ANALYSING THE DIVERSITY OF DEPRIVED AREAS IN MUMBAI, INDIA

Transcription

1 ANALYSING THE DIVERSITY OF DEPRIVED AREAS IN MUMBAI, INDIA MONIKA KUFFER KARIN PFEFFER, RICHARD SLIUZAS, ISA BAUD, MARTIN VAN MAARSEVEEN Source: Google Earth

2 CONTENT OF PRESENTATION OVERVIEW Global urbanization trends Data on slums deprived areas Mapping slum areas Mapping the diversity of deprived areas Some concluding remarks 36th EARSeL Symposium - M. Kuffer, /07/2016

3 URBANIZATION RATES AFRICA ASIA LATIN AMERICA - CARRIBBEAN

4 URBAN POPULATION OF MEGA CITIES Delhi population recently estimated by UN 25 million > 36 million 36th EARSeL Symposium - M. Kuffer, /07/2016

5 SLUM POPULATION

6 DEFINITION COMPLEXITY SLUMS EXAMPLE INDIA Indian census has three types of slums: notified by the Government recognized (not formally notified but recognized by the Government) identified (at least 300 people or households in congested and unhygienic environments and lack basic services - visited and registered by a Charge Officer) (Office of the Registrar General & Census Commissioner, 2013) From 2001 to 2011 the census urban slum population decreased from 26.3% to 17.4% (Office of the Registrar General & Census Commissioner, 2013), Success of policy initiatives such as the Rajiv Awas Yojana aiming at a slum free India (Ministry of Housing & Urban Poverty Alleviation, 2010)? 36th EARSeL Symposium - M. Kuffer, /07/2016

7 URBAN DEVELOPMENT PROJECTS EXAMPLE AHMEDABAD Several urban renewal projects, e.g. Sabarmati River Front Development (SRFD), the Kankaria Lakefront Development and the Bus Rapid Transit System (BRTS) project For implementing the projects around 29,000 centrally located slum houses were demolished. Households (when being eligible ) were offered to be resettled to a site in the urban periphery. (Patel et al., 2015). Source: Kotadiya, 2016

8 EXAMPLE OF RESETTLEMENTS AHMEDABAD (ROYCHOWDHURY, 2013)

9 WHERE ARE THE POOR DEPRIVED SLUMS? MUMBAI Municipal data often not up-todate Hotspots of poor extracted from census data But, wards are heterogeneous Multiple deprivation Index Ward Boundaries Kilometers Kuffer, Pfeffer, Baud & Sliuzas (2013)

10 WHERE ARE THE POOR DEPRIVED SLUMS? MUMBAI WorldView 2 VNIR: 1.8 m (8 bands) PAN: 0.5 m

11 THE MORPHOLOGY OF DEPRIVE AREAS Features Slum areas Formal built-up areas Size Small (substandard) building sizes Generally larger building sizes Density (Very) high roof coverage densities Lack of public (green) spaces within or in the vicinity of slum areas Low moderate density areas Provision of public (green spaces) within or in vicinity of planned areas Pattern Organic layout structure (no orderly road arrangement and noncompliance with set-back standards) Regular layout pattern (showing planned regular roads and compliance with set-back rules) Site characteristics Often at hazardous locations (e.g. flood prone, close to Land has basic suitability for being built-up industrial areas, steep slope) (Basic) infrastructure is provided Proximity to infrastructure lines and livelihood opportunities Kuffer, M., Pfeffer, K., & Sliuzas, R. (2016). Slums from Space 15 Years of Slum Mapping Using Remote Sensing. Remote Sensing, 8(6), 455.

12 REMOTE SENSING PUBLICATIONS ON SLUM MAPPING METHODS Analysis of types of informal/slum areas Correlation with socioeconomic indicators Contour model Machine learning Object-based approach METHODS Pixel-based Statistical approach model Texture/ Morphology Visual image interpretation Total Number (Percentage) (6.9%) (5.7%) FOCI Identification of slum areas Extractions of roofs/roads (objects) (55.2%) (14.9%) Land use/cover mapping Total Number (Percentage) (17.2%) 4 (4.6%) 11 (12.6%) 28 (32.2%) 11 (12.6%) 4 (4.6%) 14 (16.1%) 15 (17.2%) 87 (100%) Kuffer, M., Pfeffer, K., & Sliuzas, R. (2016). Slums from Space 15 Years of Slum Mapping Using Remote Sensing. Remote Sensing, 8(6), 455.

