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Spatial Analysis Unit

GIS image

The Spatial Analysis Team carries out analytical work and advisory services using geographic information systems (GIS) and related technologies for development research and policy analysis. Spatial technologies involve applications that can be used to visualize data, integrate data, and conduct spatial analysis. 

Contact: Brian Blankespoor -  BBlankespoor@worldbank.org

 
 Visualization  |  Data integration  |  Spatial Analysis  |  Related Links  |  Core Team   

Visualization: Helps explore patterns of information collected in the field or socioeconomic data compiled by statistical offices.

Data integration: By overlaying different types of spatially referenced information GIS can create new indicators and variables for visualization and Analysis. Geographic data integration uses ‘space as an indexing system’ to combine heterogeneous information sources.

Spatial analysis: Spatial process models aid in the analysis of transport networks, migration or disease distribution. Incorporating information on the location of data observations in statistical or econometric analysis corrects for possible estimation bias and can provide insights beyond typical cross-sectional or time series results.

Overview of Geographic Information Systems (PDF/PPT 3.8mb) 

 
Visualization 

Visualization of socioeconomic data illustrates concepts central to economic geography. For instance, “spike maps” of subnational GDP highlight the large concentration of economic activity in urban agglomerations.

 

Example: 
World Development Report 2009 

Urban agglomeration GIS
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Field researchers now routinely use global positioning systems (GPS) to georeference households, farms, or public facilities locations. These observations can then be integrated with thematic maps, air photos, or satellite data such as in this example from a household survey program in India.

 

 

 

Example:
Using the global positioning system in household surveys for better economics and better, World Bank Policy Research Working Paper 4195.

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Maps of East Asia clearly show how poor regions may be ranked very differently depending on whether poverty rates or poverty counts are considered.

 

 

Poverty EAP 120 pixels

 

 

 

 


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Data integration

 

Where is biodiversity highest? GIS methods help integrate and summarize habitat range maps by ecoregion for more than 10,000 species. The resulting information feeds into the Global Environment Facility’s Resource Allocation Framework. 
More >>

 

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Integrating vulnerable population and species range maps and analyzing their proximity to stockpiles of obsolete pesticides in Tunisia as part of the Africa Stockpile Programme. 
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What areas are already at high risk of flooding and will likely get even wetter with climate change? A study in Latin America used GIS overlays to integrate data on current disaster risk, probable changes in local climate, and socioeconomic characteristics.
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LAC poverty 120 pixels
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Analysis | models

 

Modeling global-scale, multi-hazard risk data indicators from models in order to identify key hotspots where the risks of natural disasters are particularly high. The 2004 World Bank Disaster Hotspots study was recently updated in the ISDR Global Assessment Report on Disaster Risk Reduction, a collaboration between the UN, the World Bank and other partners. More >>

 

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GIS data integration generates new data sets and variables that can then be analyzed using econometric techniques. The results can, in turn, be mapped for further analysis. A study on Bangladesh combined maps of agroecological endowment with household survey data to identify barriers to non-farm growth and to find regions where possible interventions are most promising.

Example: "Spatial specialization and farm-nonfarm linkages," World Bank Policy Research Working Paper 4611.

 

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Analyzing market and transport accessibility to develop a better understanding of the role of public infrastructure in enhancing firm level productivity and in the location decisions of private sector firms.

 

Example:
Economic structure, productivity, and infrastructure quality in southern Mexico, World Bank Policy Research Working Paper 2900.

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GIS derived data improve estimation of indicators of ambient air pollution levels in cities that do not have monitored data.

 

Example:
Urban Outdoor Particulate Air Pollution: New Estimates (PPT 656kb)

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How might countries’ vulnerability to climate impacts or changes in the global fuel mix affect their disposition towards a global climate agreement? Analysis of detailed geographic data ranging from sea level rise scenarios to renewable energy resources yields a global typology.

 

Example: Country Stakes in Climate Change Negotiations: Two Dimensions of Vulnerability , World Bank Policy Research Working Paper 4300.

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What are the likely trade benefits of investing in upgrading and maintaining a trans-African highway network. Using geographical network analysis linked to a gravity trade model, this research estimated increases in trade flows along each link in the continent’s main road network.
Link to WPS 4097 or research brief (http://go.worldbank.org/PKLV2UE530)

Example: Road network upgrading and overland trade expansion in Sub-Saharan Africa, World Bank Policy Research Working Paper 4097.

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 Related Links

 

Other World Bank web sites related to spatial economics.

 ContactCore Team

Brian Blankespoor
Bblankespoor@worldbank.org

Uwe Deichmann (World Bank)
Brian Blankespoor (World Bank)
Sioban Murray (World Bank) 


Last updated: 2009-06-04




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