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Estimating Population Surfaces in Areas Where Actual Distributions Are Unknown: Dasymetric Mapping and Pycnophylactic Interpolation Across Different Spatial Scales

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Estimating Population Surfaces in Areas Where Actual Distributions Are Unknown: Dasymetric Mapping and Pycnophylactic Interpolation Across Different Spatial Scales ESTIMATING POPULATION SURFACES IN AREAS WHERE ACTUAL DISTRIBUTIONS ARE UNKNOWN: DASYMETRIC MAPPING AND PYCNOPHYLACTIC INTERPOLATION ACROSS DIFFERENT SPATIAL SCALES Thesis submitted for the degree of Doctor of Philosophy at the University of Leicester by Idris Jega Mohammed MSc (Leicester) Department of Geography University of Leicester June 2015 Idris Jega Mohammed Title: Estimating population surfaces in areas where actual distributions are unknown: dasymetric mapping and pycnophylactic interpolation across different spatial scales. ABSTRACT Spatially distributed estimates of population provide commonly used demand surfaces in support of spatial planning. In many countries, spatially detailed population summaries are not available. For such cases a number of interpolation methods have been proposed to redistribute summary population totals over small areas. Population allocations to small areas are commonly validated by comparing the estimates with some known values for those areas. In areas where spatially detailed estimates of the population do not exist, that is where the actual population in small areas is unknown, such as Nigeria validation is problematic. This research explores different interpolation methods applied at different scales in areas where the actual population distribution is known and where validation is possible. It then applies the parameters developed from these results to areas where the distribution is unknown. The binary dasymetric method using land cover data derived from a classified 30m spatial resolution satellite imagery as the ancillary data input and with disaggregation over 30m support grids, was found to provide the best target zones estimates of the population. The demand surfaces were then used to evaluate current health facility locations and then to suggest alternative spatial arrangements for health centres in Port-Harcourt, Nigeria. The average distance from each demand point to the nearest healthcare centre was found to be 1204m. When alternative locations for the current health centres were identified, the results suggest 13 service provision points would provide almost the same demand coverage as the 17 current PHCCs. This research develops methods that can be used to support informed decision making in spatial planning and policy development. ii DEDICATION To my late father, Alhaji Muhammadu Jega (1st Dan Lawan Gwandu). iii ACKNOWLEDGEMENT I would like to express my special appreciation and thanks to my supervisors Professor Alexis J. Comber and Dr. Nicholas J. Tate who patiently provided the vision, encouragement and advise necessary for me to continuously progress through the stages of my thesis. I am also deeply grateful to Professor Chris Brunsdon for his priceless contributions during the first year of my research as my supervisor. I would like to express my gratitude to Petroleum Technology Development Fund (PTDF) for fully sponsoring my PhD. To the data providers; Ordnance Survey UK, Astrium Services UK and Geo-technics Services Limited, I am grateful. Special thanks to my family and friends. Words cannot express how grateful I am to all for the sacrifices you made on my behalf. Your prayer for me was what sustained me thus far. At the end, I would like to express my appreciation to my beloved wife Hafsat and my children (Asma’u and Ahmed) for their endurance during the time I had been away from home. I have no suitable word that can fully describe their everlasting love for me. iv LIST OF ABBREVIATIONS AND ACRONYMS ACLP Anti-Covering Location Problem CASWEB Census Area Statistics on the Web CEDS Cadastral-based Expert Dasymetric System CoV Coefficient of Variance EIA Energy Information Administration ELP Expropriation Location Problem EM Expectation-Maximization EMS Emergency Medical Services ETM+ Enhanced Thematic Mapper Plus EU European Union GGA Grouping Genetic Algorithm GIS Geographical Information Science GOR Government Office Regions GP General Practitioner GWEM Geographically Weighted Expectation Maximization GWR Geographically Weighted Regressions HUP Homogeneous Urban Patches INCRA National Institute of Colonization and Agrarian Reform IQR Interquartile Range ISODATA Iterative Self-Organising Data Analysis Technique Algorithm LGAs Local Government Areas LiDAR Light Detection and Ranging LILP Limited Impact Location Problem LSCP Location Set Covering Problem LSOA Lower Super Output Area LV Limiting Variable MCLP Maximal Covering Location Problem MCLPDC Minimum Covering Location Problem with Distance Constraints MDGs Millennium Development Goals MEXCLP Maximal