Ben-Gurion University of the Negev The Jacob Blaustein Institutes for Desert Research The Albert Katz International School for Desert Studies
Accessibility and Social Equity in the
Metropolitan Area of Beer-Sheba
Thesis submitted in partial fulfillment of the requirements for the degree of "Master of Science"
By: Natalia Mednik
March, 2016 ii
Ben-Gurion University of the Negev The Jacob Blaustein Institutes for Desert Research The Albert Katz International School for Desert Studies
Thesis title: Accessibility and Social Equity in the
Metropolitan Area of Beer-Sheba
Thesis submitted in partial fulfillment of the requirements for the degree of "Master of Science"
By: Natalia Mednik
Under the Supervision of Dr. Yodan Rofe, Supervising Advisor: Dr. Aviva Peeters, Department of Man in Drylands
Author's Signature ………….……………………...... Date 17.03.2016
Approved by the Supervisor…………….…………….. Date 17.03.2016
Approved by the Director of the School ……………… Date ………….
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Abstract:
Accessibility and Social Equity in the Metropolitan Area of Beer-Sheba
By Natalia Mednik Thesis submitted in partial fulfillment of the requirements for the degree of "Master of Science"
Ben-Gurion University of the Negev
The Jacob Blaustein Institutes for Desert Research
The Albert Katz International School for Desert Studies, 2016
The main role of transportation is to provide an access to spatially distributed activities.
Accessibility is related to freedom of choice, and hence to achieving a higher level of quality of life and personal fulfillment. Increasingly, accessibility is identified as a key criterion for assessing the quality of transportation systems, and the policies that govern them.
This thesis is an extension of the ongoing research project that evaluates accessibility and social equity in
Tel-Aviv metropolitan area. The research is led by Dr. Rofè, Prof. Benenson, and Prof. Martens. During the research, a methodology to assess accessibility from the perspective of equity was developed together with CityGraph - ArcGIS tool for evaluating accessibility.
The aim of this current study is to apply an approach that was developed for Tel-Aviv metropolitan area to the Beer-Sheba region by applying CityGraph: an innovative, ArcGIS based tool developed through the research. The tool enables a detailed accessibility analysis that includes all the elements of a trip from a passenger point of view. A second aim is to analyze the results together with data on the socio-economic level of households, and their dependency on public transportation, and evaluate the equity of accessibility to jobs, services, education opportunities and commercial enterprises.
Thus the methodology described in this project allows demonstrating the level of accessibility at a high resolution and shows who actually benefits from the transportation projects.
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Acknowledgement First I would like to thank a lot and express my sincere gratitude to my supervisor, Dr. Yodan Rofe for the constant guidance, patience and support during the all entire project. Regardless the distances, obstacles and length of the project, he always supported, and encouraged me to proceed and move on.
Without this help the project would not have been done.
I am very grateful for the constant help and assistance provided by Dr. Aviva Peeters during the long hours of work on ArcGIS analysis. Thank you.
I am thankful to the Albert Katz International School and the department of Man in Drylands. I also want to say thank you to everyone in the department for the very unique atmosphere in the office, advices, lectures and help. It has been a great experience.
I would like to thank Ministry of Transport and Road Safety who has funded the research 'Accessibility and Social Equity in Tel-Aviv Metropolitan Area – examination of the current conditions and development scenarios'. My study is an extension of this research and without this funding would not have been possible.
I want to thank Prof. Benenson, Dr. Ben-Elia and Prof. Martens who developed and led the main research on Tel-Aviv.
I would like to thank Jacob Ben Arieh and Dr. Eli Safra from CityGraph, who developed the computational tools, used in the research and provided necessary calculations for the project.
Special thanks to Dorit Levin whom I would like to express my gratitude for being helpful all the way long with every question regarding documentation processes.
Finally I want to say thanks to my parents who always supported me and my friends who became a family for this period, who supported and encouraged me and spend the working and fun time together. Thank you all!
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Table of Contents Abstract: ...... iii Acknowledgement ...... iv Table of Contents ...... v List of tables and figures ...... vii 1. Introduction ...... 1 2. Literature review ...... 2 2.1. Accessibility ...... 2 2.1.1. Accessibility definition ...... 2 2.1.2. Accessibility measures in transportation planning ...... 3 2.1.3. Move from mobility to accessibility ...... 3 2.1.4. Sustainability and accessibility ...... 5 2.2. Equity in transportation ...... 6 2.2.1. Inequity in transportation planning ...... 6 2.2.2. Measures of equity in transportation planning ...... 7 2.3. Public transportation dependency ...... 9 2.3.1. Perspectives on public transport dependency ...... 9 2.3.2. Public transport disadvantage measure ...... 9 2.3.3. Transport disadvantage indicators ...... 11 2.3.4. Transportation “Need” measure approach ...... 12 2.4. Summary ...... 14 3. Research question and objectives ...... 16 4. Research methodology ...... 17 4.1. Accessibility ...... 17 4.1.1. Definition and calculation approach ...... 17 4.1.2. CTGraph – a GIS-based tool for measuring accessibility ...... 19 4.1.3. Data collection and data construction for accessibility measure ...... 20 4.2. Equity in transportation planning ...... 28 4.2.1. Measuring inequity using Lorenz curves and Gini index...... 28 4.3. Public transport dependency measure ...... 30 4.3.1. Transportation need index calculation for Beer-Sheba Metropolitan Area ...... 30 4.3.2. Demographic data collection and data construction ...... 32
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4.4 Transportation accessibility in relationship to transportation need index ...... 33 4.5. Summary ...... 33 5. The case study of Beer-Sheba Metropolitan Area ...... 34 5.1 Negev region ...... 34 5.2 The city of Beer-Sheba ...... 35 5.3 The Arab-Bedouin villages in the Negev ...... 36 5.4 Socio-economic profile of Beer-Sheba Metropolitan Area ...... 38 5.5 Local services ...... 39 6. Results and Analysis ...... 40 6.1 Accessibility in the Metropolitan Area of Beer-Sheba ...... 40 6.1.1 Accessibility for the city of Beer-Sheba ...... 40 6.1.2 Accessibility for the Metropolitan area of Beer-Sheba ...... 43 6.2. Public transport dependency ...... 44 6.3 Results ...... Error! Bookmark not defined. 6.4 Summary ...... 62 7. Discussion, conclusions and further research ...... 63 7.1 Understanding accessibility situation in the area ...... 63 7.2 Public transportation dependency ...... 64 7.3 Public transportation accessibility and dependency ...... 64 7.4 Limitations of the research ...... 64 7.5 Future work ...... 65 References ...... 66
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List of tables and figures Figure 1 Disadvantage forms………………………………………………………………………………………………………………….11 Figure 2 The general transit feed specification data………………………………………………………………………………21 Figure 3 Overview of layers of bus lines and stops in Beer-Sheba Metropolitan area and Beer-Sheba city. Each transit stop is associated with its line…………………………………………………………………………………………….24 Figure 4 Overview of road network in Beer-Sheba Metropolitan Area (a) and the city of Beer-Sheba (b) …………………………………………………………………………………………………….……………………………………..…………………27 Figure 5 Illustration of Thiessen Polygons ...... 28 Figure 6 Representation of the Lorenz curve ...... 29 Figure 7 Map of Israel with Beer-Sheba Metropolitan Area (red color) …………………………………………………35 Figure 8 TAZ, Military Zones and Bedouin settlements in Beer-Sheba Metropolitan area ...... 36 Figure 9 No. of jobs accessible from Beer-Sheba in the metropolitan area starting between 07.00 and 08.00 in the morning and taking 15 minutes ...... 41 Figure 10 No. of jobs accessible from Beer-Sheba in the metropolitan area starting between 07.00 and 08.00 in the morning and taking 30 minutes ...... 42 Figure 11 No. of jobs accessible from Beer-Sheba in the metropolitan area starting between 07.00 and 08.00 in the morning and taking 45 min ...... 42 Figure 12 Number of jobs in the Metropolitan Area of Beer-Sheba accessible in less than 45 min by bus…………………………………………………………………………………………………………………………………………………………44 Figure 13 Persons 10-18 years old…………………………………………………………………………………………………………45 Figure 14 Persons aged over 65 ...... 46 Figure 15 Adults without cars ...... 46 Figure 16 Unemployed adults ...... 47 Figure 17 Persons with disabilities ...... 47 Figure 18 Persons with income below median ...... 48 Figure 19 Public transportation need index in the Metropolitan area ...... 48 Figure 20 Public transportation need index in the Metropolitan area and the city of Beer-Sheba ...... 49 Figure 21 The makeup of weighted Transportation Need Index for the 13 highest needs TAZ ...... 50 Figure 22 Lorenz curves and calculation of Gini index for Public Transportation Need and Income by TAZ ...... 51 Figure 23 highlighted TAZ ...... 54 Figure 24 The relative distribution of public transport needs index and accessibility index……………………56 Figure 25 Means of getting to work according to the 2008 census in the communities ...... 57 Figure 26 Means of getting to work according to the 2008 census in Dimona ...... 58 Figure 27 Means of getting to work according to the 2008 census in Arad ...... 58 Figure 28 Bnei Shimon Regional Council...... 59 Figure 29 Highlighted settlements ...... 60 Figure 30 highlighted TAZ in the city of Beer-Sheba……………………………………………………………………….………61 Figure 31 Spatial distribution of accessibility per single building……………………………………………………………62
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Table 1 Groups of people typically defined as transport disadvantaged ...... 12 Table 2 Transport need indicators and weights applied (Currie, 2007) ...... 12 Table 3 The general transit feed specification data description ...... 2221 Table 4 Routes...... 23 Table 5 Route segments ...... 2323 Table 6 Stops ...... 24 Table 7 Schedule ...... 2525 Table 8 Nodes ...... 26 Table 9 Links ...... 26 Table 10 Need indicator weights ...... 31 Table 11 Summary of number jobs accessible from Beer-Sheba in the metropolitan area for 15, 30 and 45 min trip ...... 34 Table 12 Distribution of accessibility index and population for the Metropolitan area of Beer-Sheba ..... 44 Table 13 Number of TAZ and population in each category of need index………………………………… ...... …..52 Table 14: Z-score test with values below -2 or above 2 in one of the parameters - need index or accessibility index ………………………………………………………………………………………………………………………………...54 Table 15 highlighted TAZ……………………………………………………………………………………………………...... 55
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1. Introduction Cities are constantly changing and developing and changes in public transport infrastructure and services affect the inhabitants. Therefore location of public infrastructure, land use patterns, location of different population groups raise a question who is actually benefiting from the changes in transportation and land use? In order to answer this question this thesis is going to explore the public transportation accessibility in the area and the population that is served by it. The attention to public transport accessibility for those who are in need may then influence the relocation and development of transportation service in the region.
