
In addition, high rankings for intensity imply that there are small pockets of poverty
in Merton, which if not surrounded by wealthier areas, would cause ward level rankings to be
much higher. Thus, only analysing ward level data masks differences at the output area level.
As has been shown, these differences can be quite significant. The variance between output
area scores is in general greater than the variance between ward scores. Thus, although basing
80 decisions regarding poverty relief on ward level data addresses areas with abundant, widespread, deprivation, it ignores areas where poverty is distributed in scattered, concentrated pockets.
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84 Appendix A - Degree, Extent, and Intensity
London-wide ward level statistics for overall score, rank, percentile, degree of poverty, extent of poverty, and intensity of poverty based on the census index
)roug h Ward Score Rank Percentile Deg.% Ext.')/0 Int.% irking and Dagenham Abbey 2.89 177 0.28 28.32 32.28 30.06 Barking and Dagenham Alibon 6.31 48 0.08 13.29 21.84 31.33 and Dagenham Becontree 3.74 144 0.23 26.74 43.51 44.94 irking and Dagenham Chadwell Heath 4.98 92 0.15 19.30 14.56 22.15 barking and Dagenham Eastbrook 3.04 169 0.27 37.34 41.14 45.57 Barking and Dagenham Eastbury 5.04 88 0.14 21.84 23.73 24.05 I irking and Dagenham Gascoigne 7.72 19 0.03 3.01 1.90 7.91 LJrking and Dagenham Goresbrook 5.52 74 0.12 18.20 32.12 40.82 Barking and Dagenham Heath 6.94 30 0.05 6.01 11.55 7.12 irking and Dagenham Longbridge 1.16 303 0.48 61.23 68.83 I irking and Dagenham Mayesbrook 6.24 52 0.08 13.13 26.42 35.60 Barking and Dagenham Parsloes 6.28 51 0.08 12.82 20.73 33.23 fl-rking and Dagenham River 3.82 139 0.22 28.80 30.70 42.72 irking and Dagenham Thames 5.77 69 0.11 9.65 9.65 21.68 barking and Dagenham Valence 6.14 56 0.09 14.24 20.89 37.03 Barking and Dagenham Village 5.19 84 0.13 13.45 15.03 22.94 I irking and Dagenham Whalebone 1.60 268 0.43 53.96 70.41 L.--rnet Brunswick Park 0.00 85.92 74.05 Barnet Burnt Oak 3.50 156 0.25 23.26 31.01 18.35 I met Childs Hill 0.15 450 0.72 63.29 58.07 59.02 1 rnet Colindale 2.51 196 0.31 25.79 25.16 24.84 Barnet Coppetts 0.00 71.84 57.12 51.58 r rnet East Barnet 0.00 91.30 92.25 I met East Finchley 0.03 479 0.77 59.34 57.59 38.13 Barnet Edgware 0.00 68.67 54.27 47.15 Rcirnet Finchley Church End 0.00 88.92 91.14 I rnet Garden Suburb 0.00 81.65 56.33 47.31 Larnet Golders Green 0.00 70.89 38.92 45.25 Barnet Hale 0.00 76.74 72.94 E rnet Hendon 0.00 75.95 68.99 rnet High Barnet 0.00 84.81 76.74 Barnet Mill Hill 0.00 89.87 6.65 F rnet Oakleigh 0.00 87.66 91.61 rnet Totteridge 0.00 84.49 78.32 Barnet Underhill 0.69 366 0.58 54.91 56.65 52.85 P - rnet West Finchley 0.00 87.97 79.91 rnet West Hendon 0.45 406 0.65 51.42 55.22 52.53 bdrnet Woodhouse 0.27 428 0.68 72.94 72.47 Bexley Barnehurst 0.49 398 0.64 70.57 80.06 E xley Belvedere 0.94 329 0.53 51.74 47.47 43.04 L _xley Blackfen and Lamorbey 0.56 385 0.62 75.47 80.85 Bexley Blendon and Penhill 0.40 416 0.66 83.39 89.56 xley Brampton 0.54 389 0.62 70.73 82.91 xley Christchurch 0.27 429 0.69 79.43 76.27 Bexley Colyers 1.72 252 0.40 39.40 44.46 48.89 P -xley Cray Meadows 2.02 227 0.36 48.10 57.75
85 )rough Ward Score Rank Percentile Deg.% Ext.')/0 Int.% Bexley Crayford 1.54 271 0.43 52.22 -61.87 :xley Danson Park 0.80 351 0.56 63.92 -67.88 xley East Wickham 1.64 265 0.42 50.32 -62.03 Bexley Erith 0.84 344 0.55 48.89 43.99 38.29 Rexley Falconwood and Welling 0.67 369 0.59 76.90 88.29 !xley Lesnes Abbey 1.26 296 0.47 43.20 33.23 40.98 Dexley Longlands 0.53 392 0.63 64.56 33.70 40.66 Bexley North End 2.88 178 0.28 24.68 32.91 40.03 !xley Northumberland Heath 0.70 365 0.58 72.47 -66.93 _!xley Sidcup 0.27 430 0.69 69.94 33.86 45.89 Bexley St Mary's 0.00 - - 84.34 -85.76 :xley St Michael's 0.62 375 0.60 72.31 -56.17 !xley Thamesmead East 2.08 220 0.35 31.01 27.85 25.32 Brent Alperton 2.03 225 0.36 40.51 43.83 57.91 Prent Barnhill 1.42 283 0.45 39.56 48.73 43.83 I ent Brondesbury Park 0.89 338 0.54 54.11 -56.01 Brent Dollis Hill 1.53 274 0.44 44.15 -52.06 Brent Dudden Hill 1.29 294 0.47 49.37 49.84 50.32 I ent Fryent 0.55 388 0.62 58.23 -68.20 L. ent Harlesden 6.69 39 0.06 5.70 6.80 13.45 Brent Kensal Green 2.25 209 0.33 34.49 45.09 29.59 I ent Kenton 0.00 - - 85.60 -86.55 I ant Kilburn 4.98 91 0.15 12.34 11.87 12.97 Brent Mapesbury 1.54 273 0.44 47.15 55.06 56.33 l'ent Northwick Park 0.00 - 79.91 -84.49 I ant Preston 0.06 473 0.76 68.99 -68.67 Brent Queens Park 0.85 343 0.55 55.22 37.66 37.97 Brent Queensbury 0.58 384 0.61 67.41 -83.54 I ant Stonebridge 7.85 17 0.03 2.06 2.06 7.44 L. ant Sudbury 1.54 272 0.43 47.47 -67.25 Brent Tokyngton 1.16 304 0.49 52.37 -68.35 I ant Welsh Harp 1.68 258 0.41 44.62 -75.32 I ant Wembley Central 2.48 197 0.31 38.13 -75.47 Brent Willesden Green 2.96 171 0.27 28.64 50.47 27.22 rDml ey Bickley 0.00 - - 87.34 -82.75 I Dmley Biggin Hill 0.00 - - 94.46 -92.41 Bromley Bromley Common and Keston 0.33 425 0.68 71.36 -63.45 Bromley Bromley Town 0.00 - - 89.56 -76.58 E Dmley Chelsfield and Pratts Bottom 0.00 - - 88.77 54.43 60.13 L omley Chislehurst 0.14 454 0.73 80.38 55.70 70.57 Bromley Clock House 0.00 - 87.82 -84.34 Dml ey Copers Cope 0.00 - - 85.28 58.54 62.97 E Dml ey Cray Valley East 1.84 242 0.39 41.30 57.44 49.21 Bromley Cray Valley West 3.69 145 0.23 30.06 34.02 18.20 F Dmley Crystal Palace 1.97 229 0.37 33.07 38.45 29.75 Dml ey Darwin 0.59 381 0.61 70.25 -88.13 Bromley Farnborough and Crofton 0.00 - - 85.76 -77.22 Bromley Hayes and Coney Hall 0.00 - - 88.13 84.18 Dmley Kelsey and Eden Park 0.00 - 77.53 73.42 bi om ley Mottingham and Chislehurst North 2.47 199 0.32 36.39 50.16 Bromley Orpington 1.08 314 0.50 57.75 71.68 I Dml ey Penge and Cator 0.80 352 0.56 47.63 46.04 L Dml ey Petts Wood and Knoll 0.00 - - 96.84 91.77 Bromley Plaistow and Sundridge 0.01 482 0.77 75.00 73.10 Dml ey Shortlands 0.00 - - 96.20 95.25
86 I wough Ward Score Rank Percentile Deg.% E x t cb/0 Int.% Bromley West Wickham 0.00 81.17 54.91 34.34 r9mden Belsize 1.41 285 0.46 46.52 31.96 36.71 imden Bloomsbury 2.91 175 0.28 25.47 26.90 37.82 Camden Camden Town with Primrose Hill 3.86 136 0.22 22.31 24.05 0.47 Camden Cantelowes 3.92 133 0.21 20.09 19.46 10.28 imden Fortune Green 1.28 295 0.47 45.57 48.26 43.35 LJmden Frognal and Fitzjohns 0.51 394 0.63 69.15 70.73 Camden Gospel Oak 4.65 103 0.16 14.08 10.13 6.33 imden Hampstead Town 0.22 443 0.71 75.79 36.55 48.58 imden Haverstock 5.21 83 0.13 8.54 8.54 0.95 Camden Highgate 1.52 276 0.44 36.71 35.44 42.09 rimden Holborn and Covent Garden 4.01 132 0.21 19.62 20.09 15.35 imden Kentish Town 3.16 165 0.26 25.16 37.18 28.64 Camden Kilburn 5.89 67 0.11 9.18 10.44 3.96 Camden King's Cross 4.21 123 0.20 10.60 8.07 26.42 Imden Regent's Park 4.85 95 0.15 11.71 13.92 13.61 Camden St Pancras and Somers Town 8.66 10 0.02 1.42 1.27 1.42 Camden Swiss Cottage 0.63 373 0.60 57.91 50.63 imden West Hampstead 1.66 261 0.42 41.93 50.79 56.49 :y of London (all wards) 1.89 235 0.38 62.82 22.47 85.60 Croydon Addiscombe 0.00 62.66 98.10 C oydon Ashburton 0.21 445 0.71 30.54 14.72 23.73 oydon Bensham Manor 0.80 353 0.56 31.49 15.82 78.96 Croydon Broad Green 1.73 250 0.40 13.61 13.13 50.00 Croydon Coulsdon East 0.00 82.12 99.53 oydon Coulsdon West 0.00 41.61 93.35 Croydon Croham 0.00 81.01 99.37 Croydon Fairfield 0.05 475 0.76 73.89 58.39 43.51 oydon Fieldway 6.55 42 0.07 65.03 51.74 53.64 oydon Heathfield 0.00 59.81 56.96 57.12 Croydon Kenley 0.00 36.55 55.85 45.41 oydon New Addington 4.98 90 0.14 76.27 67.56 oydon Norbury 0.01 483 0.77 95.09 91.46 Croydon Purley 0.00 92.56 87.66 Croydon Sanderstead 0.00 61.08 35.13 14.40 oydon Selhurst 1.69 257 0.41 4.75 5.38 13.13 oydon Selsdon and Ballards 0.00 90.51 87.18 Croydon Shirley 0.12 460 0.73 93.51 87.03
( oydon South Norwood 0.60 379 0.61 14.87 16.61 30.54 C oydon Thornton Heath 0.19 447 0.71 76.58 68.51 Croydon Upper Norwood 0.43 410 0.65 91.61 90.03
