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