This study theorizes that transportation benefits are quantified by the number of destinations that can be reached within a certain time threshold.
To assess equity this study generates a social indicator called transportation need index to identify socially disadvantaged population and compares the level of accessibility to jobs at a specific travel time to transportation need level in the Beer-Sheba region.
Thus, this thesis is organised in the following manner: the first Chapter is Introduction, second chapter represents a literature review on accessibility, transportation equity and public transport dependency. Chapter 3 – research questions and objectives. Chapter 4 presents the research methodology used in this study for each part. Chapter 5 describes Beer-Sheba Metropolitan area case study. Chapter 6 presents the findings of the thesis. Chapter 7 presents conclusions and suggestions for futher research. 2
2. Literature review The literature review is organized in a following way: the first section discusses accessibility and accessibility measures; the second examines the role of equity in transportation, and its measures, while the third section elaborates on public transport dependency and population in need of public transportation.
2.1. Accessibility
2.1.1. Accessibility definition
„Accessibility is the geographic definition of opportunity. The opportunity individuals have to participate in necessary or desired activities, or to explore new ones, is contingent upon their ability to reach the right places at the appropriate times and with reasonable expenditure of resources and effort (Janelle DG, Hodge DC, 2000).
Accessibility has various of meanings and could be defined in numerous ways; accessibility is a geographic definition of opportunity (Janelle DG, Hodge DC, 2000), and is understood as the ability of people to access the necessary or desired activities (Geurs KT and Ritsema van Eck JT,
2001; GarbY and Levine J, 2002). It is specified as well by potential destinations and the easiness of reaching them. The more destinations that could be achieved the higher the level of accessibility
(Handy SL and Niemeier DA, 1997). Accessibility is identified as a key criterion to assess the quality of transport policy (Kenyon S, Lyons G et al, 2002). It is an important characteristic of metropolitan areas, and is considered the reason for people‟s relocation to metropolitan areas
(Handy SL and Niemeier DA, 1997).
The literature distinguishes two forms of accessibility: person accessibility and place accessibility
(Pirie, 1979; Kwan, 1999; Miller, 2007). Person accessibility refers to persons' attribute, a person has (or has not) accessibility to a given set of places. Place accessibility is a location (activity) attribute, a place is accessible (or not) for a given set of people or from a given set of other locations
(Martens, 2012). Accessibility is also considered as an important part of infrastructure and affects
3 people quality of life, people's with disabilities, people‟s health and participation in community life
(Rubulotta E, Ignaccolo M, Imturri G, Rofe Y, 2013).
Subsequently one of the most common definitions of accessibility is identified as the ability of people to reach and engage in opportunities and activities. To reach opportunity indicates to bridge spatial separation and necessity of transport use. Spatial form is only one form of separation, others include age, gender, income, ethnicity and other personal circumstances and transportation is not the only dimension that is significant in the accessibility concept (Farrington J, Farrington C, 2005).
2.1.2. Accessibility measures in transportation planning
Different types of accessibility measures have been developed (Handy SL and Niemeier DA, 1997;
Geurs KT and Ritsema van Eck JT, 2001; Geurs KT, B van Wee, 2004). They depended on the number of opportunities and activities, in which an individual can participate, the relative utility that people derive from access, and the cost and travel time of reaching them (Handy SL and Niemeier
DA, 1997). Geurs and van Wee (2004) distinguish four types of accessibility measures:
• Infrastructure-based – service level of transport infrastructure (e.g., “the average travel
speed on the road network”);
• Location-based – accessibility of location (e.g., “the number of jobs within 30 min. travel
from origin locations”);
• Person-based – individual accessibility (e.g., “the number of activities in which an
individual can participate at a given time”);
• Utility-based – the economic benefits that people derive from access to the destination
The available literature provides excellent examples that apply measures to assess accessibility
(Shen, 1998; Blumenberg EA, Ong P, 2001; Hess, 2005; Kawabata M, Shen Q, 2006 and
Kawabata, 2009)
2.1.3. Move from mobility to accessibility
The goal of the transportation system is to provide access to destinations. Accessibility is related to freedom of choice, and hence to achieving a higher level of personal fulfillment and satisfaction
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(Kwan MP, Hong XD, 1998). In this sense, accessibility links to mobility 'the ease with which a person can move through space' (Martens, 2012), or potential mobility which is related to freedom of movement or the potential of movement (Kaufmann, 2002). Personal accessibility is different from potential mobility; it refers to the ease with which a destination could be reached within given location and time (Farrington J, Farrington C, 2005); (Niemeier, 1997)
An increase in mobility means that a person can travel over longer distances or more frequently or both (Sager, 2005). Within a higher level of potential mobility more possible opportunities could be achieved (Kaufmann, 2002). However, users of transportation network are interested in accessing opportunities available at destinations and not in a travel per se but in mobility (Martens,
2013).
Traditional mobility-centered approach focuses on level of service measures and qualities of the transport network system; the approach aims to decrease cost (time and money) of travel per kilometer. However, accessibility improvement aims to reduce the total travel cost needed to access destination. Moreover accessibility indicators are more complex. They consist of the land use system and transportation system together (Garb Y and Levine J, 2002). Improved mobility can indicate improved accessibility, however not necessarily vice versa (Martens, 2013; Garb Y and
Levine J, 2002). For instance, rural areas with scattered land uses and high level of mobility do not necessarily indicate a high level of accessibility, and urban areas with low mobility do not necessary indicate poor accessibility, because of the high density of different land use types. Thus, accessibility cannot be improved only through improvement of the transport system, it also needs interventions in the land use system, because low accessibility may be at the same time the result of poor transport systems or low density of necessary destinations. Moreover, high accessibility does not necessarily indicate a good transport system; it can also be a result of a high variety of destinations at particular neighborhood, city, or metropolitan area (Martens, 2013). Thus, accessibility is considered an important criterion to assess the transport and land use system together.
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2.1.4. Sustainability and accessibility
The growing interest in sustainable development has emphasized the importance of public transport accessibility. Accessibility is an essential yardstick for each of the parts of sustainable development: economic progress, environmental quality and social equity.
A sustainable transport system is one that is accessible, safe, environmentally-friendly, and affordable. (ECMT 2004)
The social justice perspective of sustainable development uses accessibility as an indicator which is able to show how benefits and burdens are distributed over members of society such as access to employment and critical services (Farrington J, Farrington C, 2005). Another justice criterion is the principle of need (Sen, 1973). This criterion indicates that one individual or group may need higher accessibility level than another (Cass N, 2005). The analysis of transportation equity is important because equity concerns should influence transportation planning decisions for the reason that the lack of accessibility seriously influences people's way of living.
Economic development accessibility is a precondition for economic growth which refers to the economic progress of communities. Economic opportunities tend to arise within growing transportation system. Economic development refers to resource efficiency, local economic development, and affordability since it enables the exchange of people and goods and thus enables the functioning of the economy (Bruinsma FR, Nijkamp P, et al, 1990). Furthermore economic development make the most of welfare, which is also contribute to wellbeing and factors such as health, friendship, community, environmental quality, etc. (Litman, 2007)
Often these dimensions are competing with each other, however balancing of social and economic parameters are essential for achieving equity outcome to promote improvement of the quality of life for all people (Roseland 2000).
This study focuses on the use of an accessibility measure to address the equity dimension of sustainable transportation. The reason for this is that a lack of accessibility has a serious impact on
6 people's way of living and may prevent them from participation in the labor market, reaching health care services, as well as having enough contacts with friends and family (Lucas, 2012). A shift from supplying mobility to improving accessibility helps to see more sustainable travel options, like walking, cycling, public transport, and short car trips (Bertolini L, 2005).
From the perspective of environmental quality, attention is directed towards the differences between transport modes in terms of energy use and environmental externalities (Feitelson, 2002). Energy use, CO2 emissions, air pollution, and traffic noise are the top indicators of the environmental impact of transportation (Bertolini L, 2005). A performance indicator was applied to assess the differences in accessibility provided by more versus less energy-intensive and polluting modes of transport (Kwok, Yeh, 2004). The focus is on the degree of car dependence in the particular region.
When car accessibility is significantly higher than the accessibility of other more environmentally friendly modes of transportation than an area is defined as car dependent and this could be reduced by increasing accessibility of the other alternative modes of transport (Shen, 1998; Blumenberg EA,
Ong P, 2001; Hess, 2005; Kawabata M, Shen Q, 2006; Kawabata, 2009; Kwok, Yeh , 2004).
2.2. Equity in transportation
2.2.1. Inequity in transportation planning
Equity refers to how „benefits and burdens‟ are distributed over members of society and whether or not this distribution is fair and proper (Farrington J, Farrington C, 2005). Transportation planning has diverse impact on equity: the quality of transportation relates to economic and social prospects and development of locations. Social justice is understood as requiring social inclusion. Social inclusion is presence of people in society, in contrast to social exclusion where people become separated from resources that are considered normal for contemporary society (Farrington J,
Farrington C, 2005).
Numerous literature examples highlight accessibility problems that people are faced with, due to lack of transportation service in everyday life, and how the lack of mobility limits opportunities
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(Cass et al. 2005; Lucas 2012). Exclusion include unemployment, poverty, lack of education, isolation, community exclusion, etc. (Cass, Shove, 2005).