( oydon Waddon 1.29 293 0.47 84.97 78.01
( oydon West Thornton 0.84 346 0.55 35.76 57.91 43.67 Croydon Woodside 0.14 453 0.72 99.05 98.42 Fling Acton Central 0.94 328 0.52 61.39 58.23 E ling Cleveland 0.27 432 0.69 52.06 58.23 56.65 Ealing Dormers Wells 3.91 134 0.21 67.72 39.72 41.14 Ealing Ealing Broadway 0.27 427 0.68 61.55 65.35 E ling Ealing Common 0.04 477 0.76 40.82 56.17 26.74 Luling East Acton 0.89 340 0.54 56.33 55.54 Ealing Elthorne 0.24 437 0.70 78.01 86.23 E ling Greenford Broadway 1.88 237 0.38 50.47 55.70 E ling Greenford Green 0.00 484 0.77 49.84 30.54 32.12 Ealing Hanger Hill 0.00 24.37 23.89 31.65 Fling Hobbayne 0.07 470 0.75 77.69 85.13
87 )rough Ward Score Rank Percentile Deg.% Ext.% Int.')/0 Ealing Lady Margaret 1.40 286 0.46 72.15 - 82.28 P9ling North Greenford 0.00 - - 48.26 54.59 52.37 Sling Northfield 0.00 - - 53.80 38.29 30.70 Ealing Northolt Mandeville 1.65 262 0.42 38.29 52.22 47.47 Ealing Northolt West End 3.61 150 0.24 74.05 49.53 52.69 Sling Norwood Green 4.16 124 0.20 92.41 94.78 __fling Perivale 0.00 - 55.54 53.16 49.05 Ealing South Acton 2.28 208 0.33 53.48 - 71.52 Sling Southall Broadway 4.22 121 0.19 84.18 - 77.53 fling Southall Green 3.20 163 0.26 91.14 - 74.37 Ealing Southfield 0.00 - 37.50 37.34 32.91 riling Walpole 0.00 - 22.15 27.22 18.83 ifield Bowes 0.81 348 0.56 19.94 17.56 16.93 Enfield Bush Hill Park 0.00 - - 80.85 79.11 Enfield Chase 1.19 299 0.48 28.96 31.17 26.58 ifield Cockfosters 0.66 370 0.59 25.00 53.01 -. ifield Edmonton Green 6.86 32 0.05 31.80 65.03 Enfield Enfield Highway 2.48 198 0.32 85.13 86.39 ifield Enfield Lock 1.84 243 0.39 84.02 53.80 69.15 ifield Grange 0.00 - 52.69 - 62.18 Enfield Haselbury 2.94 173 0.28 82.75 - 88.45 rifield Highlands 0.70 363 0.58 46.99 52.53 46.36 I 'field Jubilee 2.19 215 0.34 58.07 50.00 42.56 Enfield Lower Edmonton 3.74 143 0.23 4.91 6.17 2.06 Enfield Palmers Green 0.20 446 0.71 31.17 52.06 39.40 'field Ponders End 2.02 226 0.36 33.23 36.87 37.34 Ifield Southbury 1.65 263 0.42 90.66 - 86.87 Enfield Southgate 0.13 458 0.73 30.70 25.00 24.68 ifield Southgate Green 0.11 462 0.74 74.84 51.90 57.28 'field Town 0.00 - - 35.13 37.03 44.46 Enfield Turkey Street 3.52 153 0.24 21.36 22.15 24.53 field Upper Edmonton 4.51 106 0.17 59.18 53.32 52.22 'field Winchmore Hill 0.00 - - 32.44 28.64 21.20 Greenwich Abbey Wood 5.48 75 0.12 36.08 50.63 43.99 nreenwich Blackheath Westcombe 0.68 368 0.59 74.21 - 61.08 1 eenwich Charlton 3.52 154 0.25 58.70 50.16 54.11 u, eenwich Coldharbour and New Eltham 2.35 201 0.32 87.03 - 83.07 Greenwich Eltham North 0.39 418 0.67 24.84 28.96 32.75 ( eenwich Eltham South 0.99 320 0.51 16.46 18.35 22.47 ( eenwich Eltham West 6.42 47 0.08 85.44 - 79.75 Greenwich Glyndon 4.70 100 0.16 12.03 13.61 12.50 I eenwich Greenwich West 1.14 308 0.49 57.59 55.54 45.09 ( eenwich Kidbrooke with Hornfair 3.86 137 0.22 23.73 21.04 22.63 Greenwich Middle Park and Sutcliffe 4.15 126 0.20 42.41 - 51.42 ''eenwich Peninsula 1.10 312 0.50 62.03 51.58 53.16 ( eenwich Plumstead 2.13 219 0.35 48.73 52.69 48.42 (Dr eenwich Shooters Hill 0.58 383 0.61 6.49 10.28 4.75 Greenwich Thamesmead Moorings 2.64 189 0.30 14.56 13.77 5.38 ( eenwich Woolwich Common 5.33 78 0.12 38.61 52.37 41.77 eenwich Woolwich Riverside 7.40 22 0.04 24.05 30.06 27.69 Hackney Brownswood 4.22 122 0.19 23.10 24.84 25.00 I ickney Cazenove 4.33 113 0.18 40.03 34.81 23.42 I ickney Chatham 7.85 18 0.03 34.65 30.38 27.53 Hackney Clissold 3.11 168 0.27 60.44 72.78 Hackney Dalston 5.99 62 0.10 30.38 29.27 33.54
88
)rough Ward Score Rank Percentile Deg.% E xt. Int.%
Hackney De Beauvoir 5.91 65 0.10 9.02 6.33 10.92
u -ackney Hackney Central 6.82 34 0.05 3.80 2.85 3.32
3ckney Hackney Downs 6.48 45 0.07 15.82 17.09 1.11
Hackney Haggerston 7.33 23 0.04 18.04 15.66 18.67
Hackney Hoxton 8.25 14 0.02 2.85 2.53 6.49
ackney King's Park 6.77 36 0.06 22.94 17.88 17.09
..ackney Leabridge 4.68 101 0.16 7.59 5.54 9.97
Hackney Lordship 3.45 157 0.25 9.34 5.85 14.72
ackney New River 6.51 44 0.07 6.33 7.59 10.44
ackney Queensbridge 8.98 9 0.01 6.96 9.97 4.91
Hackney Springfield 6.87 31 0.05 2.22 2.22 6.17
" -ackney Stoke Newington Central 3.76 142 0.23 2.37 1.58 4.43
3ckney Victoria 6.24 53 0.08 6.17 6.01 9.49
Hackney Wick 7.18 24 0.04 17.41 16.77 16.14 Hammersmith and Fulham Addison 1.83 245 0.39 21.99 13.29 25.47 3mmersmith and Fulham Askew 2.62 190 0.30 5.38 5.22 3.48 .ammersmith and Fulham Avonmore and Brook Green 1.43 282 0.45 1.58 2.69 1.58 Hammersmith and Fulham College Park and Old Oak 6.06 58 0.09 4.59 5.06 7.59 a mmersmith and Fulham Fulham Broadway 1.77 249 0.40 20.89 20.25 19.46 immersmith and Fulham Fulham Reach 0.91 335 0.54 7.28 8.39 2.69 Hammersmith and Fulham Hammersmith Broadway 2.73 182 0.29 3.96 4.43 4.59 " immersmith and Fulham Munster 0.14 456 0.73 40.19 38.13 40.19 immersmith and Fulham North End 2.35 200 0.32 38.92 37.97 35.13 Hammersmith and Fulham Palace Riverside 0.00 42.25 54.91 Hammersmith and Fulham Parsons Green and Walham 0.00 486 0.78 11.08 24.21 15.98 immersmith and Fulham Ravenscourt Park 0.68 367 0.59 33.54 28.48 16.77 i lammersmith and Fulham Sands End 1.07 315 0.50 49.21 49.37 Hammersmith and Fulham Shepherd's Bush Green 3.67 146 0.23 30.85 27.06 33.07 I tmmersmith and Fulham Town 0.81 349 0.56 81.96 81.96 I immersmith and Fulham Wormholt and White City 5.33 79 0.13 33.70 47.78
Haringey Alexandra 0.00 75.63 74.68
I iringey Bounds Green 2.72 185 0.30 65.66 46.20 30.85
I iringey Bruce Grove 4.74 99 0.16 51.90 48.42 42.41
Haringey Crouch End 0.22 442 0.71 44.30 45.73 50.95
Haringey Fortis Green 0.00 23.42 21.99 17.41
I iringey Harringay 1.50 278 0.44 53.64 47.63 51.74
Haringey Highgate 0.07 469 0.75 11.23 8.70 17.72
Haringey Hornsey 1.69 256 0.41 93.04 96.36
F iringey Muswell Hill 0.00 27.06 35.28 32.59
I ._ringey Noel Park 5.02 89 0.14 15.51 18.20 20.09
Haringey Northumberland Park 9.08 8 0.01 78.48 53.64 65.82
I ,ringey Seven Sisters 4.47 108 0.17 73.10 72.63
I ringey St Ann's 4.87 94 0.15 45.89 69.62
Haringey Stroud Green 1.50 279 0.45 69.78 69.94
F' - ringey Tottenham Green 6.83 33 0.05 32.59 34.65 39.24
ringey Tottenham Hale 6.60 41 0.07 77.06 79.59
Haringey West Green 4.32 115 0.18 12.66 12.66 14.24
Haringey White Hart Lane 8.07 15 0.02 0.79 1.11 2.22
I ringey Woodside 2.90 176 0.28 15.98 14.40 12.03
,,,rrow Belmont 0.00 20.73 12.18 19.78
Harrow Canons 0.90 337 0.54 48.42 61.55
F rrow Edgware 0.25 436 0.70 5.54 4.75 10.60
F rrow Greenhill 0.25 435 0.69 5.22 9.81 8.07
Harrow Harrow on the Hill 0.00 17.09 12.82 11.23
F rrow Harrow Weald 0.88 342 0.55 1.90 3.16 6.01
89 I rough Ward Score Rank Percentile Deg.% Ext.% I nt.% Harrow Hatch End 0.03 478 0.76 29.43 25.63 31.96 ' irrow Headstone North 0.00 98.26 97.15 I irrow Headstone South 0.00 54.59 44.15 49.68 Harrow Kenton East 0.96 323 0.52 64.40 40.03 54.27 Harrow Kenton West 0.00 66.14 66.14 I trrow Marlborough 0.00 69.62 46.36 46.52 Farrow Pinner 0.00 64.08 63.77 Harrow Pinner South 0.00 74.68 59.81 I trrow Queensbury 0.23 440 0.70 96.36 94.94 I trrow Rayners Lane 0.00 98.89 98.89 Harrow Roxbourne 0.76 356 0.57 56.80 68.04 I frow Roxeth 0.00 95.73 97.63 I irrow Stanmore Park 0.95 326 0.52 80.54 84.65 Harrow Wealdstone 0.62 376 0.60 71.04 75.16 Harrow West Harrow 0.00 90.98 40.66 74.21 F vering Brooklands 1.52 277 0.44 77.22 89.08 Havering Cranham 0.72 362 0.58 97.78 97.78 Havering Elm Park 2.32 203 0.32 39.87 41.77 41.46 F ivering Emerson Park 0.54 391 0.62 89.24 93.20 F ._.vering Gooshays 6.01 60 0.10 63.45 72.15 Havering Hacton 1.35 289 0.46 68.04 73.26 F vering Harold Wood 1.67 260 0.42 90.19 93.51 F vering Havering Park 2.53 195 0.31 53.01 53.01 22.31 Havering Heaton 6.05 59 0.09 65.82 59.34 I'vering Hylands 0.61 377 0.60 39.08 49.68 37.18 F vering Mawneys 2.07 222 0.35 77.85 86.08 Havering Pettits 0.90 336 0.54 11.87 13.45 15.82 Havering Rainham and Wennington 1.93 232 0.37 55.06 49.05 60.60 F vering Romford Town 0.47 401 0.64 49.68 63.13 F _vering South Hornchurch 2.75 181 0.29 41.46 58.07 Havering Squirrel's Heath 0.35 420 0.67 16.93 18.67 19.15 F vering St Andrew's 1.70 255 0.41 66.77 48.58 38.45 F vering Upminster 0.15 451 0.72 48.58 67.09 Hillingdon Barnhill 0.59 380 0.61 58.54 36.39 25.95 F ' Botwell 1.45 280 0.45 46.20 77.85 F lingdon Brunel 0.41 415 0.66 67.56 39.08 44.30 Hillingdon Cavendish 0.00 35.28 51.27 44.62 Hillingdon Charville 0.48 399 0.64 74.37 79.43 F lingdon Eastcote and East Ruislip 0.00 51.11 52.85 49.84 F ittlingdon Harefield 0.94 330 0.53 83.07 50.32 72.31 Hillingdon Heathrow Villages 0.50 396 0.63 60.13 70.09 F iingdon Hillingdon East 0.35 421 0.67 42.88 61.23 F lingdon Ickenham 0.00 65.19 85.92 Hillingdon Manor 0.00 91.77 94.46 F - 'ingdon Northwood 0.00 76.42 89.87 F 'ingdon Northwood Hills 0.21 444 0.71 95.57 92.88 Hillingdon Pinkwell 0.73 360 0.58 58.39 85.28 Hillingdon South Ruislip 0.09 466 0.74 62.34 44.94 65.51 F lingdon Townfield 2.29 207 0.33 78.80 81.01 Hiliingdon Uxbridge North 0.00 96.52 96.99 Hillingdon Uxbridge South 0.00 94.15 90.98 F ingdon West Drayton 2.20 213 0.34 83.54 86.71 F :ingdon West Ruislip 0.00 78.32 88.92 Hillingdon Yeading 0.88 341 0.54 56.96 46.04 58.54 F iingdon Yiewsley 0.94 331 0.53 77.37 84.81
