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Uneven Intraurban Growth in Chinese Cities: A Study of Nanjing

Uneven Intraurban Growth in Chinese Cities: A Study of Nanjing. Yehua Dennis Wei Department of Geography and Institute of Public and International Affairs University of Utah Jun Luo Department of Geography, Geology and Planning Missouri State University. Outline. 1. Introduction

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Uneven Intraurban Growth in Chinese Cities: A Study of Nanjing

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  1. Uneven Intraurban Growth in Chinese Cities: A Study of Nanjing Yehua Dennis Wei Department of Geography and Institute of Public and International Affairs University of Utah Jun Luo Department of Geography, Geology and Planning Missouri State University

  2. Outline 1. Introduction 2. Study area and growth patterns 3. Data and Methodology 4. Logistic GWR model 5. Spatial variations of urban growth 6. Conclusion

  3. 1. Introduction 1.1 Research on urban growth in China Two broadly defined groups: Institutional/political economy perspectives Process, mechanisms, theories growth machines development/entrepreneur states globalization, globalizing cities … Markusen: evidences, methodology…

  4. Neoclassical/modeling approaches Land use/land cover change Location factors, growth determinants Statistics, GIS/RS, landscape metrics… Positivism, theory?

  5. 1.2 Modeling urban growth • Statistical models: • global models • underlying forces 1.3 Urban growth • Local, non-stationary process over the space • Same set of factors have different influences on different areas of a city • Context-sensitive theory?

  6. 1.4 Objective • Theories: Regional Development Industrial agglomeration (RS), remaking the Wenzhou model (EG) • Methodology: GIS local analysis, LISA, ESDA, GWR, spatial regression… • Regional development (PiRS) • Urban growth/structure (EPB)

  7. 1) Local analysis/perspectives Explore spatially varying relationships between urban land expansion and influential factors Modeling: Logistic geographically weighted regression (GWR), a local regression technique 2) Socio-economic factors

  8. 2. Study area and Growth Patterns 2.1 Nanjing: coastal, Yangtze Delta • From 1988 to 2000 • Population: 4.88 million to 5.45 million • Built-up area: 392 km2 to 512 km2 • Study area: the majority of built-up areas, 1128.89 km2

  9. Population density 2000

  10. Urban growth in Nanjing: 1988-2000

  11. 3. Data and Methodology 3.1 Data • Census data • Landsat TM imageries: 1988 and 2000 • Image processing • Classification: built-up, agriculture, forest and water body • GIS: transportation, plan scheme, topographic and land use survey

  12. 3.2 Land use data sampling • Sampling: combined systematic and random scheme • Systematic sampling: extract regularly spaced points with 300m interval • Extract all 1332 points with non-urban to urban land use conversion • Randomly select 1350 points without land use conversion • 2682 land use sample points

  13. 3.3 Variables inputs • Dependent variable: Probability of non-urban to urban land conversion • Explanatory variables: • Proximity factors: proximity to economic nodes • Neighborhood factors

  14. Water body Agriculture Land Forest land

  15. 4. Logistic GWR model 4.1 Global logistic regression model Findings: All explanatory variables are significant road infrastructure development local roads: more important than highways Land use constraints: forest, water City centers more important than subcenters

  16. 4.2 Logistic GWR model Weighting scheme: Fixed kernel vs Adaptive kernel N=138, Chosen by minimizing an AIC score

  17. 4.3 Model comparison • Significance test for spatial variability • All parameters with p-value below 0.01 • Significant spatial variability

  18. Summary statistics for GWR parameters estimates

  19. 5. Spatial variations of urban growth pattern • Parameters vary across space: local process • All the variables except for Dis2Lard and ForeDen have both positive and negative parameter values • Dis2Lard: significant all over the city (-) • Other parameters have certain parts in the study area where they are non-significant • Use inverse distance weighted (IDW) interpolation to generate parameter and t-statistic surfaces (30×30m)

  20. GWRparameter surfaces:Roads: more negative effective in the north

  21. GWRparameter t-statistic surfaces

  22. GWRparameter surfaces: Centers: more effective in the north Influence of major centers: compact citySuburban centers: weak, local influence

  23. GWRparameter t-statistic surfaces

  24. GWRparameter surfaces:Neighborhood: varied effectiveness

  25. GWRparameter t-statistic surfaces

  26. Urban growth probabilities

  27. 6. Conclusions Findings: 1. Logistic GWR can significantly improve the global logistic regression for urban growth modeling: 2. Effects of determining factors have significant spatial variation 3. Interpretation of spatial process should be careful with spatial context; need for local analysis

  28. Limitations: 1) Data: socio-economic variables Discussion: 1) The nature of theory: Theoretical statements 2) Local analysis vs. generalization 3) Representativeness, sampling bias Thank You and Questions?

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