There is an inter-relationship between job accessibility and low-income residents, transport and gender with a problem of women who tend to combine multiple tasks which are spatially spread, ethnic minorities, youth and the elderly. Thus, lack of transportation service prevents people from going to doctor's appointment, meeting with family and friends and job opportunities and all of these factors can lead to social exclusion. The problem of social exclusion is multi-dimensional and caused by personal factors such as age, gender, race, disability, education, etc.
2.2.2. Measures of equity in transportation planning
Transportation gives people an opportunity to access desired places, goods, services and activities.
It is one of the factors helping define where people can live, thus transportation refers to the equity as the fairness with which opportunities are distributed (Litman, 2007). However, it is difficult to evaluate transport equity because of different types of equity impacts. Moreover, transportation equity could be evaluated with different priorities and various respects (respects to need and ability and with respect to income).
Two general categories of transportation equity are distinguished by Litman: horizontal equity and vertical equity (Litman, 2007). Horizontal equity (fairness or egalitarianism), according which individuals or groups of individuals are considered equal in their ability to receive equal resources.
Services are provided equally regardless of factors of need and ability such as race, gender, income, etc. The aim of such distribution is to provide services to the maximum number of users. Vertical equity (as social justice, environmental justice) this category distributes resources between individuals who are different in their abilities and needs and favors disadvantaged groups based on social class or specific needs. The goal of vertical equity perspective is to provide service for those with the greatest “need”, e.g. people without cars, or people with specific demography, such as low- income, youth, ethnic minorities (Litman, 2007).
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Transport equity contains a number of impacts (costs and benefits), for example, public services, service quality, user benefits, and economic opportunities that could be measured in different ways with regard to various units used for comparison. Therefore, the argument suggests that for analyzing equity impacts it is necessary to understand the impact of different measurement units.
For example, horizontal equity should be based on per capita (per adult/per household/per vehicle) comparison with adjustment to differences in user needs and abilities for vertical goals. However, for vertical types of equity it is more complicated and measure should be focused on travel expenses. Moreover, equity evaluation requires categorizing people to identify who is transport disadvantaged (Lucas, 2012; Wu and Hine, 2003). The categorization is multifaceted and categorizes people to demographic groups, income classes, abilities and disabilities, geographic location, travel mode choice and trip type.
There is no single correct way to assess transportation equity (Litman, 2013). The literature examples suggest that the best way to evaluate equity is to consider several perspectives, impacts and methods of analysis because the selection of variables can significantly affect results.
It is essential to select variable indicators in order to measure equity, Todd Litman, 2013 described five equity objectives and their indicators. Horizontal equity treats everybody equally, meaning that policies and regulations, service qualities are spread equally per capita, and individuals bear all costs of the travel. Vertical equity has other indicators, progressive with respect to income; disadvantaged people can get transportation benefits and service that provide adequate access to necessary activities, as employment, school, and medical service.
Transportation equity can be difficult to evaluate because of a variety of equity types, effects and ways of measuring the impacts. However, the analysis is important because equity concerns could influence transportation planning decisions. Different approaches can be used to quantify vertical inequity. The Gini-index used to quantify inequity by Currie, (2011) with income consideration, however this measure needs adjustment that reflects other factors, such as people mobility and
9 abilities (Litman, 2013). For this thesis the Gini index is used to quantify the transportation disadvantaged population.
2.3. Public transportation dependency
2.3.1. Perspectives on public transport dependency
The growing interest in the issue of transport disadvantage and how it relates to social exclusion stimulated studies that show the inter-relationship between poverty, transport disadvantage, access to life essential activities and services, and transport related social exclusion (Kenyon S, Lyons G,
2002; Lucas 2012; Currie G, Zed S, 2007; Delbosc A, Currie G, 2011). The concept of transport disadvantaged population has grown from the fact that traditional transportation planning methods usually aimed to satisfy travel demand and do not take into consideration social-economic aspects
(Hine J, Mitchel F, 2001). However analysing public transport disadvantage and social needs together with transport provision are essential in order to see where the system can be improved
(Lucas K. , 2004; Fainstein, S, 2005; Currie, 2009).
2.3.2. Public transport disadvantage measure
Public transport disadvantage measure is recognized as a complex multi-dimensional characteristic of location and individuals. The concept includes a set of individual characteristics related to location access, access to mobility, and personal circumstances, such as physical and social characteristics that could limit personal access (Delbosc, et al, 2011, Lucas 2004).
First, transport disadvantage measure is related to location analysis. This measure requires a determination of travel time, cost and distance to key life opportunities such as employment, medical centres, shops, education centers and social networks (Church A, et al., 2000; Schonfelder
S, Axhausen K, 2003; Dodson J, et al. 2006). However, taken alone this system-based approach to travel provision misses the population needs and different circumstances of participants that could influence the quality of access (Delbosc, 2011).
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The second parameter of transport disadvantage is a mobility based measure that considers a set of indicators including the level of car ownership and public transport service (Hine, 2004; Hurni,
2005 and Currie, 2009). Thus individuals without access to a car (Clifton K, Lucas K, 2004) and poor public transport provision are considered as transport disadvantaged (Currie, 2009).
A third aspect is related to socio-economic aspects and specific groups of people and their needs.
Those with physical or mental impairments (Casas, 2007), the elderly (Rosenbloom S, Morris J,
1617), (Siren, 2007), unemployed youth (Currie, 2007; Hurni, 2007), single parents (Delbosc A,
Currie G, 2011), people with low-income, or with cultural and language barriers, etc (Litman,
2013). All of this refers to difficulties that prevent people from life opportunities and full participation in social life.
The literature provides examples that consider these aspects both separately and combined. Lucas
(2004), proposed an approach to combining transport disadvantage and accessibility assessment.
She argues that policy makers should take into account individual circumstances, interactions between people, activities that they need and would like to attend and the transport options that they have. She claims that transport and social disadvantages interact and they cause transportation poverty that leads to inaccessibility to essential activities and thus to social exclusion. Therefore, both a social disadvantage index (income, employment status, skills level, health problems, poor housing) and transport disadvantage indices (car ownership, poor public transport services, high cost of fares, lack of information, fear of crime, etc) should be taken in account together (Currie,
2010, Lucas, 2004).
Thus there are three main forms of disadvantage, distinguished in literature, that include:
Locational disadvantage – spatial equity;
Transport disadvatage – accessibiltiy and mobility equity;
Social disadvantage – reflect to socio-economic equity.
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Immobility
Locational disadvantage Transport disadvantage
Exclusion Social disadvantage
Figure 1 Disadvantage forms
Figure 1 represents three dimentions of disadvantages together, showing potential of their interaction. However there is an argument in literature as some areas with social disadvantages have good transportation provision and some transport disadvantaged areas have quite high socio- economic level, thus it is not a rule that areas with poor public transportation are linked to other forms of disadvantages (Lucas, 2004). Nevertheless two factors accured together, such as transport disadvantage and location disadvantage are significantly constrained by lack of mobility.
2.3.3. Transport disadvantage indicators
Historically disadvantaged people were originally associated with lack of income, but more contemporary perspectives define the inability to participate in economic and social and cultural life as disadvantage as well. Similarly, factors that linked to lack if income, lack of employment, access to activities are also couse social exclusion and considered as disadvantages (Currie, 2011).
Transport disadvantage is a multidimentional construct, however different researchers consider different factors. Some works focus on spatial and locational disadvantages such as disadvantages through transport system distribution or lack of it, urban form, and cost of traveling. Literature also identified a broad range of factors related to people who likely have access and mobility difficulties.
Most literature highlights low income as a major disadvantage factor. Also lack of carownership is a common factor. Thus it is considered that low income households without cars is a severely transport disadvantaged group. However some car owners may suffer transport disadvantage
12 because their car became a heavy financial burdain. Moreover, older people, single parents, women, young people, ethnic minorities, disabled and unemployed are likely to be representetives of the low-income groups and also likely to lack of car ownership. The following Table 1 shows different typical groups of people that are defined as transport disadvantaged (Currie, 2011).
Table 1 Groups of people typically defined as transport disadvantaged Group Source research literature Clifton and Murray and Dodson et Wixey Hurni Currie Lucas (2004) Davis (2001) al. (2004) et.al. (2007) (2004) (2005) No/limited car access + + + Low income + + + + Women + + Elderly + + + + + Single parents + + Minority ethnic groups + + + + Youth + + + + Disabled + + + + Unemployed + + + + Beneficiaries + Outer-urban dwellers + Shift workers + Parents travelling with + Children Students +
2.3.4. Transportation “Need” measure approach
“Transportation is a basic need and fundamental to quality of life” Susan S. Fainstein, 2005(p.284)
Transportation „need‟ measure is an analysis technique developed and applied in a number of studies of Currie (2004, 2009, 2010) in an Australian cities. The index was developed in order to identify areas with high need of public transportation service. The weighting approach was used to combine average household income, unemployment rates, and average family size. The paper of
(Currie, 2010), outlines combination of measures of public transport supply and needs (social disadvantages).
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The „need‟ measure proposed by Currie is a combination of spatial distribution of social disadvantages together with transport disadvantages using recent census data Table 2. He created a
'needs gap' index that spatially compares locations of public transport supply and service quality with demand, that was estimated from socio-economic factors. The measure consists of weighted indexes of social and transport needs that combines characteristics of income, employment status, car ownership and health together (Casas, et al., 2007, Currie, 2009, Currie, 2010, Delbosc and
Currie, 2011).
Table 2 Transport need indicators and weights applied (Currie, 2007) Need indicator Weight Adults without cars 0.19
Accessibility 0.15
Persons aged over 60 years 0.14
Persons on a disability pension 0.12
Low income housholds 0.10
Adults not in the labour force 0.09
Students 0.09
Persons 5-9 years 0.12
One parameter that is not available from the Census is accessibility. The accessibility measure used for this study is based on the straight line distance to Melbourne central business distric (GPO) from the CCD centroid (Currie, 2007). All other indicators are taken from the available census. Applied weightings are derived from an analysis of travel survey accounts since they relate to these census indicator groups. Weighting that is applied to indicators are based on the degree of low trip making
(Currie, 2007). A score for each need is identified by standardisation of each value of applied need indicators. Standardisation based on relationship between score and highest value of this indicator.