90 1 trough Ward Score Rank Percentile Deg.% Ext.% Int.°/0 Hounslow Bedfont 1.56 269 0.43 32.91 34.18 35.44 I ,unslow Brentford 1.18 301 0.48 79.27 46.52 65.98
I ► unslow Chiswick Homefields 0.00 52.85 35.92 17.25 Hounslow Chiswick Riverside 0.00 38.45 48.10 49.53 Cranford 1.94 231 0.37 79.75 76.90 I )unslow Feltham North 1.85 241 0.38 45.73 58.70 Hounslow Feltham West 1.18 300 0.48 51.58 47.31 64.08 Hounslow Hanworth 2.56 193 0.31 43.83 48.73 )unslow Hanworth Park 0.78 355 0.57 40.35 56.80 1..Junslow Heston Central 0.70 364 0.58 92.72 90.35 Hounslow Heston East 0.27 431 0.69 78.96 71.36 I ,unslow Heston West 1.84 244 0.39 37.97 57.44 I )unslow Hounslow Central 0.47 402 0.64 47.78 64.72 Hounslow Hounslow Heath 0.78 354 0.57 45.41 44.62 39.08 F'-)unslow Hounslow South 0.00 28.48 44.30 46.68 I unslow Hounslow West 1.53 275 0.44 57.44 58.86 Hounslow Isleworth 1.25 297 0.47 65.51 76.42 Hounslow Osterley and Spring Grove 0.00 71.52 40.82 67.41 I lunslow Syon 1.04 317 0.51 35.60 30.85 44.15 I -unslow Turnham Green 0.02 480 0.77 62.50 64.40 Islington Barnsbury 2.71 186 0.30 56.17 42.41 54.59 I ngton Bunhill 5.23 82 0.13 99.37 40.98 98.73 I ngton Caledonian 5.13 86 0.14 46.84 29.91 46.99 Islington Canonbury 5.10 87 0.14 36.23 45.25 48.26 I "ngton Clerkenwell 2.67 188 0.30 98.10 43.35 97.47 I ngton Finsbury Park 5.58 71 0.11 47.94 42.09 65.66 Islington Highbury East 1.63 266 0.42 75.16 50.95 70.89 Iclington Highbury West 2.75 180 0.29 24.21 14.08 21.84 I ngton Hillrise 4.33 114 0.18 11.55 10.76 8.70 10,ington Holloway 6.30 49 0.08 12.97 11.71 16.61 Islington Junction 4.27 119 0.19 10.76 9.49 11.39 I ngton Mildmay 4.05 130 0.21 26.42 46.68 38.92 I ngton St George's 4.16 125 0.20 10.28 10.92 7.75 Islington St Mary's 3.39 159 0.25 38.77 35.60 21.04 I ngton St Peter's 3.56 152 0.24 27.85 22.78 20.73 I ngton Tollington 4.50 107 0.17 15.35 15.51 7.28 Kensington and Chelsea Abingdon 1.15 307 0.49 8.07 6.96 9.02 1.(,=.nsington and Chelsea Brompton 0.81 350 0.56 18.51 12.97 9.34 nsington and Chelsea Campden 0.52 393 0.63 16.77 14.24 20.41 1.\nsington and Chelsea Colville 4.83 98 0.16 18.67 12.34 8.54 Kensington and Chelsea Courtfield 1.67 259 0.41 22.63 16.46 21.36 nsington and Chelsea Cremorne 3.67 147 0.23 19.78 17.25 11.71 _nsington and Chelsea Earl's Court 3.30 162 0.26 14.40 16.14 11.08 Kensington and Chelsea Golborne 10.96 2 0.00 50.63 45.89 47.94 nsington and Chelsea Hans Town 1.01 319 0.51 57.28 49.21 46.20 nsington and Chelsea Holland 0.65 372 0.59 73.42 42.88 55.38 Kensington and Chelsea Norland 1.31 291 0.46 19.15 19.62 27.85 K - nsington and Chelsea Notting Barns 7.14 25 0.04 40.98 35.76 28.80 nsington and Chelsea Pembridge 1.79 248 0.40 23.89 20.57 19.30 Kensington and Chelsea Queen's Gate 0.92 333 0.53 24.53 23.42 29.27 Kensington and Chelsea Redcliffe 0.89 339 0.54 0.32 0.32 1.74 nsington and Chelsea Royal Hospital 0.48 400 0.64 47.31 37.50 41.93 I, _nsington and Chelsea St Charles 8.03 16 0.03 64.24 33.54 71.84 Kensington and Chelsea Stanley 0.56 386 0.62 34.97 22.94 23.58 igston upon Thames Alexandra 0.00 3.64 4.59 0.79
91 E rough Ward Score Rank Percentile Deg.% Ext.% Int. 'Yo Kingston upon Thames Berrylands 0.00 42.09 45.57 46.84 igston upon Thames Beverley 0.00 66.61 83.23 igston upon Thames Canbury 0.00 56.49 62.34 Kingston upon Thames Chessington North and Hook 0.54 390 0.62 53.16 42.25 34.02 Kingston upon Thames Chessington South 0.08 467 0.75 3.16 3.80 3.80 igston upon Thames Coombe Hill 0.00 67.25 75.95 Kit igston upon Thames Coombe Vale 0.00 97.47 98.26 Kingston upon Thames Grove 0.00 63.61 34.34 40.35 Igston upon Thames Norbiton 0.01 481 0.77 90.82 94.62 igston upon Thames Old Malden 0.00 98.42 96.04 Kingston upon Thames St James 0.00 73.58 79.27 gston upon Thames St Mark's 0.17 449 0.72 91.93 95.09 igston upon Thames Surbiton Hill 0.00 93.20 92.56 Kingston upon Thames Tolworth and Hook Rise 0.07 471 0.75 96.99 97.31 Kingston upon Thames Tudor 0.00 86.55 91.30 L -nbeth Bishop's 1.82 246 0.39 54.43 61.39 Lambeth Brixton Hill 2.32 202 0.32 95.89 96.52 Lambeth Clapham Common 0.04 476 0.76 81.80 39.87 77.06 L -nbeth Clapham Town 1.19 298 0.48 80.70 80.38 L_nbeth Coldharbour 5.42 76 0.12 88.61 87.97 Lambeth Ferndale 2.95 172 0.27 89.40 88.77 L nbeth Gipsy Hill 2.04 224 0.36 98.73 97.94 L nbeth Herne Hill 1.91 234 0.37 34.81 46.84 50.79 Lambeth Knight's Hill 2.04 223 0.36 31.96 25.47 33.39 L -mbeth Larkhall 3.01 170 0.27 67.09 63.92 nbeth Oval 2.32 204 0.33 46.36 51.27 Lambeth Prince's 4.26 120 0.19 8.70 11.39 13.29 Lambeth St Leonard's 1.02 318 0.51 29.91 21.20 36.23 L -nbeth Stockwell 4.07 129 0.21 31.33 28.32 29.91 Streatham Hill 0.73 361 0.58 37.18 29.75 20.25 Lambeth Streatham South 0.33 424 0.68 34.02 31.49 31.01 -nbeth Streatham Wells 1.43 281 0.45 26.58 38.61 25.79 L -nbeth Thornton 1.06 316 0.50 28.01 24.53 19.94 Lambeth Thurlow Park 0.22 441 0.70 15.19 15.35 13.77 L -nbeth Tulse Hill 3.65 148 0.24 60.92 83.70 L -nbeth Vassall 4.64 104 0.17 18.83 16.30 29.43 Lewisham Bellingham 4.10 127 0.20 55.85 59.18 L Pwisham Blackheath 0.14 455 0.73 66.93 60.92 L Nisham Brockley 2.20 214 0.34 46.68 57.59 Lewisham Catford South 0.06 472 0.75 43.04 53.96 41.30 Lewisham Crofton Park 0.24 438 0.70 62.18 63.29 L Nisham Downham 4.35 111 0.18 21.04 21.68 26.27 L Nisham Evelyn 5.91 64 0.10 13.92 12.03 23.89 Lewisham Forest Hill 0.74 359 0.57 21.68 25.32 22.78 L Nisham Grove Park 0.95 324 0.52 57.12 56.80 34.65 L Nisham Ladywell 0.40 417 0.67 35.44 55.38 51.11 Lewisham Lee Green 0.11 463 0.74 84.65 81.65 a.wisham Lewisham Central 2.25 210 0.34 78.16 81.80 L Nisham New Cross 4.09 128 0.20 21.20 22.63 28.48 Lewisham Perry Vale 0.62 374 0.60 8.23 6.49 15.03 Lewisham Rushey Green 1.86 239 0.38 54.75 56.49 43.20 L Nisham Sydenham 2.14 217 0.35 50.16 53.32 L _ Nisham Telegraph Hill 2.73 183 0.29 76.11 85.44 Lewisham Whitefoot 3.12 167 0.27 63.77 58.39 fn !don Abbey 0.00 34.34 28.16 27.37
