A quick overview of geographic information systems (GIS) Uwe Deichmann, DECRG

Transcription

1 A quick overview of geographic information systems (GIS) Uwe Deichmann, DECRG

2 Why is GIS important? A very large share of all types of information has a spatial component ( 80 % of all government data ) Applications vary greatly in terms of scope and complexity Used in just about every sector In operations, but also in policy analysis and research

3 What does it take? Based on a number of integrating technologies and methods Data collection Traditional maps or administrative data Remote sensing Field data collection (GPS) Visualization and dissemination Cartography Internet mapping Analysis

4 What is a GIS? The boring definition a computer-based tool for the input, storage, management, retrieval, update, analysis and output of information The less boring definition a way to organize, display and analyze information that is a lot more fun and useful than using spreadsheets and tables alone

5 Foundations of GIS Socioeconomic surveys Topographic surveys Environmental surveys Marketing Real estate Utilities Archaeology Emergency services Tourism Social sciences Private sector Facilities management Regional economics Political redistricting Human geography Transportation Public administration Demography Navigation Mineral exploration Public health Epidemiology Ecology Soil science Geology Environmental sciences Oceanography Hydrology Hazards management Forestry Meteorology Cartography Surveying Computing Mathematics Visualization Photogrammetry Computer graphics Database Statistics Semiotics management Computational Image processing Operations research geometry Remote sensing Artificial intelligence Geostatistics Adapted from Jones, Geographical Information Systems and Computer Cartography, 1997

6 Some technical jargon GIS data come in two main flavors vector data: discrete objects such as points, lines, polygons raster data: continuous fields made up of grid cells Can integrate both or convert from one to the other fairly easily

7 Vector data Water quality sample information Road network Map sources: DECRG Administrative units

8 Raster data Accessibility Elevation Population density Satellite images Map sources: DECRG

9 GIS applications: Inventories versus analytical Inventory-type applications focus on queries and data summaries where are mangroves located? how far are they from coral reefs? display all habitat areas for endangered species Spatial analysis asks the why and how do healthy mangroves support recruitment of reef fish? which mangrove and reef areas are most threatened? where would protected areas provide maximum benefit?

10 Elements of spatial analysis Data integration generate new information from existing geographic data Data visualization exploratory spatial data analysis (ESDA) Data analysis test hypotheses, calibrate models

11 Data integration: GIS provides a platform for many types of information

12 Administrative units Elevation Buildings Hydrology Roads Vegetation Data integration Geographic location provides the frame of reference: space as an indexing system

13 Spatial visualization helps make sense of oodles of data Nepal: District population by five-year age groups and gender Source: Census of Nepal, 1991

14 Patterns of outmigration by young males (red clusters) are clearly visible Sex Ratio by Five Year Age Groups ratio male / female Data source: Census of Nepal, 1991

15 Spatial analysis In the Bank, this often involves use of GIS derived variables in econometric models Similar to time series data, spatial data require special attention spatial dependence and spatial heterogeneity use of spatial econometric techniques

16 Example: Pollution monitoring How many independent observations? Spatial dependence (information loss) requires correction in statistical analysis

17 GIS techniques also support many other approaches to spatial analysis map algebra; terrain modeling; finding and quantifying relationships; generalization; spatial process models; spatial statistics; geostatistics; spatial econometrics; point pattern analysis; spatial interpolation; spatial search; location/allocation; districting; spatial interaction modeling; cellular automata; distance modeling; neighborhood filtering; pattern recognition;