Each standardised value is weighed and reset the scores between the values 0 and 100. The final need index is produced by adding together the weighted standardised values. Then measures are
14 aggregated and a comparison is made between transport needs and public transport provision in order to identify spatial gaps between needs and supply, in order to show where transport system improvements should take place.
The structure of the index proposed by Currie was used for Australian cities and later applied for
Palermo (Italy) (Amoroso, et al. 2010). The methodology was used for Santiago de Cali (Jaramillo
C, 2012), however with modified weights more in accordance with its different socio-economic conditions.
2.4. Summary
This chapter has reviewed a broad range of literature related to accessibility, social equity and various forms of disadvantages.
Accessibility has various of meanings and could be defined in numerous ways. The literature distinguishes two forms of accessibility: person accessibility and place accessibility. Different measures have been developed to measure accessibility - infrastructure-based, location-based, person-based, and utility-based. Accessibility improvement aims to reduce the total travel cost needed to access destination. Accessibility indicators are consisting of land use system and transportation system together; also accessibility is an essential yardstick for each of the parts of sustainable development: economic progress, environmental quality and social equity. The degree to which public transportation users are disadvantaged relative to car users could be measured using accessibility gap analyses.
Equity refers to how „benefits and burdens‟ are distributed over members of society and whether this distribution is fair and proper. Two general categories of transportation equity are distinguished by Litman: horizontal equity and vertical equity.
Public transport disadvantage measure is recognized as a complex multi-dimensional characteristic of location and individuals. There are three main forms of disadvantage, distinguished in literature, that include:
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Locational disadvantage – spatial equity;
Transport disadvatage – accessibiltiy and mobility equity;
Social disadvantage – reflect to socio-economic equity.
Transportation „need‟ measure was developed by Currie in order to identify areas with high need of public transportation service. People who experience transport disadvantage are constrained by lack of access to opportunities which prevent people from access to services, markets, leisure activities, thus significantly affect socio-economic well-being and cause social exclusion.
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3. Research question and objectives The goal of this research is to analyze how equitable is accessibility using public transportation in the Beer-Sheba metropolitan area.
This research is concentrated on the development of equity indicators for accessibility which could be used to assess and evaluate transportation and land use policies. In order to achieve this goal, the research will identify most suitable accessibility indicators, and what will be the effects of the current public transportation system. After defining accessibility indicators, a GIS tool will be employed to analyze transportation based accessibility to measure the equity of the transportation system at every location and for different hours of the day (Benenson I, Martens K , 2010). Hence, equity impacts on accessibility will be evaluated. Having developed the approach of the analysis tool through the work on the Tel-Aviv Metropolitan Area, the project proceeds to show how it can be used to evaluate the transportation system and land use in the Beer-Sheba region.
Understanding the study question requires putting in several layers of context. The layers include:
1. Understanding the state of accessibiltiy in the area
2. Understanding how equitable is the state
3. Understanding the demographic and geographic context – who is in need of public
transportation and who will benefit out of improvements?
4. Understanding what are the relationship between accessibility and dependent population?
This thesis is organized into five sections: first, a literature review that covers accessibility definitions and transportation studies on equity. This is followed by a context that outlines the background of the study area Beer-Sheba Metropolitan area, and a description of the methodology and data collection process. Next the data organization process is described. Last, results are presented together with the conclusions and the discussion.
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4. Research methodology The methodology purpose is to develop and represent the measure that identifies the differences in level of accessibility together with a measure for social transportation need for each of Beer-Sheba region TAZ and then to find and compare gaps in accessibility level and transport need. The following section outlines the data collection process, data sources and calculation methodology.
The description of accessibility, the developed methodology for its calculation and the equity analyses, together with the transportation dependency will give the complex picture of the location to understand the context of the whole project. The chapter describes all the components of the methodology separately and concludes with how they all fit together.
4.1. Accessibility
4.1.1. Definition and calculation approach
Geurs defines accessibility as: “The extent to which land-use transport system enables individuals to reach destinations by means of transport modes” (Geurs KT and Ritsema van Eck JT, 2001).
Accessibility gathers together all three components of the transportation system:
a) Land-use (distribution of jobs and activities, population density and distribution of socio-
economic characteristics of population);
b) Transportation network (distribution of transportation, mode availability, cost, time-table
and road configuration);
c) The demand and benefits that individuals can gain out of travelling from home to particular
destinations.
The transportation system changes and adapts, reciprocally, however each component of the system changes and adapts at a different time scale. The land-use system involves the management and modification of natural environment into built environment and involve many arrangements and activities thus it is the slowest part of the transportation system – it takes a period of years, even decades for it to respond to demographic or infrastructure change – therefore for this research it is
18 assumed that it is relatively constant. Individual utility as an outcome of users‟ adaptation is a factor that changes quickly– and to a great degree is not predictable. Thus, the research concentrated on accessibility as representing users‟ potential movement choices, and not necessarily on predicting their actual movements. The transportation system is a directive parameter, which in a major way is a factor that controls the whole system.
Accessibility depends on transportation mode and can be calculated for different categories of opportunities. The measure of accessibility for this research is based on an estimate of the travel time between origin and destination that is defined for a given transportation mode: public transport and calculated for all the job opportunities in the region. The approach is based on that developed and applied by (Benenson I, et al., 2011).
Measuring public transport (bus) travel time
This study captures estimated transportation travel time to all job opportunity locations for every person in the region. The modeled commute time is very detailed and includes walking time from the place of origin to a bus stop, waiting time for the bus, travelling time of the bus, time to transfer to another bus, waiting time and traveling time in the bus, plus another (optional) transfer and travelling time in additional bus plus walking time from the final stop to the destination.
The measure of accessibility that was taken for this study measures the number of destinations of interest (potential jobs) that are available for a person, within a reasonable time frame (between 15 minutes to one hour) using public transport.
An ideal estimate of transport accessibility is an origin-destination (OD) travel time, or travel cost, however the actual number of individual origins and destinations is very high. Therefore to estimate the outcome of OD-travels, Mode Access and Mode Service areas are employed (Benenson et. al.
(2010).
Mode Access Area ( : all the destinations that could be reached using a particular mode
(M) from origin (O) within particular time frame (t).
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Mode Service Area ( : all the area that is served by a particular destination (D) using a particular mode (M) within a particular time frame (t).
Given origin O and destination D, considered:
Public Transport Access Area
Public Transport Service Area
4.1.2. CTGraph – a GIS-based tool for measuring accessibility
The following section describes what CTGraph is and how it is used to calculate and map access and service areas in a metropolitan area, then how these results are further analyzed to calculate subsequent social inequity.
CTGraph is the program developed within ArcGIS (ESRI, Redlands, CA, USA, Copyright © 1999-
2010 ESRI Inc) environment to implement the framework, described above. CTGraph is a 3rd generation application of the previous Urban.Access and AccessCity applications (Benenson I,
Martens K, Rofe Y, 2010).
Originally AccessCity was developed as a tool that merges components of spatio-temporal information for estimating accessibility in various ways. The application is used to construct maps of car and transportation access and service areas for any chosen time from/to any chosen location in a metropolitan area. Thus it allows calculating car-based and transportation-based accessibility to different types of land uses, based on the access/service areas. The application produces accessibility maps for certain origins/destinations. The tool has been used to calculate and compare accessibility by public transport and car for Tel-Aviv Metropolitan area, (Benenson I, Martens K,
Rofe Y, 2010). The main advantage of this version is an increase in the level of spatial resolution and computation speed.
CTGraph works at the resolution of the individual buildings, bus stops and lines and thus processes large amounts of GIS data. In a typical metropolitan area there are usually between 100,000 –
1,000,000 buildings and between several hundreds and thousands of public transport lines. The tool
20 has been developed and applied to the Tel-Aviv metropolitan area, which has a population of 2.5 million people, 250,000 buildings and 300 bus lines. This results in (1-10 billions) OD pairs (http://media.mot.gov.il/PDF/HE_MADAN/accessibilityTel-Aviv.pdf). CTGraph uses the street and public transport route networks. In this thesis we applied the public transportation accessibility calculation of CTGraph, to the Metropolitan Area of Beer-Sheba which has 142,528 buildings and 136 bus lines. All available information is fully exploited – traffic directions and speeds and explicit position of PT lines and stops to estimate accessibility by car and by public transport. Bus timetable data is a critical non-spatial component required to estimate accessibility by public transport. Activities are estimated based on spatial data of job distributions, socio economic indicators, car ownership and land use composition.
4.1.3. Data collection and data construction for accessibility measure
The following describes the data collection and data processing methods that are required for
CTGraph application in order to build the model for accessibility measure.
Data sources:
The model is based on information received from the following sources:
Public transport network attributes (GTFS from July 2014) from Google Transit data
https://developers.google.com/transit/
GIS-based layers from Mapa (GISrael Digital Mapping), Updated: (March, 2014)
Israel Bureau of Statistics (CBS)
Google Transit Data
The General Transit Feed Specification (GTFS) is a data format developed by Google (2006).
Google established a unified specification, GTFS in order to encourage as many agencies as possible to participate. It defines a common format for public transportation schedules and
21 associated geographic information. This format enables public transport agencies to publish relevant information considering their transportation data and developers/researchers use this information for applications that are based on this data (Google Transit 2010). As an open-source the data offers researchers the opportunity to conduct public transportation analyses in a more convenient manner.
A GTFS feed is a group of comma separated values (CSV files) that is organized in a ZIP file and contains public transport system schedules, stop coordinates, transportation operators and route information. Each file contains a particular part of the information – stops, routes, trips, agencies and schedule data. The file itself is published by transportation agencies and is available free on
Google website, describing all the details in the GTFS reference.