92 E rough Ward Score Rank Percentile Deg.% Ext.% I nt.% Merton Cannon Hill 0.00 18.99 19.78 18.04 1` 4 "rton Colliers Wood 0.00 53.32 39.56 44.78 :rton Cricket Green 2.93 174 0.28 43.67 31.33 39.56 Merton Dundonald 0.00 33.86 57.75 38.77 Merton Figge's Marsh 1.11 311 0.50 27.69 25.79 28.01 :rton Graveney 0.00 29.75 37.82 34.97 N.,,rton Hillside 0.00 80.06 - 70.25 Merton Lavender Fields 0.56 387 0.62 90.35 - 90.51 I :rton Longthornton 0.00 81.49 - 87.50 :rton Lower Morden 0.42 412 0.66 25.63 32.75 31.17 Merton Merton Park 0.00 99.68 - 99.68 " -, rton Pollards Hill 0.75 357 0.57 42.56 - 55.22 !don Ravensbury 1.98 228 0.36 89.72 95.57 Merton Raynes Park 0.00 91.46 87.34 Merton St Helier 2.53 194 0.31 51.27 60.76 :rton Trinity 0.00 79.11 90.19 F' ,rton Village 0.00 82.28 95.41 Merton West Barnes 0.00 99.21 99.21 :rton Wimbledon Park 0.00 55.38 77.37 r wham Beckton 3.78 141 0.23 45.25 66.61 Newham Boleyn 4.85 96 0.15 83.86 84.02 r' wham Canning Town North 9.37 6 0.01 44.94 - 63.61 I` wham Canning Town South 9.58 4 0.01 82.59 - 80.70 Newham Custom House 5.54 73 0.12 98.58 - 96.20 NIPwham East Ham Central 4.40 110 0.18 99.53 - 99.05 wham East Ham North 4.27 118 0.19 97.31 - 94.30 Newham East Ham South 4.95 93 0.15 20.25 18.83 15.66 Newham Forest Gate North 3.64 149 0.24 15.66 17.41 3.64 r wham Forest Gate South 5.25 81 0.13 1.11 1.42 0.32 Is wham Green Street East 5.18 85 0.14 0.63 0.47 1.90 Newham Green Street West 5.81 68 0.11 9.97 11.23 12.18 1' wham Little Ilford 6.81 35 0.06 22.78 41.61 35.28 r wham Manor Park 5.37 77 0.12 21.52 24.68 26.11 Newham Plaistow North 6.29 50 0.08 15.03 19.94 8.86 NI-wham Plaistow South 4.67 102 0.16 26.11 26.11 34.49 wham Royal Docks 4.84 97 0.15 16.30 23.26 18.51 Newham Stratford and New Town 6.13 57 0.09 16.61 26.74 34.81 Newham Wall End 3.50 155 0.25 12.50 26.27 33.70 I\ wham West Ham 5.89 66 0.11 7.75 7.28 17.56 F...dbridge Aldborough 0.93 332 0.53 17.25 23.58 24.21 Redbridge Barkingside 0.42 411 0.66 10.13 11.08 16.30 F dbridge Bridge 0.42 413 0.66 20.57 21.36 28.96 F dbridge Chadwell 0.29 426 0.68 8.39 5.70 15.19 Redbridge Church End 0.00 9.81 9.02 5.85 P - dbridge Clayhall 0.12 461 0.74 26.90 27.53 35.76 F dbridge Clementswood 2.71 187 0.30 10.92 8.86 10.76 Redbridge Cranbrook 0.65 371 0.59 74.53 - 75.63 Redbridge Fairlop 1.12 310 0.50 70.09 - 71.20 F dbridge Fullwell 2.14 216 0.35 64.87 69.30 F-dbridge Goodmayes 0.24 439 0.70 71.99 - 67.72 Redbridge Hainault 5.68 70 0.11 86.71 - 80.54 F dbridge Loxford 2.82 179 0.29 78.64 43.04 73.58 F dbridge Mayfield 1.17 302 0.48 33.39 40.19 31.80 Redbridge Monkhams 0.00 75.32 - 76.11 P dbridge Newbury 0.12 459 0.73 59.65 44.78 71.99
93 L.,rough Ward Score Rank Percentile Deg.% Ext. % I n t. Redbridge Roding 0.00 49.05 53.96 dbridge Seven Kings 0.60 378 0.60 71.68 78.16
, dbridge Snaresbrook 0.91 334 0.53 18.35 18.51 30.38 Redbridge Valentines 1.60 267 0.43 29.59 21.52 12.66 P dbridge Wanstead 0.00 61.87 42.72 60.28 I Thmond upon Thames Barnes 0.00 87.50 89.72 Richmond upon Thames East Sheen 0.00 73.73 84.97 Richmond upon Thames Fulwell and Hampton Hill 0.00 66.30 34.49 12.34 [ ;hmond upon Thames Ham Petersham and Richmond Riverside 0.59 382 0.61 65.98 69.46 1,,,Mmond upon Thames Hampton 0.00 59.02 51.42 31.49 Richmond upon Thames Hampton North 0.00 44.78 59.97 Thmond upon Thames Hampton Wick 0.00 72.78 47.94 39.72 Mmond upon Thames Heathfield 0.47 404 0.65 83.70 43.20 64.24 Richmond upon Thames Kew 0.00
-,hmond upon Thames Mortlake and Barnes Common 0.00 96.68 96.84 F tmond upon Thames North Richmond 0.00 62.97 54.75 Richmond upon Thames South Richmond 0.00 92.25 81.49 Richmond upon Thames South Twickenham 0.00 68.51 41.30 62.82 [ -,hmond upon Thames St Margarets and North Twickenham 0.00 82.91 83.39 F.,,;hmond upon Thames Teddington 0.00 64.72 40.35 54.43 Richmond upon Thames Twickenham Riverside 0.00 96.04 96.68 F ;hmond upon Thames West Twickenham 0.00 94.78 93.99 F ;hmond upon Thames Whitton 0.00 86.23 82.12 Southwark Brunswick Park 3.80 140 0.22 93.67 92.72 F uthwark Camberwell Green 6.66 40 0.06 97.63 95.73 uthwark Cathedrals 2.21 212 0.34 99.84 99.84 Southwark Chaucer 2.57 192 0.31 94.62 83.86 S' uthwark College 0.75 358 0.57 93.99 90.66 uthwark East Dulwich 0.00 92.88 90.82 Suuthwark East Walworth 5.97 63 0.10 89.08 88.61 Southwark Faraday 6.70 38 0.06 19.46 19.15 25.63 uthwark Grange 4.51 105 0.17 7.44 7.91 9.65 _uthwark Livesey 8.63 11 0.02 29.27 51.11 25.16 Southwark Newington 5.26 80 0.13 27.53 36.71 38.61 uthwark Nunhead 7.12 29 0.05 42.72 34.97 42.25 uthwark Peckham 7.12 28 0.04 81.33 75.79 Southwark Peckham Rye 0.82 347 0.55 10.44 9.18 11.87 F-uthwark Riverside 1.82 247 0.39 7.12 6.65 11.55 uthwark Rotherhithe 1.89 236 0.38 12.18 12.50 14.08 Southwark South Bermondsey 4.34 112 0.18 1.74 1.74 5.54 Southwark South Camberwell 1.86 240 0.38 13.77 15.98 21.52 uthwark Surrey Docks 0.26 434 0.69 5.85 8.23 5.70 ..uthwark The Lane 4.03 131 0.21 4.11 3.64 12.82 Southwark Village 0.00 52.53 48.89 48.10 tton Beddington North 0.08 468 0.75 25.95 17.72 24.37 tton Beddington South 0.98 321 0.51 28.16 29.43 37.50 Sutton Belmont 0.00 14.72 23.10 27.06 tton Carshalton Central 0.00 35.92 28.80 36.08 tton Carshalton South and Clockhouse 0.00 60.28 59.65 Sutton Cheam 0.00 22.47 26.58 33.86 S tton Nonsuch 0.00 90.03 77.69 tton St Helier 3.18 164 0.26 86.39 89.24 Sutton Stonecot 0.26 433 0.69 41.14 41.93 41.61 Sutton Sutton Central 0.00 83.23 65.19 tton Sutton North 0.45 407 0.65 93.83 93.04
94 E rough Ward Score Rank Percentile Deg.% Ext.% Int.°/0 Sutton Sutton South 1.15 306 0.49 88.45 75.00 tton Sutton West 0.00 88.29 40.51 78.48 tton The Wrythe 0.50 395 0.63 86.87 87.82 Sutton Wallington North 0.00 32.12 43.67 50.47 Si ton Wallington South 0.45 408 0.65 92.09 94.15 tton Wandle Valley 1.70 254 0.41 67.88 62.66 Sutton Worcester Park 0.00 71.20 41.46 62.50 Tower Hamlets Bethnal Green North 6.45 46 0.07 61.71 49.37 45.73
- wer Hamlets Bethnal Green South 7.13 26 0.04 95.41 95.89 wer Hamlets Blackwall and Cubitt Town 2.21 211 0.34 72.63 81.17 Tower Hamlets Bow East 7.13 27 0.04 82.44 91.93 wer Hamlets Bow West 3.14 166 0.27 68.83 66.30 wer Hamlets Bromley-by-Bow 9.45 5 0.01 37.82 61.71 Tower Hamlets East India and Lansbury 10.16 3 0.00 93.35 93.83 Tower Hamlets Limehouse 6.00 61 0.10 6.80 4.91 10.13 7 wer Hamlets Mile End and Globe Town 6.14 55 0.09 4.43 3.01 8.39
-lower Hamlets Mile End East 7.57 20 0.03 27.22 20.41 18.99 Tower Hamlets Millwall 0.98 322 0.51 4.27 4.11 4.27
- wer Hamlets Shadwell 6.72 37 0.06 26.27 24.37 6.96 _wer Hamlets Spitalfields and Banglatown 6.52 43 0.07 0.95 0.79 2.37 Tower Hamlets St Dunstan's and Stepney Green 8.39 12 0.02 0.47 0.63 0.16 wer Hamlets St Katherine's and Wapping 1.55 270 0.43 8.86 7.12 6.80 wer Hamlets Weavers 8.29 13 0.02 9.49 7.75 9.81 Tower Hamlets Whitechapel 5.54 72 0.12 3.48 3.48 3.01 \A 1-31tham Forest Cann Hall 3.31 161 0.26 44.46 31.65 29.11 aitham Forest Cathall 4.29 116 0.19 5.06 2.37 4.11 Waltham Forest Chapel End 0.50 397 0.63 6.65 7.44 8.23 Waltham Forest Chingford Green 2.07 221 0.35 2.69 3.32 3.16 altham Forest Endlebury 1.10 313 0.50 36.87 29.59 28.16 \,. altham Forest Forest 1.37 288 0.46 3.32 4.27 2.53 Waltham Forest Grove Green 0.94 327 0.52 11.39 9.34 16.46 \ 31tham Forest Hale End and Highams Park 0.36 419 0.67 25.32 19.30 20.89 \ altham Forest Hatch Lane 2.73 184 0.29 20.41 27.37 28.32 Waltham Forest High Street 0.41 414 0.66 70.41 81.33 31tham Forest Higham Hill 3.56 151 0.24 41.77 32.59 13.92 31tham Forest Hoe Street 3.38 160 0.26 56.65 42.56 37.66 Waltham Forest Larkswood 1.95 230 0.37 50.79 64.87 \N9Itham Forest Lea Bridge 2.30 206 0.33 54.27 71.04 31tham Forest Leyton 4.28 117 0.19 69.46 69.78 \iv altham Forest Leytonstone 0.44 409 0.65 39.24 53.48 Waltham Forest Markhouse 2.57 191 0.31 55.70 36.23 47.63 31tham Forest Valley 1.65 264 0.42 23.58 18.99 32.44 \ 31tham Forest William Morris 0.84 345 0.55 29.11 29.11 9.18 Waltham Forest Wood Street 3.43 158 0.25 43.99 33.39 30.22 3ndsworth Balham 0.00 37.03 36.08 34.18 3ndsworth Bedford 0.33 423 0.68 17.56 15.19 14.87 Wandsworth Earlsfield 0.00 59.97 64.56 W ndsworth East Putney 0.06 474 0.76 31.65 33.07 36.39 3ndsworth Fairfield 0.00 46.04 53.80 \iv andsworth Furzedown 0.00 50.95 47.15 51.90 Wandsworth Graveney 0.34 422 0.67 27.37 22.31 36.87 3ndsworth Latchmere 3.84 138 0.22 87.18 78.64 3ndsworth Nightingale 0.13 457 0.73 69.30 66.77 Wandsworth Northcote 0.00 73.26 73.89 \ andsworth Queenstown 1.41 284 0.45 80.22 82.44
