A quick overview of geographic information systems (GIS)
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Transcript of A quick overview of geographic information systems (GIS)
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
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
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
Foundations of GIS
Socialsciences
EnvironmentalsciencesPrivate
sectorMineral exploration
Marketing
TourismNavigation
Real estate
UtilitiesFacilities
management
Public
administration
Transportation
Public health
Regional
economics
Emergency
services
Political
redistricting
Epidemiology
Forestry
Soil science
GeologyEcology
HydrologyMeteorology
Demography
Human
geography
Archaeology
Socioeconomicsurveys
Topographicsurveys Environmental
surveys
Cartography Surveying Computing MathematicsVisualization
Semiotics
Remote sensing
Image processing
Photogrammetry Computer graphics
Computational
geometry Artificial intelligence
Statistics
Geostatistics
Operations research
Database
management
Oceanography
Hazards
management
Adapted from
Jones, Geographical
Information Systems
and Computer
Cartography, 1997
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
Vector data
Water quality sample informationRoad network
Administrative
unitsMap sources: DECRG
Raster data
Population
density
Elevation
Accessibility
Satellite
images
Map sources: DECRG
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?
• Data integration
– generate new information from existing
geographic data
• Data visualization
– exploratory spatial data analysis (ESDA)
• Data analysis
– test hypotheses, calibrate models
Elements of spatial analysis
Data integration:
GIS provides a platform for
many types of information
Data
integration
Geographic
location
provides the
frame of
reference:
“space as an
indexing
system”
Buildings
Elevation
Administrativeunits
Hydrology
Roads
Vegetation
Source: Census of Nepal, 1991
Nepal: District population by five-year age groups and gender
Spatial visualization helps make sense of oodles of data
0 - 4 5 - 9
70 - 74
50 - 54
30 -3 4
60 - 64
40 - 44
20 - 24
10 - 14
75 - 79
55 - 59
35 - 39
15 - 1 9
65 - 6 9
45 - 4 9
25 - 2 9
0.6 1.61.41.21.00.8
Sex Ratio by Five Year Age Groups
ratio male / female
Data source: Census of Nepal, 1991
Patterns of outmigration by
young males (red clusters)
are clearly visible
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
Example: Pollution monitoring
How many independent observations?
Spatial dependence (information loss)
requires correction in statistical analysis
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; …