The structure of specifications includes data that is summarized in Figure 2 and in Table 3 below:
Figure 2 The general transit feed specification data
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Table 3 The general transit feed specification data description
Data table Description
Agency File contains data about one or more transportation agencies that provide the data in this feed.
Stops File contains data about individual locations where vehicles pick up or drop off passengers.
Routes File contains data about transportation routes. A route is a group of trips that are displayed to riders as a single service
Trips File lists trips for each route. A trip is a sequence of two or more stops that occurs at specific time.
Stop times File lists the times that a vehicle arrives at and departs from individual stops for each trip.
Calendar File defines dates for service IDs using a weekly schedule. Specify when service starts and ends, as well as days of the week where service is available.
Calendar dates File lists exceptions for the service IDs defined in the calendar.txt file. If calendar_dates.txt file includes ALL dates of service, this file may be specified instead of calendar.txt.
Shapes File defines the rules for drawing lines on a map to represent a transportation organization‟s routes.
Infrastructure network data
Road network is required for constructing accessibility model in CTGraph. The data is taken from
“Mapa” database (GISrael Digital Mapping) – mapping and publisher Ltd
General view of the CTGraph input and output database:
The raw data that is provided by Google Transit and Mapa needs to be processed and reorganized for the CTGraph input database. The structure and requirements of the input database are presented below in Tables 4 - 9 and Figures 3 – 4.
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Table 4 - A Route is the service on a transportation line from one end to the other. A typical transportation line consists of two routes: to and from. A circular route (one route per transportation line) is also possible.
Table 4 Routes
OBJECTID ID of the GIS feature ROUTEID ID of each ROUTE
ROUTENAME The origin and destination of the route
MODE 45 - TRAIN 51- REGIONAL BUS 52 – CITY BUS COMPANY Name of the transportation agency operating the route
MODENAME Name of modality
STARTSTOP STOPID at which this ROUTE begins
ENDSTOP STOPID at which this ROUTE ends
INTERVAL_M The average interval in minutes between two buses/trains from 7AM to 9AM
INTERVAL_E The average interval in minutes between two buses/trains from 16AM to 18AM
INTERVAL_R The average interval in minutes for the rest of time outside peak hours
STARTTIME The time of the first depart of this route at it STARTSTOP
ENDTIME The time of the last depart of this route from the STARTSTOP
Table 5: Route segments - A route segment is a fraction of a route between two consecutive stops
Table 5 Route segments
OBJECTID ID of the GIS feature STARTSTOP STOPID at which this ROUTE begins
ENDSTOP STOPID at which this ROUTE terminates
LAST_TRAVTIME_M The morning peak travel time to pass this ROUTE_SEGMENT
LAST_TRAVTIME_E The evening peak travel time to pass this ROUTE_SEGMENT
LAST_TRAVTIME_R The off-peak travel time to pass this ROUTE_SEGMENT
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ROUTE_ID The ROUTEID of the route that a segment belong to
MODE Modality 45 - TRAIN 51- REGIONAL BUS 52 – CITY BUS FROMSTOP The STOPID where route segment begins
TOSTOP The STOPID where route segment ends
Table 6: Stops - The Stop is a point on a Route where passengers can board or disembark a bus.
Table 6 Stops
OBJECTID ID of the GIS feature STOPID ID of each STOP STOPNAME Name of this STOP NODEID Geometry of stops and nodes does not match STOPTYPE Bus, train
Figure 3 presents an overview of layers of bus lines and stops for Beer-Sheba Metropolitan area and
Beer-Sheba city. Each transportation stop is located with its line
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Beer-Sheba Metropolitan area
Beer-Sheba city
Figure 3 Overview of layers of bus lines and stops in Beer-Sheba Metropolitan area and Beer-Sheba city. Each transit stop is associated with its line
Table 7 – Exact timetable by routes and route start time
Table 7 Schedule
OBJECTID ID of the GIS feature ROUTEID ID of the Route STARTSTOP_1 The STOP ID where Route starts ENDSTOP The STOPID where the Route ends ROUTESTARTSAT The start time of the route FROMSTOP The STOPID of the FROMSTOP of the Route Segment TOSTOP The STOPID of the TOSTOP of the Route Segment EXACTARRIVALTIME Arrival time at the FROM STOP EXACT_DURATION The duration of the trip FROMSTOP to TOSTOP
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Table 8 – Nodes - junctions of the road network which can include stops but not required
Table 8 Nodes
OBJECTID ID of the GIS feature NODEID ID of each NODE X X-coordinate Y Y-coordinate JUNCTYPE No data JUNCMODE No data
Table 9 - Segments of transport infrastructure between two consecutive Nodes
Table 9 Links
OBJECTID ID of the GIS feature LINKNR Official code (number) designating the street NAME Name (Primary) or Number of road, an interval belongs to, according to the standard enumeration. DIRECTION Direction of traffic 1 from A to B 2 from B to A 3 two-way 4 no traffic LENGTH The length of the link ANODE beginning of the interval BNODE end of the interval LINKTYPEAB Indication of which kind of Transportation Is allowed LINKTYPEBA Indication of which kind of Transportation Is allowed SPEEDAB Estimated or measured maximum speed for motor vehicle on relevant road segment SPEEDBA Estimated or measured maximum speed for motor vehicle on relevant road segment
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Beer-Sheba Metropolitan area
Beer-Sheba City
Figure 4 Overview of road network in Beer-Sheba Metropolitan Area (a) and the city of Beer-Sheba (b)
Table: Buildings - Polygons representing buildings, each of them containing information on possible activities (jobs, residences, etc.) and population number is necessary for constructing the origin/destination layer. In order to estimate these parameters, the number of jobs and population per TAZ is distributed over buildings according to their type and size within each TAZ. The type of building usage is taken from the data received from CBS. For buildings with multiple uses lower levels are assumed as businesses while upper levels as residential. The data is taken from Google
Street Map.
The expected database output:
Given a trip start time CTGraph software generates a mappable table that contains numbers of activities for every building at a given time resolution. Based on this table various tables of accessibility are generated according to the user‟s demand. Moreover CTGraph constructs a table of
28 transfers based on the layers of stops and lines and on the table of departure and arrival times. This transfer table contains all possible pairs of stops which are less than 500 meter air distance apart.
The table is used to estimate possible travelers' transfers between transportation lines. The result of
CTGraph is a series of accessibility maps.
Data presentation:
For evaluation of accessibility by public transportation a high resolution view is essential as was demonstrated in the research for Tel-Aviv Metropolitan Area
(http://media.mot.gov.il/PDF/HE_MADAN/accessibilityTel-Aviv.pdf). The reason for this is that the walking and waiting time at the bus stops takes significant share of the total door-to-door travel time by public transport. Thus an analysis at the TAZ level will ignore the differences in accessibility experienced by different people within the same TAZ.
For representation of the outcome of the accessibility measure on a building level, 'Thiessen
Polygons' GIS tool was used. The tool allows attaching center of the polygons (buildings) to
Voronoi polygons, to guarantee continuous coverage of the area (see illustration below, Figure 5).
Figure 5 Illustration of Thiessen Polygons
4.2. Equity in transportation planning
4.2.1. Measuring inequity using Lorenz curves and Gini index
Lorenz curves are a graphical representation of the cumulative distribution function of wealth across the population often used in economics (Lorenz, 1905). Lorenz curves can be applied not just to income but to any quantity that can be distributed across a population. They have been applied in a range of disciplines, from studies of biodiversity to business modeling and transportation (Fridstrom L, 2001). Figure 6 is an example of a Lorenz curve for income
29 distribution. The blue line represents a population of perfectly equitable income distribution; the red line represents an inequitable distribution of (e.g. 70% of the population shares about 25% of the population‟s income).
Figure 61 Representation of the Lorenz curve
It is important to note that Lorenz curves do not imply that perfect equity is possible or even desirable. The Gini index (Gini, 1912) is a statistical measure of the distribution of the attribute
(e.g. income). Whereas the Lorenz curve is a visual representation of inequality, the Gini index is a single simple mathematical metric representing the overall degree of inequality. Graphically, it is the ratio of the area between the line of equality and the Lorenz curve (area „„A‟‟ in Figure 6) divided by the total area underneath the line of equality (A + B in Figure 6). The lower its value the more equal is the distribution.
Using the Gini index, the distribution of two different Lorenz curves can be mathematically compared. Statistically, Gini is a measure of equity variance computed as half of the Relative Mean
Difference of the value of attribute Y, between two randomly chosen objects. As such, Gini is one of the measures of variation and is independent of the average value of the distribution. For a rank- ordered sample S of the population i.e. < , the Gini index is computed as:
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∑ ( ) (1) ∑
The Gini index is always between 0 and 1. A value of 0 implies complete equality whereas a value of 1 suggests complete inequality. The lower the value the more equal is the distribution in question.
For any attribute Y the Lorenz curve and Gini index are simple to compute based on the distribution of Y in the population and they are independent of scale i.e. they can be based on the value of Y for individual objects (e.g. buildings) and for the aggregates of objects (e.g. average values of Y over statistical areas). The Lorenz curve and Gini index are proposed to be constructed for transport dependent population.
4.3. Public transport dependency measure
The following section discusses measures for estimating the vertical equity (discussed in Literature
Review above) of accessibility based on dependence on public transport. This chapter aims to identify a measure for the relation between social disadvantage and transportation dependency. The method for calculating the „need‟ index developed by G. Currie (2004), was adopted for this approach. The analysis was conducted at the level of transportation accessibility zone (TAZ), the smallest level where census data can be collected. For Beer-Sheba Metropolitan Area there were
156 TAZ in 2010.
4.3.1. Transportation need index calculation for Beer-Sheba Metropolitan Area
Transportation need is defined as the number of people in a given geographic area who are likely to require public transportation services. The measure is the index of social characteristics related to transport disadvantages, for each of the TAZ of the Beer-Sheba Metropolitan Area. For the calculation of transportation needs index, the method proposed by Currie, was taken as a baseline, modified to the available data and socio-economic conditions that are relevant to Israel. The index itself is a weighed summary of transport and social disadvantage indicators within the area of
31 analysis. The final index consists of the following need indicators: adults without cars (expressed by the difference between the number of adults and the number of cars in the TAZ), persons aged over
65 years old, persons with disabilities, low income households (below median income), persons that are unemployed (as defined by the CBS = percentage of labor force that is unemployed aged over
15 that) and persons between the ages 10-18.