95 E rough Ward Score Rank Percentile Deg.% Ext.`)/0 Int.% Wandsworth Roehampton 3.90 135 0.22 94.30 89.40 ' 3ndsworth Shaftesbury 0.00 59.49 30.22 15.51 \ indsworth Southfields 0.00 60.60 54.11 17.88 Wandsworth St Mary's Park 0.10 465 0.74 17.72 16.93 21.99 \N9ndsworth Thamesfield 0.00 65.35 39.40 5.06 lndsworth Tooting 0.17 448 0.72 95.25 92.09 \ig andsworth Wandsworth Common 0.00 37.66 56.01 39.87 Wandsworth West Hill 0.10 464 0.74 16.14 14.87 23.26 indsworth West Putney 0.15 452 0.72 79.59 73.73 ;stminster Abbey Road 0.47 403 0.64 86.08 74.53 Westminster Bayswater 1.87 238 0.38 58.86 39.24 35.92 )stminster Bryanston and Dorset Square 1.39 287 0.46 97.15 93.67 ;stminster Church Street 11.28 1 0.00 68.35 53.48 59.49 Westminster Churchill 4.41 109 0.17 94.94 82.59 Westminster Harrow Road 6.18 54 0.09 66.46 74.84 stminster Hyde Park 1.72 251 0.40 63.13 54.75 55.06 At estminster Knightsbridge and Belgravia 0.00 68.20 66.46 Westminster Lancaster Gate 2.31 205 0.33 39.72 45.41 60.44 ;stminster Little Venice 0.95 325 0.52 49.53 78.80 )stminster Maida Vale 0.45 405 0.65 0.16 0.16 0.63 Westminster Marylebone High Street 1.13 309 0.49 17.88 18.04 20.57 )stminster Queen's Park 9.10 7 0.01 7.91 10.60 14.56 )stminster Regent's Park 1.31 290 0.46 45.09 56.96 Westminster St James's 1.71 253 0.40 97.94 98.58 W2.stminster Tachbrook 1.91 233 0.37 32.75 57.28 42.88 )stminster Vincent Square 2.14 218 0.35 50.00 31.80 40.51 estminster Warwick 1.30 292 0.47 56.01 47.78 55.85 Westminster West End 1.16 305 0.49 60.76 80.22 )stminster Westbourne 7.45 21 0.03 1.27 0.95 2.85
96 Appendix B - Combined ratings for output areas in Merton
Combined ratings for the census index of output areas in Merton
Ward Output Area Total Score Rank Percentile
Abbey 00BAFX0001 0.64 14923 61.82 Abbey 00BAFX0002 0.51 15717 65.11 Abbey 00BAFX0003 0.00 22288 92.33 Abbey 00BAFX0004 0.00 22276 92.28 Abbey 00BAFX0005 0.00 22285 92.32 Abbey 00BAFX0006 0.00 22290 92.34 Abbey 00BAFX0007 0.00 22273 92.27 Abbey 00BAFX0008 0.05 18932 78.43 Abbey 00BAFX0009 0.00 22279 92.29 Abbey 00BAFX0010 0.00 22280 92.29 Abbey 00BAFX0011 0.72 14509 60.10 Abbey 00BAFX0012 0.00 22283 92.31 Abbey 00BAFX0013 1.06 12867 53.30 Abbey 00BAFX0014 3.50 5370 22.25 Abbey 00BAFX0015 0.18 17995 74.54 Abbey 00BAFX0016 0.00 22281 92.30 Abbey 00BAFX0017 0.20 17822 73.83 Abbey 00BAFX0018 0.00 22291 92.34 Abbey 00BAFX0019 1.58 10742 44.50 Abbey 00BAFX0020 0.00 22289 92.33 Abbey 00BAFX0021 2.41 8037 33.29 Abbey 00BAFX0022 2.35 8194 33.94 Abbey 00BAFX0023 0.00 22287 92.32 Abbey 00BAFX0024 0.00 22282 92.30 Abbey 00BAFX0025 2.96 6608 27.37 Abbey 00BAFX0026 0.00 22274 92.27 Abbey 00BAFX0027 4.08 4241 17.57 Abbey 00BAFX0028 0.00 22284 92.31 Abbey 00BAFX0029 0.00 22275 92.27 Abbey 00BAFX0030 0.00 22277 92.28 Abbey 00BAFX0031 0.15 18220 75.48 Abbey 00BAFX0032 0.00 22278 92.29 Abbey 00BAFX0033 0.00 22286 92.32 Abbey 00BAFX0034 0.17 18035 74.71 Abbey 00BAFX0035 0.03 19108 79.15 Abbey 00BAFX0036 0.00 19342 80.12
Cannon Hill 00BAFY0001 0.00 20526 85.03 Cannon Hill 00BAFY0002 0.00 20524 85.02 Cannon Hill 00BAFY0003 0.03 19088 79.07 Cannon Hill 00BAFY0004 0.00 20525 85.02 Cannon Hill 00BAFY0005 0.29 17231 71.38 Cannon Hill 00BAFY0006 0.00 20529 85.04 Cannon Hill 00BAFY0007 0.47 15937 66.02 Cannon Hill 00BAFY0008 0.33 16961 70.26 Cannon Hill 00BAFY0009 0.38 16584 68.70 Cannon Hill 00BAFY0010 2.60 7569 31.35
97 Ward Output Area Total Score Rank Percentile Cannon Hill 00BAFY0011 0.00 20528 85.04 Cannon Hill 00BAFY0012 0.00 20530 85.05 Cannon Hill 00BAFY0013 2.02 9226 38.22 Cannon Hill 00BAFY0014 0.17 18087 74.93 Cannon Hill 00BAFY0015 0.11 18500 76.64 Cannon Hill 00BAFY0016 0.00 20523 85.02 Cannon Hill 00BAFY0017 0.15 18167 75.26 Cannon Hill 00BAFY0018 0.33 16931 70.14 Cannon Hill 00BAFY0019 1.56 10801 44.74 Cannon Hill 00BAFY0020 0.01 19266 79.81 Cannon Hill 00BAFY0021 0.17 18066 74.84 Cannon Hill 00BAFY0022 0.00 20522 85.01 Cannon Hill 00BAFY0023 0.19 17923 74.25 Cannon Hill 00BAFY0024 0.15 18207 75.42 Cannon Hill 00BAFY0025 0.01 19241 79.71 Cannon Hill 00BAFY0026 0.02 19144 79.30 Cannon Hill 00BAFY0027 0.00 20527 85.03 Cannon Hill 00BAFY0028 1.38 11526 47.75
Colliers Wood 00BAFZ0001 0.34 16872 69.89 Colliers Wood 00BAFZ0002 0.00 22950 95.07 Colliers Wood 00BAFZ0003 0.31 17107 70.87 Colliers Wood 00BAFZ0004 0.01 19225 79.64 Colliers Wood 00BAFZ0005 0.26 17398 72.07 Colliers Wood 00BAFZ0006 0.00 22940 95.03 Colliers Wood 00BAFZ0007 0.00 22949 95.07 Colliers Wood 00BAFZ0008 1.20 12304 50.97 Colliers Wood 00BAFZ0009 1.50 11056 45.80 Colliers Wood 00BAFZ0010 0.43 16202 67.12 Colliers Wood 00BAFZ0011 0.13 18317 75.88 Colliers Wood 00BAFZ0012 0.11 18495 76.62 Colliers Wood 00BAFZ0013 0.00 22964 95.13 Colliers Wood 00BAFZ0014 0.00 22945 95.05 Colliers Wood 00BAFZ0015 0.00 22939 95.02 Colliers Wood 00BAFZ0016 0.43 16256 67.34 Colliers Wood 00BAFZ0017 0.00 22958 95.10 Colliers Wood 00BAFZ0018 0.00 22951 95.07 Colliers Wood 00BAFZ0019 0.00 22943 95.04 Colliers Wood 00BAFZ0020 0.00 22952 95.08 Colliers Wood 00BAFZ0021 0.30 17191 71.21 Colliers Wood 00BAFZ0022 2.00 9270 38.40 Colliers Wood 00BAFZ0023 2.57 7638 31.64 Colliers Wood 00BAFZ0024 1.54 10883 45.08 Colliers Wood 00BAFZ0025 0.30 17144 71.02 Colliers Wood 00BAFZ0026 0.29 17255 71.48 Colliers Wood 00BAFZ0027 0.90 13638 56.50 Colliers Wood 00BAFZ0028 2.33 8254 34.19 Colliers Wood 00BAFZ0029 0.62 15001 62.14 Colliers Wood 00BAFZ0030 0.08 18672 77.35 Colliers Wood 00BAFZ0031 1.24 12149 50.33 Colliers Wood 00BAFZ0032 0.00 22963 95.12
Cricket Green 00BAGA0001 0.73 14451 59.86 Cricket Green 00BAGA0002 1.95 9426 39.05
98 Ward Output Area Total Score Rank Percentile Cricket Green 00BAGA0003 2.64 7479 30.98 Cricket Green 00BAGA0004 1.41 11442 47.40 Cricket Green 00BAGA0005 0.98 13229 54.80 Cricket Green 00BAGA0006 1.22 12208 50.57 Cricket Green 00BAGA0007 2.64 7466 30.93 Cricket Green 00BAGA0008 1.76 10080 41.76 Cricket Green 00BAGA0009 1.51 11034 45.71 Cricket Green 00BAGA0010 3.21 6015 24.92 Cricket Green 00BAGA0011 1.71 10262 42.51 Cricket Green 00BAGA0012 5.11 2689 11.14 Cricket Green 00BAGA0013 3.56 5239 21.70 Cricket Green 00BAGA0014 1.41 11417 47.29 Cricket Green 00BAGA0015 1.25 12099 50.12 Cricket Green 00BAGA0016 2.69 7321 30.33 Cricket Green 00BAGA0017 4.07 4253 17.62 Cricket Green 00BAGA0018 5.44 2264 9.38 Cricket Green 00BAGA0019 4.72 3227 13.37 Cricket Green 00BAGA0020 4.61 3373 13.97 Cricket Green 00BAGA0021 3.41 5596 23.18 Cricket Green 00BAGA0022 4.69 3256 13.49 Cricket Green 00BAGA0023 3.68 4994 20.69 Cricket Green 00BAGA0024 1.31 11846 49.07 Cricket Green 00BAGA0025 4.45 3652 15.13 Cricket Green 00BAGA0026 4.79 3102 12.85 Cricket Green 00BAGA0027 4.55 3484 14.43 Cricket Green 00BAGA0028 3.39 5636 23.35 Cricket Green 00BAGA0029 1.60 10663 44.17 Cricket Green 00BAGA0030 1.23 12167 50.40 Cricket Green 00BAGA0031 1.13 12574 52.09 Cricket Green 00BAGA0032 3.15 6137 25.42 Cricket Green 00BAGA0033 7.86 420 1.74 Cricket Green 00BAGA0034 0.70 14585 60.42
Dundonald 00BAGB0001 0.00 19475 80.68 Dundonald 00BAGB0002 0.00 19479 80.69 Dundonald 00BAGB0003 0.00 19489 80.73 Dundonald 00BAGB0004 0.58 15254 63.19 Dundonald 00BAGB0005 0.00 19490 80.74 Dundonald 00BAGB0006 0.00 19497 80.77 Dundonald 00BAGB0007 0.00 19486 80.72 Dundonald 00BAGB0008 0.00 19500 80.78 Dundonald 00BAGB0009 0.00 19493 80.75 Dundonald 00BAGB0010 0.00 19499 80.77 Dundonald 00BAGB0011 0.19 17927 74.26 Dundonald 00BAGB0012 0.00 19487 80.72 Dundonald 00BAGB0013 0.00 19488 80.73 Dundonald 00BAGB0014 0.00 19476 80.68 Dundonald 00BAGB0015 0.00 19485 80.72 Dundonald 00BAGB0016 0.21 17793 73.71 Dundonald 00BAGB0017 0.00 19483 80.71 Dundonald 00BAGB0018 0.00 19494 80.75 Dundonald 00BAGB0019 0.00 19495 80.76 Dundonald 00BAGB0020 0.00 19492 80.75 Dundonald 00BAGB0021 0.00 19496 80.76
99 Ward Output Area Total Score Rank Percentile Dundonald 00BAGB0022 0.00 19482 80.70 Dundonald 00BAGB0023 0.00 19480 80.70 Dundonald 00BAGB0024 0.00 19484 80.71 Dundonald 00BAGB0025 0.00 19477 80.68 Dundonald 00BAGB0026 0.37 16668 69.05 Dundonald 00BAGB0027 0.00 19501 80.78 Dundonald 00BAGB0028 0.00 19478 80.69 Dundonald 00BAGB0029 0.13 18318 75.88 Dundonald 00BAGB0030 0.00 19481 80.70 Dundonald 00BAGB0031 0.40 16437 68.09 Dundonald 00BAGB0032 0.00 19491 80.74 Dundonald 00BAGB0033 0.13 18341 75.98 Dundonald 00BAGB0034 0.00 19498 80.77