Expected Covering Location Problem MLMCD Multi-Layer Multi-Class Dasymetric v MS Multiple Sclerosis MSOA Middle Super Output Area NASRDA National Space Research and Development Agency NHP National Health Policy NNPC Nigerian National Petroleum Corporation NPC National Population Commission NPHCDA National Primary Health Care Development Agency OA Output Area OD Origin and Destination OLS Ordinary Least Squares ONS Office of National Statistics PHCC Primary Health Care Centre PHCCs Primary Health Care Centres PTDF Petroleum Technology Development Fund PUMS Public Use Microdata Sample RMSE Root Mean Squared Error RNB Road Network Buffer RNHP Revised National Health Policy SANET Spatial Analysis Network Tools SW Street Weighting TIGER Topologically Integrated Geographic Encoding and Referencing TIN Triangulated Irregular Networks UA Unitary Authority UAVs Unmanned aerial vehicles UK United Kingdom UPCs Unit Postcodes US United States USGS United States Geological Survey vi LIST OF PUBLICATIONS Peer reviewed conference proceedings Mohammed, I. J., Comber, A. and Tate, N, 2013. Effects of Land Cover resolution on spatially distributed demand surfaces: the binary dasymetric approach. GISRUK 2013, Proceedings of the Geographical Information Science UK Conference. University of Liverpool 3rd-5th April 2013. University of Liverpool, Liverpool Mohammed, I. J., Comber, A. and Brunsdon, C., 2012. Population estimation in small areas: combining dasymetric mapping with pycnophylactic interpolation. GISRUK 2012, Proceedings of the GIS Research UK 20th Annual Conference, Volume 1, pp.79- 87 (eds. Barry Rowlingson and Duncan Whyatt), 11-13th April 2012, Lancaster. Poster presentation at festival of postgraduate research Mohammed, I. J., 2013. Effects of satellite image resolution on population estimates, University of Leicester Festival of Postgraduate Research 2013. vii TABLE OF CONTENTS ABSTRACT ...................................................................................................................... ii DEDICATION ................................................................................................................. iii ACKNOWLEDGEMENT ............................................................................................... iv LIST OF ABBREVIATIONS AND ACRONYMS ......................................................... v LIST OF PUBLICATIONS ............................................................................................ vii TABLE OF CONTENTS ............................................................................................... viii LIST OF FIGURES ....................................................................................................... xiii LIST OF TABLES ......................................................................................................... xix Chapter 1 ........................................................................................................................... 1 1. INTRODUCTION .................................................................................................... 1 1.1 Problem description ................................................................................................ 1 1.2 Motivation ............................................................................................................... 4 1.3 Aim and objectives of the study ............................................................................. 5 1.4 Research questions .................................................................................................. 6 1.5 Thesis Structure ...................................................................................................... 7 Chapter 2 ........................................................................................................................... 9 2. LITERATURE REVIEW ......................................................................................... 9 2.1 Introduction ............................................................................................................. 9 2.2 Areal Interpolation Techniques .............................................................................. 9 2.2.1 The Pycnophylactic Interpolation Technique ................................................ 12 2.2.2 The Dasymetric Mapping Method ................................................................. 14 2.3 Influence of demand population on spatial accessibility ...................................... 18 2.4 GIS and Geographical Access to Healthcare ........................................................ 19 2.4.1 Review of spatial access ................................................................................ 20 2.5 Location-allocation models ................................................................................... 22 2.5.1 The p-Median problem .................................................................................. 23 viii
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