Lacking a detailed study of the likelihood of taking public transportation based on socio-economic characteristics, we assumed that the highest factor among all the factors involved is the lack of access to a car by persons in the driving age. Therefore, we assigned this factor the highest weight and assigned all the other factors an equal weight.
Weighting was applied in the following way: 25% for adults without cars with the balance divided equally among the other groups (15%) - see Table 10.
Table 10 Need indicator weights
Need indicator Source Weight
Adults over 18 without cars Census, 2010 0.25 Persons aged +65 Census, 2010 0.15 Persons with disabilities Census, 2008 0.15 Low income households (below median income) Census, 2008 0.15 Persons not in labor force Census, 2010 0.15 Persons between the ages 10-18 Census, 2010 0.15
The measure of indicator is considered as a total need indicator – the weight of indicator is a size of population in a TAZ. The total index is calculated as the weighed sum of the indicators for all chosen factors within each TAZ. Relatively disadvantaged areas are those with a higher index.
The formula for calculating the index score is as following:
= * + * + … + * (2)
Where:
– Transportation Need Index for the transportation accessibility zone under analysis;
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– – standardized indicator
- – are the weights for the indicators
All indicators of transport disadvantage are standardized before they are derived for the single need score for comparison purposes. Standardization involves resetting the scores to values between 0-
100, based on the relationship of each score to the highest value within its set. Then each standardized value is weighed, added together and the need index is calculated. Finally each need score is standardized and receives a score between 0 and 100 for each TAZ. Relatively disadvantaged areas are those with a higher index score and advantaged areas are those with a lower index (Currie, 2004).
Equation for standardization:
SI = (I-Imin) / (Imax-Imin)*100, (3)
Where:
SI – Standardized Indicator
I – Indicator of the disadvantage factor for the district
I min – Minimum value of the indicator for the district
I max – Maximum value of the indicator for the district
4.3.2. Demographic data collection and data construction
The method requires demographic data acquired from CBS. Parameters that indicate persons with disabilities and income of households are taken from the 2008 Census and are only available for localities numbering 2,000 residents and over. For localities with less than 2000 people, the index is taken from the regional council. For recognized Bedouin towns (Hura, Kuseife, Laqye, Ar‟ara-
Banegev, Rahat, Segev-Shalom and Tel-Sheva) vehicle rate is taken from 2007 Census (The Negev
Bedouin Statistical Data Book, 2010). All other indicators are taken from the 2010 Census and are available for all TAZ. However there is not enough available data for the Bedouin unrecognized villages and they are represented as a separate group - marked brown on the map.
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Analyzing „needs‟ is important for identifying gaps where there is a low level of accessibility and a relatively high level of need for transportation. These areas represent inequality and should be taken as special attention areas fur further development. The figures which represent each need separately and the aggregated map are presented in the next section.
4.4 Transportation accessibility in relationship to transportation need index
After the transportation accessibility model and the need index model were generated, the equity of transportation system was assessed by overlaying two datasets. Areas that included higher transportation need than accessibility level were considered inequitable, whereas areas with higher or same level of transport accessibility than need index were considered as equitable areas (Accola,
2015). Since accessibility was calculated on the level of single buildings and transport need index on the level of TAZ - the inequitable areas were identified on a level of TAZ also, and accessibility was taken as an average within TAZ. Maps for inequitable TAZ, with different levels of accessibility are, presented individually in the Results chapter.
4.5. Summary
This research employs three components within the research that have been combined together in order to provide the results for the study, these include:
Public transportation Accessibility analysis, using the GIS-tool CTGraph.
Equity analysis of Public Transportation Accessibility across the city of Beer-Sheba and
across the metropolitan area.
Developing a public transport need index that provides a base for evaluating transport
dependence, constructed based on demographic indicators from the recent census.
Calculating the weighted accessibility score related to public transportation need.
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5. The case study of Beer-Sheba Metropolitan Area
This chapter describes the social-economic and demographic characteristics of Beer-Sheba
Metropolitan Area communities, the role of the city in the region and local transportation services.
The data is taken from Census 2008
5.1 Negev region
The Negev region is 13,000 sq. km, a largely empty territory that covers around 60% of the territory of Israel and is home to about 8% of the country's population. A large part of the region is dedicated to security needs. The Negev region (Figure 7) has been always considered as a development challenge for the country; today‟s Negev is dynamically growing. One of the important modernization projects of the Negev has been the Jewish National Fund‟s Blueprint Negev that aims to bring many Israelis to the area. Recent plans include improvements to the transportation infrastructure, employment opportunities, preservation of the water resources and protection of the environment (Halevi, 2011).
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Figure 7 Map of Israel with Beer-Sheba Metropolitan Area (red color)
Among the major important infrastructure projects is the development of the Cross-Israel Highway, known as Route Six that stretches from the lower Galilee to the northern Negev, which provides opportunity to live in the periphery and work in the center. Moreover development of Israel railways enables residents of the area to reach Tel-Aviv in about an hour.
5.2 The city of Beer-Sheba
Beer-Sheba is one of the ancient cities in Israel, located in the Southern region at the beginning of the Negev highlands. The road network makes Beer-Sheba the gateway to the Negev and everyone who enter the region has to pass through the city (Gradus, 1977). Today Beer-Sheba is the fourth largest rapidly growing urban concentration with nearly 200,000 inhabitants and serves the region's population of nearly 500,000 residents of the Negev. The city is fast growing and has a plan to
36 increase the number of its residents to 450,000-500,000 (Jewish National Fund‟s Blueprint Negev); moreover the master plan goal is to increase the population of Beer-Sheba and metropolitan area to
1 million by 2020.
Beer-Sheba was the first town in the area and takes central position among the other towns in metropolitan area. It offers many administrative and public functions – most of the governmental offices are located in the city and provide the service to the entire region. Since the city is a major transportation junction and gateway to the Negev it is also an administrative, military, civic, commercial and social center for the entire region. In addition, the medical center and the
University are major employment centers.
5.3 The Arab-Bedouin villages in the Negev
Negev Bedouins represent more than a quarter of the population of the Beer-Sheba region (Figure
8). The region cannot be fully developed without including special programs for Bedouins (The
Negev Bedouin. Statistical Data Book, 2010).
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Figure 8 TAZ, Military Zones and Bedouin settlements in Beer-Sheba Metropolitan area
The State of Israel has established, since 1960, seven urban centers for the Bedouin population in the Negev. Today, about half of the population – 190.000 is settled in these towns and the other half lives in unrecognized settlements and eight towns in the process of recognition. The data of the
Central Bureau of Statistic (CBS) does not include information on the unrecognized settlement inhabitants; moreover the information on the recognized settlements is partial and unrepresentative
(The Negev Bedouin. Statistical Data Book, 2010). Due to this factor other sources than the CBS were used to collect relevant data on the recognized Bedouin towns. This was included in the model in Chapter 4 in the calculation of the transportation dependency.
The differences between Bedouin society and the rest of the Israeli society is that up to 50% of the society consist of children under 14 years old and the participation in the work force is less than
27% compared to 57% in Beer-Sheba. Distance to social services, for instance to the nearest
38 primary school is 5 km away, for more than 13% of the households. (The Negev Bedouin Statistical
Data Book, 2010).
Population in the unrecognized localities:
Arab communities that are not officially recognized by Israeli authorities mean that these communities do not have local government or public services, and lack basic infrastructure such as water and electricity. The Central Bureau of statistics estimates this number to be 56840 residents
(CBS, 2008). The unrecognized villages do not appear in the official maps of Israel and most of the information about the villages and their population is not available or unrepresentative. Those localities do not have any jurisdiction unit that is responsible for providing services such as health and education; therefore those locations lack basic infrastructure and utilities, such as running water and electricity. (The Negev Bedouin Statistical Data Book, 2010).
5.4 Socio-economic profile of Beer-Sheba Metropolitan Area
According to the Central Bureau of Statistic (CBS, 2008), the total population in Beer-Sheba
Metropolitan area is 536.500 people.
People with disabilities –11.7% of the population have great difficulty and cannot perform basic activities like walking around the house or walking up and down the stairs.
Labor force: 55.4 % of population is participating in the annual civilian labor force and 95.2% of them are working.
Means of getting to work: 41.1% of the people get to work by private or commercial vehicle as a driver and 6.3% as passengers. 15% use public bus and 21.3% by transportation organized by place of work.
47.1% of households have at least one vehicle and 9.5% have two vehicles and more
Age distribution: 8.2% of population is older than 65, young population, 10-18 years old - 17.6%
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The rate of motorization: The rate of motorization in the data of 1997 was 210 vehicles per thousand residents in Arab communities. Beer-Sheba level of motorization is 218 vehicles per thousand residents, when rate in Israel is 315.2 vehicle per thousand people. These figures are lower than the national average of 283 vehicles per thousand residents in the same year. There is a difference in the level of motorization among the communities in the metropolitan area, depending on the socio-economic characteristics. Particularly high level of motorization is found in Omer,
Metar and Lehavim (over 350 vehicles per 1,000 people). Low level of motorization is found in the
Arab communities, Kuseife, Rahat, Ar‟ara-Banegev, Segev Shalom and Tel Sheva (less than 100 vehicles per 1,000 people) (The Negev Bedouin Statistical Data Book, 2010).
Public transport services in Bedouin recognized localities: only residents of Hura, Kuseife and
Rahat have access to regular bus services to and from the city of Beer-Sheba: one line for each of the locations. There are no lines that enter the rest of the localities, but they do stop at nearby intersections or at the entrance road to the locality (The Negev Bedouin Statistical Data Book,
2010).
