Post on 22-Dec-2015
Gridded population databases: The demand side viewMajor usages
Both descriptive and analytic Main areas
Environment & Biodiversity ClimateHazards & Emergency managementLand use change AgricultureHealthUrban studiesEconomic development
The demand side view (continued)
Minor usages Ready-to-go denominator
Example: used in Environmental Sustainability Indicator Project to adjust So2 emissions by populated land area rather than total land area (World Economic Forum, 2000)
Proxy variableExample: for climatic center of population
distribution in a study of labor economics (Hall and Jones, 1998)
Better description about the distribution of human population
Cohen and Small
World population generally “localized” along low-lying coasts, rivers
Population peak at 2300 m altitude: Mexican Plateau
Data used: GPW
UNDP & WRI Population distribution
(red) by aridity zone (browns)
Shows highest density populations living in predominantly semi-arid or dry sub-humid climates
Data: GPW
Better description about the distribution of human population (continued)
Ecosystem stress:Human modification of coastal areas
WRI Close-up of SE Asia Global estimates nearly all
areas with 100 km of coast are modified by human activity 20% highly altered by
conversion to agriculture or urbanization
Data: Night-time lights (OLS)
Cropland/natural vegetation mosaic
Cropland and built up area
Least modified
Ecosystem stress (2):Water Availability by River Basin
WRI Estimate ~2.3 billion people are living in conditions of water stress or
water scarcity Allows for estimates by river basin not by national aggregates Many more than had been previously estimated Combines pop data with river basin model run-off data Data: GPW
Health (1):Pop growth and the extinction of the tsetse fly
Project human and tsetse fly population distribution to 2040 Human population growth
causes loss of fly habitat Model human-fly interactions
based on species-specific behavior
Estimate that by 2040 the fly population will decline throughout Africa but an area as large as Europe will remain infested
Pop growth affects subspecies differently
Robin Reid et al., International Livestock Research Institute, Nairobi
Combine human pop data with fly pop data previous efforts failed
because national level data do not match that of fly habitat
Data: GPW
Health (2):Studies of Malaria
Snow et al. Estimate morbidity and
mortality in Africa 1 million deaths in 1995
estimated due to malaria 200 million clinical events
Combine gridded population density with national-level data on age structure and malarial data (endemicity, hospital records)
Data: GPW
Gallup and Sachs Deterministic analysis of
malaria and economic growth Population density within
100 km a coastline--to proxy for access to transportation--is dependent variable
Despite the strong correlation between poverty and malaria, and the strong impacts of malaria on the economy, the causal mechanism are unclear
Data: GPW
Migration:Studies of displacement of persons
Dobson and colleagues
Shows short term (1-2 years) population movement in Kosovo
All systematic record keeping suspended
Combines heuristic model (for pop data) with media accounts of wartime movement
Data: Landscan
What’s missing from known studies?
Examples from: Climate Land use change Urbanization Agriculture Hazards Economic development
Scale issues: Small vs. large areas
It seems that topics--and even disciplines--tend to have scale preferences: Health studies tend to be local, at best continental Urban studies tend to be local or regional Climate studies tend to be global
To what extent is this constraint data-driven rather than theory-driven?
Can we create a collection of best-available data sets that can always be aggregated for coarser-scale analysis? To what extent are methods or inputs scale-specific?
Where have we come in 6 years?
Lots of use UNEP/GRID-Environment Canada data base has had over 75,000 data
transfers since 1996 Dozens of published papers and books have used gridded population
data almost all note that the study is improved or innovate in part because of
the recent availability of population data on a grid
Inputs are getting better Input data--both for population and administrative boundaries--
continue to improve Satellite data are becoming more useful for integration with
population data Methods are more sophisticated
Applications are becoming more apparent Need for (and value of) interdisciplinary studies is
increasingly recognized