Figge's Marsh 00BAGC0001 0.58 15246 63.16 Figge's Marsh 00BAGC0002 0.44 16183 67.04 Figge's Marsh 00BAGC0003 3.73 4872 20.18 Figge's Marsh 00BAGC0004 2.82 6964 28.85 Figge's Marsh 00BAGC0005 0.27 17349 71.87 Figge's Marsh 00BAGC0006 0.40 16426 68.04 Figge's Marsh 00BAGC0007 0.22 17702 73.33 Figge's Marsh 00BAGC0008 0.20 17888 74.10 Figge's Marsh 00BAGC0009 0.20 17889 74.11 Figge's Marsh 00BAGC0010 0.20 17887 74.10 Figge's Marsh 00BAGC0011 0.80 14106 58.43 Figge's Marsh 00BAGC0012 0.51 15731 65.17 Figge's Marsh 00BAGC0013 3.55 5270 21.83 Figge's Marsh 00BAGC0014 0.69 14683 60.82 Figge's Marsh 00BAGC0015 0.87 13785 57.10 Figge's Marsh 00BAGC0016 3.45 5513 22.84 Figge's Marsh 00BAGC0017 0.30 17173 71.14 Figge's Marsh 00BAGC0018 4.04 4322 17.90 Figge's Marsh 00BAGC0019 3.42 5573 23.09 Figge's Marsh 00BAGC0020 4.15 4131 17.11 Figge's Marsh 00BAGC0021 3.83 4699 19.47 Figge's Marsh 00BAGC0022 1.70 10307 42.70 Figge's Marsh 00BAGC0023 3.34 5721 23.70 Figge's Marsh 00BAGC0024 1.60 10660 44.16 Figge's Marsh 00BAGC0025 5.23 2535 10.50 Figge's Marsh 00BAGC0026 1.21 12224 50.64 Figge's Marsh 00BAGC0027 0.53 15568 64.49 Figge's Marsh 00BAGC0028 0.26 17396 72.06 Figge's Marsh 00BAGC0029 1.44 11323 46.91 Figge's Marsh 00BAGC0030 1.68 10382 43.01 Figge's Marsh 00BAGC0031 1.39 11502 47.65 Figge's Marsh 00BAGC0032 0.89 13654 56.56
Graveney 00BAGD0001 0.05 18882 78.22 Graveney 00BAGD0002 0.00 21974 91.03 Graveney 00BAGD0003 0.00 21969 91.01 Graveney 00BAGD0004 0.32 16991 70.39 Graveney 00BAGD0005 0.06 18851 78.09 Graveney 00BAGD0006 0.00 21962 90.98 Graveney 00BAGD0007 1.59 10703 44.34
100 Ward Output Area Total Score Rank Percentile Graveney 00BAGD0008 0.00 21943 90.90 Graveney 00BAGD0009 1.20 12296 50.94 Graveney 00BAGD0010 0.85 13886 57.52 Graveney 00BAGD0011 0.01 19267 79.81 Graveney 00BAGD0012 0.84 13925 57.68 Graveney 00BAGD0013 0.25 17507 72.52 Graveney 00BAGD0014 0.00 21961 90.97 Graveney 00BAGD0015 0.30 17166 71.11 Graveney 00BAGD0016 0.36 16738 69.34 Graveney 00BAGD0017 0.60 15130 62.68 Graveney 00BAGD0018 0.27 17375 71.98 Graveney 00BAGD0019 0.00 21968 91.00 Graveney 00BAGD0020 0.92 13506 55.95 Graveney 00BAGD0021 0.29 17237 71.40 Graveney 00BAGD0022 0.00 21965 90.99 Graveney 00BAGD0023 1.36 11626 48.16 Graveney 00BAGD0024 0.18 18009 74.60 Graveney 00BAGD0025 0.61 15069 62.42 Graveney 00BAGD0026 0.33 16918 70.08 Graveney 00BAGD0027 0.00 21973 91.02 Graveney 00BAGD0028 0.43 16248 67.31
Hillside 00BAGE0001 0.52 15679 64.95 Hillside 00BAGE0002 0.14 18267 75.67 Hillside 00BAGE0003 0.00 19406 80.39 Hillside 00BAGE0004 0.00 19408 80.40 Hillside 00BAGE0005 0.00 19409 80.40 Hillside 00BAGE0006 1.20 12274 50.85 Hillside 00BAGE0007 0.00 19407 80.39 Hillside 00BAGE0008 0.00 19417 80.43 Hillside 00BAGE0009 0.53 15572 64.51 Hillside 00BAGE0010 0.00 19405 80.39 Hillside 00BAGE0011 0.59 15178 62.87 Hillside 00BAGE0012 3.09 6282 26.02 Hillside 00BAGE0013 0.14 18270 75.68 Hillside 00BAGE0014 0.00 19412 80.41 Hillside 00BAGE0015 0.00 19415 80.43 Hillside 00BAGE0016 0.00 19419 80.44 Hillside 00BAGE0017 1.15 12499 51.78 Hillside 00BAGE0018 0.46 16042 66.45 Hillside 00BAGE0019 0.00 19411 80.41 Hillside 00BAGE0020 0.00 19413 80.42 Hillside 00BAGE0021 0.00 19414 80.42 Hillside 00BAGE0022 2.64 7455 30.88 Hillside 00BAGE0023 0.00 19410 80.41 Hillside 00BAGE0024 0.00 19404 80.38 Hillside 00BAGE0025 0.31 17065 70.69 Hillside 00BAGE0026 0.00 19418 80.44 Hillside 00BAGE0027 0.00 19401 80.37 Hillside 00BAGE0028 0.11 18484 76.57 Hillside 00BAGE0029 0.00 19420 80.45 Hillside 00BAGE0030 0.70 14615 60.54 Hillside 00BAGE0031 0.00 19416 80.43 Hillside 00BAGE0032 0.05 18915 78.36
101 Ward Output Area Total Score Rank Percentile Hillside 00BAGE0033 0.00 19403 80.38 Hillside 00BAGE0034 0.00 19402 80.37 Hillside 00BAGE0035 0.95 13371 55.39 Hillside 00BAGE0036 0.07 18768 77.75
Lavender Fields 00BAGF0001 0.53 15562 64.47 Lavender Fields 00BAGF0002 0.85 13885 57.52 Lavender Fields 00BAGF0003 0.00 23762 98.43 Lavender Fields 00BAGF0004 2.60 7571 31.36 Lavender Fields 00BAGF0005 0.34 16869 69.88 Lavender Fields 00BAGF0006 0.25 17479 72.41 Lavender Fields 00BAGF0007 0.76 14269 59.11 Lavender Fields 00BAGF0008 0.93 13463 55.77 Lavender Fields 00BAGF0009 2.75 7172 29.71 Lavender Fields 00BAGF0010 1.61 10641 44.08 Lavender Fields 00BAGF0011 0.52 15630 64.75 Lavender Fields 00BAGF0012 1.12 12617 52.27 Lavender Fields 00BAGF0013 3.60 5159 21.37 Lavender Fields 00BAGF0014 0.62 15025 62.24 Lavender Fields 00BAGF0015 0.04 19002 78.72 Lavender Fields 00BAGF0016 4.35 3813 15.80 Lavender Fields 00BAGF0017 0.49 15860 65.70 Lavender Fields 00BAGF0018 1.68 10385 43.02 Lavender Fields 00BAGF0019 1.65 10489 43.45 Lavender Fields 00BAGF0020 0.02 19185 79.47 Lavender Fields 00BAGF0021 0.03 19031 78.84 Lavender Fields 00BAGF0022 1.56 10802 44.75 Lavender Fields 00BAGF0023 0.38 16569 68.64 Lavender Fields 00BAGF0024 1.01 13125 54.37 Lavender Fields 00BAGF0025 4.46 3645 15.10 Lavender Fields 00BAGF0026 1.27 11983 49.64 Lavender Fields 00BAGF0027 0.50 15778 65.36 Lavender Fields 00BAGF0028 3.18 6063 25.12 Lavender Fields 00BAGF0029 2.65 7434 30.80 Lavender Fields 00BAGF0030 0.64 14914 61.78 Lavender Fields 00BAGF0031 1.57 10794 44.71 Lavender Fields 00BAGF0032 1.40 11454 47.45 Lavender Fields 00BAGF0033 0.00 23763 98.44
Longthornton 00BAGG0001 0.46 16006 66.30 Longthornton 00BAGG0002 0.00 22780 94.37 Longthornton 00BAGG0003 0.06 18869 78.16 Longthornton 00BAGG0004 0.16 18105 75.00 Longthornton 00BAGG0005 2.76 7132 29.54 Longthornton 00BAGG0006 0.17 18038 74.72 Longthornton 00BAGG0007 1.25 12112 50.17 Longthornton 00BAGG0008 0.23 17602 72.92 Longthornton 00BAGG0009 0.07 18800 77.88 Longthornton 00BAGG0010 0.11 18520 76.72 Longthornton 00BAGG0011 0.37 16679 69.09 Longthornton 00BAGG0012 1.84 9790 40.56 Longthornton 00BAGG0013 0.37 16677 69.08 Longthornton 00BAGG0014 1.65 10515 43.56 Longthornton 00BAGG0015 0.41 16362 67.78
102 Ward Output Area Total Score Rank Percentile Longthornton 00BAGG0016 0.35 16808 69.63 Longthornton 00BAGG0017 1.40 11463 47.49 Longthornton 00BAGG0018 1.12 12615 52.26 Longthornton 00BAGG0019 0.00 22779 94.36 Longthornton 00BAGG0020 0.00 22777 94.35 Longthornton 00BAGG0021 0.00 22778 94.36 Longthornton 00BAGG0022 0.05 18921 78.38 Longthornton 00BAGG0023 1.28 11943 49.47 Longthornton 00BAGG0024 0.46 16034 66.42 Longthornton 00BAGG0025 1.77 10012 41.47 Longthornton 00BAGG0026 0.50 15793 65.42 Longthornton 00BAGG0027 0.53 15609 64.66 Longthornton 00BAGG0028 0.28 17275 71.56 Longthornton 00BAGG0029 0.42 16260 67.36 Longthornton 00BAGG0030 0.10 18533 76.77
Lower Morden 00BAGH0001 0.27 17341 71.84 Lower Morden 00BAGH0002 0.62 14995 62.12 Lower Morden 00BAGH0003 0.67 14760 61.14 Lower Morden 00BAGH0004 0.58 15258 63.21 Lower Morden 00BAGH0005 0.14 18275 75.70 Lower Morden 00BAGH0006 1.43 11352 47.03 Lower Morden 00BAGH0007 0.84 13889 57.54 Lower Morden 00BAGH0008 0.00 20442 84.68 Lower Morden 00BAGH0009 0.00 20440 84.67 Lower Morden 00BAGH0010 0.81 14096 58.39 Lower Morden 00BAGH0011 0.10 18577 76.96 Lower Morden 00BAGH0012 0.12 18395 76.20 Lower Morden 00BAGH0013 0.51 15698 65.03 Lower Morden 00BAGH0014 0.76 14278 59.15 Lower Morden 00BAGH0015 1.74 10128 41.96 Lower Morden 00BAGH0016 0.30 17193 71.22 Lower Morden 00BAGH0017 0.34 16882 69.93 Lower Morden 00BAGH0018 0.00 20441 84.68 Lower Morden 00BAGH0019 0.00 20439 84.67 Lower Morden 00BAGH0020 0.39 16498 68.34 Lower Morden 00BAGH0021 0.45 16084 66.63 Lower Morden 00BAGH0022 1.11 12651 52.41 Lower Morden 00BAGH0023 0.69 14639 60.64 Lower Morden 00BAGH0024 0.71 14543 60.24 Lower Morden 00BAGH0025 0.64 14925 61.83 Lower Morden 00BAGH0026 0.34 16866 69.87 Lower Morden 00BAGH0027 0.35 16766 69.45
Merton Park 00BAGJ0001 0.00 19925 82.54 Merton Park 00BAGJ0002 0.00 19927 82.55 Merton Park 00BAGJ0003 0.31 17089 70.79 Merton Park 00BAGJ0004 0.26 17440 72.25 Merton Park 00BAGJ0005 0.00 19930 82.56 Merton Park 00BAGJ0006 0.00 19937 82.59 Merton Park 00BAGJ0007 0.00 19928 82.55 Merton Park 00BAGJ0008 0.00 19929 82.56 Merton Park 00BAGJ0009 0.00 19926 82.54 Merton Park 00BAGJO010 0.00 19920 82.52