13 SLUM AREA MAPPING BASED ON IMAGE TEXTURE UTILITY OF GLCM VARIANCE - EXAMPLE AHMEDABAD

14 RANDOM FOREST CLASSIFICATION EXTRACTION OF SLUM AREAS (HOMOGENEOUS URBAN PATCHES) IN MUMBAI Kuffer, M., Pfeffer, K., Sliuzas, R., & Baud, I. (2016). Extraction of slum areas from VHR imagery using GLCM variance. IEEE J. Sel. Top. Appl. Earth Observ. and Remote Sens., 9(5),

15 BUT HOW MUCH DO WE SIMPLIFY THE REALITY? DELHI DIVERSITY OF DEPRIVED AREAS? Sub-types Indicators - Measure Informal A (squatter settlement, semipermanent) Structure of layout: irregular patterns, absence of public green space Built-up densities: more than 80% of roof coverage Building size: small multi-family buildings Informal B Structure of layout: irregular patterns,, absence of (squatter settlement most precarious) public green space Built-up densities: more than 90% of roof coverage Resettlement colony like (low standard formal settlement) Building size: no clearly identifiable buildings Site suitability: small encroachments Structure of layout: regular patterns, clearly identifiable road network, planned public green space Built-up densities: more than 80% of roof coverage Building size: small multi-family buildings Distance: remote settlement 15/07/ th EARSeL Symposium - M. Kuffer, 2016

16 DETERMENTS OF THE TYPOLOGY OF DEPRIVED AREAS

17 DIVERSITY OF DEPRIVED AREAS IN MUMBAI A FIRST TYPOLOGY CLASS 1 CLASS 2 CLASS 3 CLASS 4 CLASS 5 Slum pocket Slum area, small Slum area, mix Basic formal and Formal areas buildings small/larger buildings chawls Size: Small roofs Size: Small roofs Size: Small-medium roofs Size: Medium roofs Size: Large roofs Density: High Density: High Density: Mix Density: High-medium Density: Medium - low Patter: Organic Patter: Organic Patter: Diverse Patter: Some structure Patter: Well structured

18 INFORMATION EXTRACTION FROM IMAGERY HOMOGENOUS URBAN PATCHES (HUPS)

19 METHODOLOGY MAPPING THE TYPOLOGY OF DEPRIVATION LRM: Statistical significant indicators

20 INDICATORS Indicators B Sig. Odds ratio GLCM variance Constant th EARSeL Symposium - M. Kuffer, /07/2016

21 BINARY LOGISTIC REGRESSION MODEL PROBABILITY CLASS INDICATORS B ACCURACY Class 1 Class 2 Class 3 Class 4 Built-up mean area GLCM variance Built-up patch density Constant GLCM variance Land cover evenness Built-up mean area Constant GLCM variance Slope mean GLCM second moment mean Constant GLCM variance Slope mean GLCM entropy mean Land cover evenness Constant th EARSeL Symposium - M. Kuffer, 2016 Nagelkerke R 2 : 0.88 Nagelkerke R 2 : 0.98 Nagelkerke R 2 : 0.97 Nagelkerke R 2 : /07/2016

22 MAPPING THE TYPOLOGY OF DEPRIVATION OVERALL ACCURACY FOR BUILT-UP CLASSES 77% In particular Class 3 and 4 have the omission and commission problems

23 BUT HOW MUCH DO WE SIMPLIFY THE REALITY? MUMBAI DIVERSITY OF DEPRIVED AREAS? 36th EARSeL Symposium - M. Kuffer, /07/2016

24 CLUSTER OF DEPRIVATION ACROSS WARD BOUNDARIES HEIGHT INDICATES THE VALUE OF DEPRIVATION (BASED ON CENSUS DATA)

25 CONCLUDING REMARKS RAPID DEVELOPMENT TRENDS NEED TIMELY DATA In particular pattern and environment indictors play an important role to map the typology of deprivation The deprived classes have fuzzy boundaries, which are difficult to capture on ground as well as via morphological indicators. Census data at administrative units are commonly geographically meaningless and smooth local variability. For many planning questions (hazard, transport, planning interventions) disaggregated spatial data on deprivation is important. 36th EARSeL Symposium - M. Kuffer, /07/2016

26 End of the Presentation Thank you for your attention! 36th EARSeL Symposium - M. Kuffer, 2016