5.5 Local services
The principal form of public transportation in Beer-Sheba and the region are buses, mainly operated by Metrodan Beer-Sheba. The company founded at 2003 and operating 23 lines in the city
(Metrodan, 2003)
Egged and Metropolin companies operate lines to and from Beer-Sheba city, all intercity buses run through the Beer-Sheba Central Bus Station.
Israel Railways operates two stations in Beer-Sheba: Beer-Sheba Central railway station and Beer-
Sheba North and continues to Dimona and the Dead Sea Factories.
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6. Results and Analysis
Results from the accessibility model demonstrate that most of the jobs in the metropolitan area are accessible by public transportation within 45 min in the morning hours. The city of Beer-Sheba shows the highest level of accessibility. The level of accessibility gradually decreases towards the edges which is natural due to the big distances and large open spaces.
Outcomes from the data analysis of the transportation need index show that the TAZ with the highest transportation need in Beer-Sheba Metropolitan Area are located towards the edges of the regions as well. This TAZ have a large number of people with disabilities, unemployed, young people and people above 65 years old.
6.1 Accessibility in the Metropolitan Area of Beer-Sheba
6.1.1 Accessibility for the city of Beer-Sheba
Figures 9 – 11 and Table 11 show the results of the public transportation accessibility measure according to the methodology described in previous chapters. The accessibility index is calculated for 15, 30 and 45 min trip and indicates available number of jobs for trips originating in Beer-Sheba city with destinations within all the metropolitan area. The calculation was done for the morning hours, for travelers who start their trip on a weekday in the morning, between 7:00 and 8:00.
Maximal aerial distance between the traveler‟s origin (and destination) and a bus stop were set at
400m and maximal waiting time for a transfer was 10 minutes. The index shows that:
Almost 45% of available jobs are accessible for Beer-Sheba residents within a 15 min trip
57.5% of available jobs are accessible within a 30 min trip
67% of jobs are available within a 45 min trip
The accessibility levels are shown in colors blue- green; where green represent more jobs that are accessible. The unit of analysis used in this measure is the single building. All buildings were
41 attached to Voronoi polygon to guarantee continuous coverage of the area (The method is discussed in Methodology chapter above). The results are presented in absolute number of available jobs in a given time threshold.
Figure 92 No. of jobs accessible from Beer-Sheba in the metropolitan area starting between 07.00 and 08.00 in the morning and taking 15 minutes
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Figure 3 No. of jobs accessible from Beer-Sheba in the metropolitan area starting between 07.00 and 08.00 in the morning and taking 30 minutes
Figure 11 No. of jobs accessible from Beer-Sheba in the metropolitan area starting between 07.00 and 08.00 in the morning and taking 45 min
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Table 11 Summary of number jobs accessible from Beer-Sheba in the metropolitan area for 15, 30 and 45 min trip Job Accessibility 15 min % 30 min % 45 min % Total
Number of 68735.64 44.59 88625.34 57.49 103650.86 67.23 154163.76 available Jobs
6.1.2 Accessibility for the Metropolitan area of Beer-Sheba
Figure 12 and Table 12 show the job accessibility for the metropolitan area of Beer-Sheba within 45 min trip. Accessibility index is calculated as average accessibility for each TAZ. The index is grouped into six groups including zero access group, which indicates the following:
Zero level of accessibility is provided to 3,009 residents of the metropolitan area or 0.61%
of the population which represent the edge of the metropolitan area – Tse'elim, Lahav,
Dvira, Shomriya, Revivim. Furthermore in many areas, especially small settlements many
houses are located further than 400 m distance from the bus stop which results
inaccessibility
The very low level of accessibility covers 78,621 or almost 16% of population –Dimona,
Arad, Urim, Mishmar HaNegev, Kramim, Ashalim. This TAZ are situated at the edges of
the metropolitan area and the main reason for their low accessibility index is location, since
many of the job opportunities for these regions are actually located outside of the
metropolitan area and these job opportunities are not included in the calculation. However
there are many people whose using organized transportation provided by employers.
Overall 21% of the population has low level of accessibility: these are Rahat, Kseifa, Ar'ara
BaNagev, Yerucham and Shoval.
Medium level of accessibility is for 13% of population – Ofakim, Netivot, Beit Kama,
Omer and Meitar
High and very high level of accessibility is refers to 56% of population or 240572 residents
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Figure 12 Number of jobs in the Metropolitan Area of Beer-Sheba accessible in less than 45 min by bus
Table 12 Distribution of accessibility index and population for the Metropolitan area of Beer-Sheba
Accessibility score Number of TAZ Population Number %Total 0 Zero 5 3009 0.61 0-20 Very low 8 78621 15.98 20-40 Low 7 105350 21.42 40-60 Medium 7 64293 13.07 60-80 High 15 80259 16.32 80-100 Very high 41 160313 32.59
Total 83 491845 100
6.2. Public transport dependency
The spatial distribution of computed transport need index indicators are shown below in Figures 13-
18, each indicator presented individually. Figure 19 illustrates the spatial distribution of composite need index. The demographic data is available and presented for urban localities in the metropolitan
45
area (cities and local authorities). The grey areas are those where no people live or military bases are located, the brown areas represent places where Bedouin unrecognized villages are sited, since there is no available reliable demographic data for these locations they are marked as a separate group. The indicator weighted by the size of population in TAZ.
One can clearly see that the variables are distributed differently throughout the metropolitan area, and do not necessarily coincide. For example in Rahat – a city that has the highest need score the income index is relatively low however there is a very high number of young population aged 10-18 years old, high level of unemployment and many residents do not have private cars. A very high number of the older population is concentrated in Dimona, and Arad. The share of indices over locations can be seen in Figure 19.
Beer-Sheba Metropolitan area
Beer-Sheba city
Figure 13 Persons 10-18 years old
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Beer-Sheba Metropolitan area
Beer-Sheba city
Figure 14 Persons aged over 65
Beer-Sheba Metropolitan area
Beer-Sheba city
Figure 45 Adults without cars
47
Beer-Sheba Metropolitan area
Beer-Sheba city
Figure 16 Unemployed adults
Beer-Sheba Metropolitan area
Beer-Sheba city
Figure 5 Persons with disabilities
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Beer-Sheba Metropolitan area
Beer-Sheba city
Figure 186 Persons with income below median
Beer-Sheba Metropolitan area
Beer-Sheba city
Figure 7 Public transportation need index in the Metropolitan area
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Figure 19 shows the spatial distribution of the composite transportation need index for the Beer-
Sheba Metropolitan area and the city of Beer-Sheba separately, on city scale (Figure 20). The tendency of relative disadvantage need scores being located in suburbs to the South-East of the city, as well as the North is not the rule - some neighborhoods of Beer- Sheba are also having high levels of transportation need.
Figure 80 Public transportation need index in the Metropolitan area and the city of Beer-Sheba
Results from the data analysis presented above show that the TAZ with highest transportation need are located in densest cities. Areas that have highest levels of transportation need are: Rahat,
Dimona, Arad, and Ofakim. In all of these regions the presence of carless population is high and very high. In Rahat the level of unemployment and young adults is also very high. In Dimona there old people as well. Moreover these regions have many disabled people. The areas with lower levels of need index are also located around metropolitan area, these areas have lower level of transportation need also because they have less density and have less people in need.
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Figure 21 illustrates the share of values of each need indicator factor. The result is presented for the
13 top highest needs TAZ. Usually the most significant factor in determining dependency on public transportation is the lack of ownership of a private vehicle. Elderly people are taking a high percentage of shares too. Although in the case of the two most dependent TAZ: a TAZ in Rahat and
Dimona, these are compounded by pensioners above 65 years old (Dimona) and a high population below driving age in Rahat.
80
70
60
50
40
30
20
10
0
Arad
Hura
Rahat
Laqye
Ofakim
Kuseife
Netivot
Dimona
Yeruham
Tel-Sheva
Segev-Shalom
Ar'arat an-Naqab Ar'arat Beit HaGadi, HaGadi, Klahim… Beit Eshbol, 100 84 67 63 59 42 41 30 27 23 22 21 21
Persons with income below median Persons with disabilities Persons 10-18 years old Unemployed adults Persons above 65 years old Adults without cars
Figure 91 The makeup of weighted Transportation Need Index for the 13 highest needs TAZ
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Figure 22 below shows the Lorenz curves and Gini Indices for the transportation need index and income index. It shows that the inequality of distribution of public transport need is almost the same as the inequality of spatial distribution of income in the Beer-Sheba Metropolitan Area.
Figure 102 Lorenz curves and calculation of Gini index for Public Transportation Need and Income by TAZ
Table 13 present a summary of the individual need indicator of transportation need index using the methodology described above. The need index was assembled according it score; the highest score represents the highest need. The population for each TAZ is also identified.
Table 13 Number of TAZ and population in each category of need index Population Index decile Number of TAZ Population percentage 0 no people 13 0 0
Unrecognized settlements 19 56841 10.36
1 (0.0 - 8.4) 8 35380 6.45 2 (8.5 - 12.6) 9 35040 6.39 3 (12.7 - 15.0) 9 34964 6.37
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4 (15.1 - 18.5) 9 26207 4.78 5 (18.6 - 19.5) 8 23322 4.25 6 (19.6 - 20.2) 8 7750 1.41 7 (20.3 - 20.9) 8 15694 2.86 8 (21.0 - 23.0) 8 42695 7.78 9 (23.1 - 30.2) 8 59803 10.90 10 (30.3 - 100) 8 210990 38.45 Total: 115 548686 100.00
Overall almost 39% of the population in the metropolitan area or 210990 residents have relatively high transportation needs scores which is above the average.
The very high transportation needs score group are scattered all over metropolitan area and covers:
Jewish Development Towns: Netivot, Ofakim, Arad, Dimona. Bedouin planned towns: Kuseife,
Tel-Sheva, Rahat. Part of the city of Beer-Sheba: Part of southern neighborhood Neve Zeev.
On city scale the most dependent neighborhoods are: part of Neve Zeev, Dalet and Vav which represent 32567 residents or 16.7% of the population of the city of Beer-Sheba.