103 Ward Output Area Total Score Rank Percentile Merton Park OOBAGJ0011 0.00 19932 82.57 Merton Park OOBAGJ0012 0.35 16821 69.68 Merton Park OOBAGJ0013 0.00 19922 82.53 Merton Park OOBAGJ0014 0.00 19924 82.54 Merton Park OOBAGJ0015 0.13 18307 75.84 Merton Park OOBAGJ0016 0.14 18284 75.74 Merton Park OOBAGJ0017 0.00 19938 82.59 Merton Park OOBAGJ0018 0.69 14685 60.83 Merton Park OOBAGJ0019 0.00 19940 82.60 Merton Park OOBAGJ0020 0.32 17038 70.58 Merton Park OOBAGJ0021 0.81 14065 58.26 Merton Park OOBAGJ0022 0.27 17365 71.93 Merton Park OOBAGJ0023 0.00 19923 82.53 Merton Park OOBAGJ0024 0.00 19942 82.61 Merton Park OOBAGJ0025 0.00 19933 82.57 Merton Park OOBAGJ0026 0.00 19919 82.51 Merton Park OOBAGJ0027 0.00 19935 82.58 Merton Park OOBAGJ0028 0.00 19917 82.51 Merton Park OOBAGJ0029 0.00 19939 82.60 Merton Park OOBAGJ0030 0.00 19918 82.51
Pollards Hill OOBAGK0001 2.50 7815 32.37 Pollards Hill OOBAGK0002 1.33 11752 48.68 Pollards Hill OOBAGK0003 0.40 16423 68.03 Pollards Hill OOBAGK0004 1.01 13139 54.43 Pollards Hill OOBAGK0005 0.46 16026 66.39 Pollards Hill OOBAGK0006 0.82 14016 58.06 Pollards Hill OOBAGK0007 2.21 8634 35.77 Pollards Hill OOBAGK0008 3.64 5074 21.02 Pollards Hill OOBAGK0009 0.28 17281 71.59 Pollards Hill OOBAGK0010 0.04 18968 78.57 Pollards Hill OOBAGK0011 0.27 17356 71.90 Pollards Hill OOBAGK0012 0.01 19268 79.82 Pollards Hill OOBAGK0013 0.37 16682 69.11 Pollards Hill OOBAGK0014 1.03 13006 53.88 Pollards Hill OOBAGK0015 2.26 8461 35.05 Pollards Hill OOBAGK0016 0.31 17125 70.94 Pollards Hill OOBAGK0017 1.37 11562 47.90 Pollards Hill OOBAGK0018 0.59 15194 62.94 Pollards Hill OOBAGK0019 0.77 14250 59.03 Pollards Hill OOBAGK0020 3.04 6405 26.53 Pollards Hill OOBAGK0021 2.19 8697 36.03 Pollards Hill OOBAGK0022 2.74 7189 29.78 Pollards Hill OOBAGK0023 0.57 15294 63.36 Pollards Hill OOBAGK0024 0.71 14560 60.31 Pollards Hill OOBAGK0025 1.73 10171 42.13 Pollards Hill OOBAGK0026 0.10 18572 76.93 Pollards Hill OOBAGK0027 0.03 19026 78.82 Pollards Hill OOBAGK0028 2.71 7272 30.12 Pollards Hill OOBAGK0029 0.33 16933 70.14 Pollards Hill OOBAGK0030 1.87 9682 40.11
Ravensbury OOBAGL0001 2.37 8156 33.79 Ravensbury OOBAGL0002 2.30 8348 34.58
104 Ward Output Area Total Score Rank Percentile Ravensbury 00BAGL0003 2.63 7501 31.07 Ravensbury 00BAGL0004 1.71 10271 42.55 Ravensbury 00BAGL0005 1.41 11426 47.33 Ravensbury 00BAGL0006 1.95 9434 39.08 Ravensbury 00BAGL0007 2.08 9027 37.39 Ravensbury 00BAGL0008 2.01 9235 38.26 Ravensbury 00BAGL0009 2.30 8355 34.61 Ravensbury 00BAGL0010 3.04 6407 26.54 Ravensbury 00BAGL0011 3.83 4689 19.42 Ravensbury 00BAGL0012 0.17 18053 74.78 Ravensbury 00BAGL0013 0.40 16440 68.10 Ravensbury 00BAGL0014 0.74 14389 59.61 Ravensbury 00BAGL0015 1.47 11173 46.28 Ravensbury 00BAGL0016 1.48 11161 46.23 Ravensbury 00BAGL0017 0.46 16054 66.50 Ravensbury 00BAGL0018 0.17 18054 74.79 Ravensbury 00BAGL0019 0.75 14324 59.34 Ravensbury 00BAGL0020 2.68 7347 30.43 Ravensbury 00BAGL0021 1.26 12030 49.83 Ravensbury 00BAGL0022 4.28 3929 16.28 Ravensbury 00BAGL0023 0.59 15163 62.81 Ravensbury 00BAGL0024 0.47 15949 66.07 Ravensbury 00BAGL0025 1.70 10293 42.64 Ravensbury 00BAGL0026 0.33 16938 70.17 Ravensbury 00BAGL0027 0.95 13415 55.57 Ravensbury 00BAGL0028 1.15 12479 51.69 Ravensbury 00BAGL0029 1.12 12610 52.24 Ravensbury 00BAGL0030 1.02 13073 54.15 Ravensbury 00BAGL0031 1.62 10617 43.98
Raynes Park 00BAGM0001 0.00 21072 87.29 Raynes Park 00BAGM0002 2.34 8224 34.07 Raynes Park 00BAGM0003 0.47 15957 66.10 Raynes Park 00BAGM0004 0.00 21071 87.29 Raynes Park 00BAGM0005 0.00 21069 87.28 Raynes Park 00BAGM0006 0.00 21084 87.34 Raynes Park 00BAGM0007 0.00 21077 87.31 Raynes Park 00BAGM0008 0.00 21076 87.31 Raynes Park 00BAGM0009 0.30 17161 71.09 Raynes Park 00BAGM0010 0.00 21082 87.33 Raynes Park 00BAGM0011 0.48 15917 65.94 Raynes Park 00BAGM0012 0.00 21066 87.27 Raynes Park 00BAGM0013 0.00 19335 80.10 Raynes Park 00BAGM0014 0.00 21080 87.32 Raynes Park 00BAGM0015 0.00 21067 87.27 Raynes Park 00BAGM0016 0.00 21081 87.33 Raynes Park 00BAGM0017 0.00 21068 87.27 Raynes Park 00BAGM0018 0.00 21074 87.30 Raynes Park 00BAGM0019 0.00 21079 87.32 Raynes Park 00BAGM0020 0.00 21078 87.32 Raynes Park 00BAGM0021 0.00 21075 87.30 Raynes Park 00BAGM0022 0.36 16749 69.38 Raynes Park 00BAGM0023 3.06 6375 26.41 Raynes Park 00BAGM0024 0.00 21070 87.28
105 Ward Output Area Total Score Rank Percentile Raynes Park 00BAGM0025 0.28 17333 71.80 Raynes Park 00BAGM0026 0.00 21083 87.34 Raynes Park 00BAGM0027 2.26 8493 35.18 Raynes Park 00BAGM0028 2.64 7450 30.86 Raynes Park 00BAGM0029 1.28 11961 49.55 Raynes Park 00BAGM0030 2.39 8088 33.50 Raynes Park 00BAGM0031 0.02 19171 79.42 Raynes Park 00BAGM0032 0.23 17611 72.95 Raynes Park 00BAGM0033 0.00 21085 87.34 Raynes Park 00BAGM0034 0.00 21073 87.29
St Helier 00BAGN0001 0.51 15741 65.21 St Helier 00BAGN0002 1.12 12616 52.26 St Helier 00BAGN0003 1.98 9351 38.74 St Helier 00BAGN0004 3.72 4910 20.34 St Helier 00BAGN0005 2.24 8550 35.42 St Helier 00BAGN0006 0.93 13490 55.88 St Helier 00BAGN0007 0.19 17952 74.37 St Helier 00BAGN0008 1.20 12282 50.88 St Helier 00BAGN0009 0.41 16361 67.78 St Helier 00BAGN0010 1.91 9550 39.56 St Helier 00BAGN0011 2.22 8600 35.63 St Helier 00BAGN0012 2.14 8835 36.60 St Helier 00BAGN0013 2.12 8893 36.84 St Helier 00BAGN0014 1.98 9326 38.63 St Helier 00BAGN0015 4.45 3656 15.14 St Helier 00BAGN0016 1.37 11594 48.03 St Helier 00BAGN0017 2.58 7630 31.61 St Helier 00BAGN0018 0.86 13802 57.17 St Helier 00BAGN0019 1.25 12065 49.98 St Helier 00BAGN0020 0.78 14199 58.82 St Helier 00BAGN0021 0.00 24107 99.86 St Helier 00BAGN0022 1.47 11197 46.38 St Helier 00BAGN0023 1.95 9422 39.03 St Helier 00BAGN0024 1.64 10532 43.63 St Helier 00BAGN0025 0.08 18721 77.55 St Helier 00BAGN0026 3.60 5154 21.35 St Helier 00BAGN0027 0.00 19344 80.13 St Helier 00BAGN0028 1.41 11419 47.30 St Helier 00BAGN0029 1.74 10148 42.04 St Helier 00BAGN0030 1.62 10615 43.97 St Helier 00BAGN0031 2.17 8764 36.30
Trinity 00BAGP0001 0.57 15331 63.51 Trinity 00BAGP0002 0.00 21197 87.81 Trinity 00BAGP0003 0.00 21199 87.82 Trinity 00BAGP0004 0.47 15935 66.01 Trinity 00BAGP0005 0.55 15450 64.00 Trinity 00BAGP0006 2.15 8822 36.55 Trinity 00BAGP0007 0.00 21166 87.68 Trinity 00BAGP0008 0.00 21176 87.72 Trinity 00BAGP0009 0.00 21173 87.71 Trinity 00BAGP0010 0.00 21207 87.85 Trinity 00BAGP0011 2.12 8890 36.83
106 Ward Output Area Total Score Rank Percentile Trinity 00BAGP0012 1.05 12925 53.54 Trinity 00BAGP0013 0.00 21167 87.68 Trinity 00BAGP0014 4.13 4169 17.27 Trinity 00BAGP0015 0.00 21163 87.67 Trinity 00BAGP0016 0.00 21191 87.78 Trinity 00BAGP0017 0.00 21196 87.80 Trinity 00BAGP0018 0.51 15706 65.06 Trinity 00BAGP0019 2.57 7653 31.70 Trinity 00BAGP0020 0.00 21182 87.75 Trinity 00BAGP0021 0.00 21194 87.80 Trinity 00BAGP0022 0.00 21171 87.70 Trinity 00BAGP0023 0.00 21172 87.71 Trinity 00BAGP0024 1.77 10013 41.48 Trinity 00BAGP0025 0.85 13882 57.51 Trinity 00BAGP0026 0.00 21195 87.80 Trinity 00BAGP0027 0.00 21203 87.83 Trinity 00BAGP0028 0.00 21177 87.73 Trinity 00BAGP0029 0.00 21192 87.79 Trinity 00BAGP0030 0.00 21206 87.85 Trinity 00BAGP0031 0.45 16096 66.68 Trinity 00BAGP0032 0.10 18568 76.92 Trinity 00BAGP0033 0.00 21160 87.66 Trinity 00BAGP0034 0.00 21193 87.79
Village 00BAGQ0001 0.00 19398 80.36 Village 00BAGQ0002 0.01 19269 79.82 Village 00BAGQ0003 0.00 19385 80.30 Village 00BAGQ0004 0.00 19380 80.28 Village 00BAGQ0005 0.00 19395 80.34 Village 00BAGQ0006 0.00 19399 80.36 Village 00BAGQ0007 1.76 10091 41.80 Village 00BAG00008 0.01 19255 79.76 Village 00BAGQ0009 0.00 19376 80.27 Village 00BAGQ0010 0.00 19393 80.34 Village 00BAGQ0011 0.00 19390 80.32 Village 00BAGQ0012 0.00 19384 80.30 Village 00BAGQ0013 0.00 19392 80.33 Village 00BAGQ0014 0.00 19377 80.27 Village 00BAGQ0015 0.00 19396 80.35 Village 00BAGQ0016 1.62 10600 43.91 Village 00BAGQ0017 0.00 19381 80.29 Village 00BAGQ0018 0.00 19400 80.36 Village 00BAGQ0019 0.00 19378 80.27 Village 00BAGQ0020 0.00 19386 80.31 Village 00BAGQ0021 0.00 19388 80.31 Village 00BAG00022 0.00 19383 80.29 Village 00BAGQ0023 0.00 19391 80.33 Village 00BAGQ0024 0.00 19382 80.29 Village 00BAGQ0025 0.00 19387 80.31 Village 00BAGQ0026 0.00 19394 80.34 Village 00BAGQ0027 0.00 19389 80.32 Village 00BAGQ0028 0.00 19379 80.28 Village 00BAGQ0029 0.00 19397 80.35 Village 00BAGQ0030 0.58 15228 63.08
