Multi scale and multi sensor analysis of urban cluster development and agricultural land loss in China and India

Transcription

1 Multi scale and multi sensor analysis of urban cluster development and agricultural land loss in China and India Karen C. Seto, PI, Yale Michail Fragkias, Co I, Arizona State Annemarie Schneider, Co I, U of Wisconsin Jiyuan Liu, Co I, Chinese Academy of Sciences, China P.K. Joshi, Co I, TERI, India Xiangzheng Deng, Co I, Chinese Academy of Sciences, China Vikramāditya Prakāsh, Collaborator, U of Washington Jeremy Wallace, Collaborator, Ohio State Qingling Zhang, Research Scientist, Yale Qian Zhang, Postdoctoral Fellow, Yale Donghwan Kim, Postdoctoral Fellow, Yale Peter Christensen, Phd student, Yale Li Jiang, Phd student, Yale Chris Shughrue, MESc student, Yale

2 By 2030, China and India s urban populations are expected to increase by more than 700 million ~700,000,000 PEOPLE 10,000,000 PEOPLE

3 China s urban population is expected to reach 1 billion by 2030

4 Our project goal is to quantify and understand the growth of urban clusters

5 Underlying concept introduced in 1890 By agglomerating in close geographical proximity, firms can receive increasing returns from a trinity of agglomeration economies: a local pool of skilled labour, local supplier linkages, and local knowledge spillovers.

6 Urban clusters have become a key topic in economic, innovation, and globalization debates

7 Clusters emphasize relative economic and political functions Extends beyond city administrative boundaries and includes complex social, economic, and political processes. Can be a single urban core surrounded by many smaller towns, or it can consist of several large cities of similar sizes.

8 Silicon Valley is an innovation cluster

9 Silicon Valley is an innovation cluster

10 Focus on clusters, not cities Which urban clusters are hot spots? What are the land use implications of urban clusters? What explains their growth? How will urban clusters develop?

11 Project Goals 1. Detect and quantify the growth of urban cluster hot spots in China and India. 2. Identify the types of land cover changes in these urban cluster hot spots and when they occurred, with an emphasis on the loss of agricultural land. 3. Explain the drivers of the growth of urban clusters and urban land conversion within them.

12 Goal 1: MODIS and DMSP OLS to identify urban cluster hot spots Local Indicators of Spatial Association Cluster identification Hot spots Faster growth Slower growth (Anselin, L. 1995)

13 Goal 1: Progress to Date

14 Goal 1: Progress to Date Preliminary urban cluster hot spot identification in China Algorithm refinement

15 Goal 2: Identify types of land cover changes in urban cluster hot spots MODIS NDVI Evergreen forest Deciduos forest Agriculture Urban day interval since January 1, 2001

16 Goal 2: Identify types of land cover changes in urban cluster hot spots Agriculture To Urban 0.6 MODIS NDVI Δ = Σ NDVI j Σ NDVI i Σ NDVI i Σ NDVI j day interval since January 1, 2001

17 Goal 3: Explain drivers of urban cluster growth and urban land conversion within them India Delhi Chandigarh Case study Multi scale model Agent Based Model Investments in construction Urban Planning Investments in education Socioeconomic data

18 Why Delhi and Chandigarh? Chandigarh: State capital of Punjab & Haryana. The best & most planned city in India. Modernist design and development. Delhi: National capital. National transportation & economic hub. Second largest population.

19 Goal 3: Explain drivers of urban cluster growth and urban land conversion within them India Delhi Chandigarh Case study Multi scale model Agent Based Model Investments in construction Urban Planning Investments in education Socioeconomic data

20 Objective: How does the Delhi urban system respond to external shocks? ABM of Urban Drivers in Delhi Urban System Values and Preferences Higher Education Commerce and Investments Policies Decisions on Delhi Urban System Housing Output Credit: design/

21 Goal 3: Explain drivers of urban cluster growth and urban land conversion within them India Delhi Chandigarh Case study Multi scale model Agent Based Model Investments in construction Urban Planning Investments in education Socioeconomic data

22 Goal 3: Progress to Date Data collection campaign Field visits, meetings with government & researchers

23 Yale India Urban Conference

24 Goal 3: Explain drivers of urban cluster growth and urban land conversion within them China Multi scale model Fiscal transfers Hukou policies Socioeconomic data Construction & ag prod stats

25 Agricultural investment is a cause of agricultural land loss in China (Jiang, Deng, and Seto. In review. Landscape and Urban Planning.) At the county level Agricultural investment drives agricultural land conversion. Both urban land rent and urban wages contribute to agricultural land conversion. At the provincial level Urban wages and foreign direct investments both contribute to agricultural land conversion. Higher GDP correlates with more urban expansion and the relationship is nonlinear.

26 Goal 3: Explain drivers of urban cluster growth and urban land conversion within them China Multi scale model Fiscal transfers Hukou policies Socioeconomic data Construction & ag prod stats

27 Goal 3: Progress to Date Where do fiscal transfers go and how do they affect the growth of urban clusters? Fiscal transfers from central government as percentage of a county s total revenue, 2000 Data Source: All China Prefecture City County Fiscal Statistical Data (2000)

28 Expected Benefits Identification of urban cluster hot spots in China and India. Direct measurement and new datasets of urban land conversion and agricultural land loss. Better understanding of dynamics underlying urban cluster development. New remote sensing algorithms for detecting urban growth hot spots and urban change. Empirical land use observations useful to other global change research efforts.

29 Analytical Approaches Study Regions

30 Next Steps Continued database development Fieldwork in 2012 Algorithm refinement Model building for India & China

31 Our website

32 Extra slides

33 New and ongoing international collaborations Graduate training for US and non US scientists through collaborative international research effort with TERI and CAS. Institutional level commitments through Yale School of Forestry and Environmental Studies and TERI. Explicit connections with: IGBP/IHDP Global Land Project, IHDP Urbanization and Global Environmental Change Project, MEGAPOLI