6.3 Results In order to overly transportation need model result with accessibility model result, standardized score (Z-score) is determined. This statistical methodology gives the possibility to understand which value is high and which is low and make the comparison of two different distributions.
To find Z-score the following formula is used:
z=(x-µ)/σ, (4 )
Where µ - mean, x- score, σ – standard deviation
In general the z-test results show (Standard score, Wikipedia):
53
If the z-score is between -2 and 2 (95%) – the distribution is normal
If the z-score is between -3 and -2 or between 2 and 3 this is significantly unnormal
If the z-score is below -3 or above 3 this is highly significantly unnormal (exceptional)
According to this knowledge, we assume that TAZ with z-score below -2 and above 2 will be exceptional, thus this regions are unequal and require further analyses,
Table 14 shows the result of z-score test with z-values above 2 or below -2 in one of the parameters
- need index or accessibility.
Table 14: Z-score test with values below -2 or above 2 in one of the parameters - need index or accessibility index TAZ Settlement Z -score need Z-score accessibility
2131 Rahat 4.98 -0.93
2144 Dvira -0.08 -2.07
2145 Lahav -0.07 -2.07
2146 Kramim -0.07 -2.07
2148 Shomriya -0.10 -2.07
2411 Ofakim 2.66 -0.51
3211 Arad 2.86 -1.83
3212 Dimona 3.97 -1.63
3343 Revivim -0.16 -2.07
3411 Netivot 2.39 -0.66
3442 Zimrat, Tushiya,Kfar Maimo, Shokeda, Shuva, Sa'ad, -0.11 -2.03 Tkuma, Yizre'am
3445 Tse‟elim -0.05 -2.07
The most important finding is that the z-score test did not show extreme cases with exceptional values for both accessibility and need indexes for the same TAZ, however it does show exceptional values separately for each distribution which highlighted and used for the analyses.
54
Figure 23 indicates this highlighted TAZ and Table 15 lists locations with population number, need index and accessibility respectively.
Figure 23 highlighted TAZ
In total 12 TAZ represent 148,454 residents (almost 30% of the population) in the Beer-Sheba
Metropolitan area.
Table 15 highlighted TAZ
TAZ Settlement NEED Accessibil Accessibilit Population index ity Score y Value
2131 Rahat 100 36 37,029 53,095
2144 Dvira 19.95 0 0.00 408
2145 Lahav 20.1 0 0.00 397
2146 Kramim 19.96 1 0.00 192
2148 Shomriya 19.63 0 0.00 653
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2411 Ofakim 63.36 49 50,582 24,198
3211 Arad 66.51 7 7695.54 23,725
3212 Dimona 84.11 14 14386.28 32,559
3343 Revivim 18.53 0 0.00 1,133
3411 Netivot 59.04 44 45,665 8,333
3442 Zimrat, Tushiya,Kfar Maimo, 19.41 1 1278.64 3,343 Shokeda, Shuva, Sa'ad, Tkuma, Yizre'am
3445 Tse‟elim 20.39 0 0.00 418
Total: 148,454
Figure 24 illustrates the relative distribution of public transport need index and accessibility index.
There is no clear link between accessibility and need, rather scores are scattered overall and concentrated in low/average level of accessibility and high need indicators. In red color marked dots that represent 12 TAZ listed above and require attention.
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Figure 114 the relative distribution of public transport needs index and accessibility index
Highlighted TAZ are required further analyses and deeper understanding of the problem and possible reasons of why did the situation there went this way. Every location is unique and has its own cases, for example in Rahat only 6% of population is using public transportation in order to get to work and 61% are going by car (Figure 25). It should be analyzed more (emphasized in the paragraph of 'Future work) why local population do not using public transportation, in this situation survey would be suggested.
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Figure 25Means of getting to work according to the 2008 census in the communities
In case of Arad and Dimona - the situation is different and most of the people are commute to work by organized transportation (Figure 26-27), but what is going on with people who do not work? Do they have enough opportunities to travel by public transportation? - This question also should be explored.
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Figure 26 Means of getting to work according to the 2008 census in Dimona
Figure 2712 Means of getting to work according to the 2008 census in Arad
59
Kramim, Dvira, Shomriya and Lohav - these settlements are part of Bnei Shimon Regional Counsil
(Figure 28)
Figure 28 Bnei Shimon Regional Council
Here is another reason for poor accessibility level: the frequency of lines that go inside settlements in all regional council is low – 1-2 buses in the morning hours, however lines that stop on the entrance to the settlements have higher frequency and just distance makes them less accessible.
60
The same situation is in Shova, Shokeda, KfarMaimon, Tse‟elim and Revivim, the frequency of lines that go inside settlements is low, thus accessibility level is low- Figure 29.
Figure 29 Highlighted settlements
Nativot and Ofakim low level of accessibility are connected to technical errors that were discovered in GTFS file. There is a problem with stop sequence numeration and while route is computed it appears doubled and significantly increase the travel time at some locations, this should be noted by
Egged bus operated company and corrected, because many planning companies are using GTFS as a relying source of transportation data.
Since the analyses of accessibility in the city of Beer-Sheba is done at the resolution of single building, the analyses of TAZ can go further, and point to individual buildings that are most
61 disadvantaged in the reviewed area. Accessibility analysis of the city did not show any region that should be highlighted, thus for the analyses only areas with high need were chosen in order to check accessibility. Figure 30 shows TAZ with highest need.
Figure 13 highlighted TAZ in the city of Beer-Sheba
Below Figure 31 a-d shows the spatial distribution of accessibility index on a resolution of single building for TAZ with high level of need index in the city of Beer-Sheba. This analysis is very visual and can show in high resolution which buildings exactly are in lack of access. We can see on a Figure 31 a-b that Neve Ze‟ev and Vav neighborhoods can be improved at the places where color appeared as blue. This analysis is very precise and can help to choose the places for the new bus station.
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(a) Neve Ze'ev neighborhood, TAZ 1322 (b) Vav neighborhood, TAZ 1234
(c) Dalet neighborhood, TAZ 1234 (d) Dalet neighborhood, TAZ 1234 Figure 14 Spatial distribution of accessibility per single building.
6.4 Summary
Results shown in this chapter highlight 12 TAZ that require analyses; it represents almost 30% of the metropolitan population. It is important to emphasize that findings show that there is no extreme locations with very high transportation need and very low accessibility level.
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7. Discussion, conclusions and further research
This section discusses the main findings of the study, which aimed to analyze how equitable are the differences in accessibility in the Beer-Sheba Metropolitan Area. The approach includes the development of measures of public transportation accessibility together with a measure to assess transportation equity applied to TAZ in Beer-Sheba Metropolitan area. This study is an extension of the project focusing on Tel-Aviv Metropolitan area equity and accessibility, from which the methodology was adopted and further modified.
In order to understand the study question: 'How equitable are the differences in accessibility in
Beer-Sheba Metropolitan area' the study defines several layers as research questions: first the accessibility situation in the area was analyzed, then public transport dependency measure was designed and then these models were overlayed to find the gap.
7.1 Understanding accessibility situation in the area
Accessibility is measured at high resolution – single buildings by applying CTGraph tool for public transportation mode for 15, 30 and 45 min trip thresholds. The results show that 67% of jobs are available within 45 min for most of the population in the city of Beer-Sheba. The highest accessibility level is in the center and lowers towards the edges. Moreover the importance of calculation at high resolution can be seen to have significance in the calculation by TAZ and that it can affect results, since the outcomes are different within the same TAZ.
The result of the accessibility calculation for the entire metropolitan area shows that most of the population (62%) has accessibility above average. Beer-Sheba has the highest level of accessibility which gradually decreases while moving towards the edges of the metropolitan area. The regions that have the lowest level of public transportation job accessibility are areas that located in the edges of the metropolitan area and naturally take longer time to get to the city through large open spaces.
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The study examined only job accessibility by public transportation mode, though further research should examine accessibility to public services and commerce together with a comparison to accessibility by private transportation mode.
7.2 Public transportation dependency
In order to assess equity, a transportation need indicator was generated based on socioeconomic factors. The index is estimated for the entire metropolitan area and also separately for the city of
Beer-Sheba. The index is calculated at TAZ level. The spatial distribution of public transportation dependent population is concentrated in suburbs to the South-East and North part of the city.
However, some neighborhoods located at the South part of the city have also high levels of transportation dependency.
7.3 Public transportation accessibility and dependency
By overlaying the transportation need model and the accessibility model 12 TAZ that represent
148,454 residents – almost 40% of population were identified as possibly suffering from transportation inequity and require further attention. This includes the population which lives in areas with low accessibility level and/ or has a high level of transportation need.
This finding should be taken into consideration for future planning of the region. However results are not surprising. The concentration of job opportunities is in the center of the region and these locations are scattered to the edges, moreover job opportunities that are located outside of the metropolitan area were not taken into consideration by this model (see limitations of the study).
The technique described in this study can be easily adopted and applied and can be suggested for further development and use by local authorities to identify the gap between transportation and public needs.
7.4 Limitations of the research
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One of the main limitations of the study is that there is no available demographic data at the level of single building, which is necessary for modeling; therefore full analyses cannot be completed at this level. The available data is only for TAZ level however it results in inaccuracies since the population distribution within TAZ is not homogenous. Accessibility shows much variation within
TAZ.
Another disadvantage is that most of the regions that represent inequality are located at the edges of the metropolitan area while the number of possibly accessible jobs is taken within the metropolitan area. As a result this method does not show the number of jobs taken by residents of the metropolitan area that are located outside the region – for instance in case of Dimona many residents work in factories located near the Dead Sea which is outside of the metropolitan area thus the city shows a low level of accessibility.
7.5 Future work
For future work it is suggested to analyze more the highlighted 12 TAZ that represent inequality and more detailed research of these regions in terms of accessibility level and needs. Moreover, the need index can be calibrated and further adjusted by running sensitivity analyses Furthermore it would be suggested to calibrate the model in terms of distance from bus stop to houses in rural areas and travelling times – the distance should be increased in order to adjust it for remote locations and trip time should be increased due the long travelling distances.
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