107 Ward Output Area Total Score Rank Percentile West Barnes 00BAGR0001 0.00 20429 84.63 West Barnes 00BAGR0002 0.00 20432 84.64 West Barnes 00BAGR0003 0.00 20422 84.60 West Barnes 00BAGR0004 0.00 20418 84.58 West Barnes 00BAGR0005 0.00 20420 84.59 West Barnes 00BAGR0006 0.00 20426 84.61 West Barnes 00BAGR0007 0.58 15260 63.21 West Barnes 00BAGR0008 0.69 14636 60.63 West Barnes 00BAGR0009 0.00 20431 84.64 West Barnes 00BAGRO010 0.00 20417 84.58 West Barnes 00BAGRO011 0.00 20423 84.60 West Barnes 00BAGRO012 0.00 20427 84.62 West Barnes 00BAGRO013 0.16 18140 75.14 West Barnes 00BAGRO014 0.53 15582 64.55 West Barnes 00BAGRO015 0.00 20421 84.59 West Barnes 00BAGRO016 0.11 18471 76.52 West Barnes 00BAGRO017 0.00 20437 84.66 West Barnes 00BAGRO018 0.00 20434 84.65 West Barnes 00BAGRO019 0.00 20424 84.61 West Barnes 00BAGRO020 0.00 20436 84.66 West Barnes 00BAGRO021 0.00 20416 84.57 West Barnes 00BAGRO022 0.00 20425 84.61 West Barnes 00BAGRO023 0.00 20433 84.64 West Barnes 00BAGRO024 0.00 20435 84.65 West Barnes 00BAGRO025 0.00 20415 84.57 West Barnes 00BAGRO026 0.00 20430 84.63 West Barnes 00BAGRO027 0.00 20428 84.62 West Barnes 00BAGRO028 0.00 20419 84.59 West Barnes 00BAGRO029 0.65 14837 61.46 West Barnes 00BAGRO030 0.00 20438 84.66
Wimbledon Park 00BAGS0001 0.00 20133 83.40 Wimbledon Park 00BAGS0002 0.13 18361 76.06 Wimbledon Park 00BAGS0003 0.00 20115 83.33 Wimbledon Park 00BAGS0004 0.60 15113 62.61 Wimbledon Park 00BAGS0005 0.00 20107 83.29 Wimbledon Park 00BAGS0006 0.04 18969 78.58 Wimbledon Park 00BAGS0007 0.00 20118 83.34 Wimbledon Park 00BAGS0008 0.00 20101 83.27 Wimbledon Park 00BAGS0009 0.00 20110 83.31 Wimbledon Park 00BAGS0010 0.00 20127 83.38 Wimbledon Park 00BAGS0011 0.00 20106 83.29 Wimbledon Park 00BAGS0012 0.47 15969 66.15 Wimbledon Park 00BAGS0013 3.73 4887 20.24 Wimbledon Park 00BAGS0014 0.00 20135 83.41 Wimbledon Park 00BAGS0015 0.00 20114 83.32 Wimbledon Park 00BAGS0016 0.00 20097 83.25 Wimbledon Park 00BAGS0017 0.00 20136 83.41 Wimbledon Park 00BAGS0018 0.00 20131 83.39 Wimbledon Park 00BAGS0019 0.00 20122 83.36 Wimbledon Park 00BAGS0020 0.00 20096 83.25 Wimbledon Park 00BAGS0021 0.00 20128 83.38 Wimbledon Park 00BAGS0022 0.02 19175 79.43 Wimbledon Park 00BAGS0023 0.00 20134 83.41
108 Ward Output Area Total Score Rank Percentile Wimbledon Park 00BAGS0024 0.00 20102 83.27 Wimbledon Park 00BAGS0025 0.00 20112 83.31 Wimbledon Park 00BAGS0026 0.00 20108 83.30 Wimbledon Park 00BAGS0027 0.00 20139 83.43 Wimbledon Park 00BAGS0028 0.00 20103 83.28 Wimbledon Park 00BAGS0029 0.23 17632 73.04 Wimbledon Park 00BAGS0030 0.00 20111 83.31 Wimbledon Park 00BAGS0031 0.00 20116 83.33
109 Appendix C: What is an Interactive Qualifying Project?
According to the Handbook for IQP Advisors and Students an Interactive Qualifying
Project (IQP) at WPI is
"a project which deals with the relationship between technology and
society. The goals of the Plan are to promote learning by doing through
project work, maximize student choice in designing their own
educational programs, and ensure that students had not only passed
courses but were in fact competent as professionals, literate in the
humanities and understood the societal implications of their
professional work" (Woods, Ch. 1, ¶1).
The IQP portion of the WPI Plan is designed to teach students the role of technology
in society and to make them aware of the impact of technology on the world. All too often
students go through their academic careers without a sense of the effect that their work will
have. While some classes attempt to capture this idea, it is difficult while still in the ivory
tower of academia. Lectures tend to focus on how things work, why they work, but not what
that means in a broader sense for society. Learning about the real world is only effective
when the learning takes place in the real world. In 1972, a committee formed to define the
requirements of the IQP concluded that "graduates often emerged ill-equipped to assist
society in evaluating the overall effects of technology on the quality of life" (Woods, Ch. 2,
¶2). The committee determined that resulting from the IQP students would be aware of larger
social issues; able to analyse societal, humanistic, and technological interactions; able to
make better judgements and recommendation on matters that affect society.
The goal of our project is to use GIS and statistical tools to analyse the poverty levels
of Merton's neighbourhoods so that the local authority of Merton may make better informed
decisions on where and how to help alleviate poverty. From this statement it is easy to see
110 that the project involves technology, namely the use of Geographical Information Systems and the statistical analysis tools. The statement also shows the larger societal issue involved: poverty.
After seven weeks of studying Merton's census and survey data, analysing it, and mapping it out, we have a very good understanding of the conditions of life in the borough
and where they are located. We see the differences between neighbourhoods and wards in
Merton, and the difference between Merton and the rest of London. We have a better
understanding of the variables that influence poverty, not just in Merton but through out the
world. And from that we see the broader issues that stem from poverty. From the data we
learn how technologies in areas such as healthcare, transportation, and education affect the
world in which they are applied. From using GIS to analyse the data, we see how technology
can be used to improve the lives of everyday people. And while none of our results may be
able to be applied to any other location, at least we have a better understanding of how the
choices that we and people in authority make affect the community, country, and world that
we live in.
111 Digital Appendix D: Population Profile
This digital appendix is contained on the companion CD-ROM. Both the web based and Microsoft Word versions of the 2001 Population Profile for Merton are included. The
Population Profile contains basic statistics on Merton from the 2001 Census. Please click on index.html to begin the web based version.
Digital Appendix E: Poverty Profile
This digital appendix is contained on the companion CD-ROM. Both the web based
and Microsoft Word versions of the 2001 Poverty Profile for Merton are included. The
Poverty Profile is an analysis of deprivation in Merton, based primarily on census data.
Please click on index.html to begin the web based version.
Digital Appendix F: GIS Maps
This digital appendix is contained on the companion CD-ROM. Included are all of the
GIS maps that have been produced for the local authority of Merton. Please click on
index.html to begin browsing the pictures.
112 Digital Appendix G: MapInfo Workspaces
This digital appendix is contained on the companion CD-ROM. Included are all of the
MapInfo worksapces that were used for producing the GIs maps. Inside this directory are three other directories. The directory "London" includes workspaces for the indicators of
deprivation at the ward level and the deprivation index itself The directory "Merton"
includes workspaces for all variables included in the census and the deprivation index. The
directory "Southwest London" includes workspaces for the indicators of deprivation at the
ward and OA level and the deprivation index itself
Digital Appendix H: IQP Report
A copy of our IQP report, names "IQP Report.doc" has been provided on the
companion CD-ROM.
Digital Appendix I: Website Files
A copy of all website files has been provided in the folder "website" on the
companion CD-ROM.
113