What is GIE?

Geospatial Impact Evaluation (GIE) is a quasi-experimental statistical approach that aims to estimate the effect of wide range of policy interventions on land cover at different scales. Because many types of policy interventions, such as forestry, agriculture, water resource management, urban development, and pollution control, are associated with land cover values, GIE can shed light on results across time and space.

To establish the impact of an intervention, we compare land cover values within the area targeted and in comparable areas that were not targeted by an intervention. We construct a set of control areas that have observed characteristics similar to the areas that receive interventions. Treatment effects of the intervention are estimated based on statistical comparisons of these matched locations.

Policy Relevance

The developing world has a pressing need to understand what types of aid activities are most successful at promoting sustainable development. Better evidence is needed about why some programs work and others do not. Evidence is limited because impact evaluations are typically expensive and results are difficult to measure across time. GIE uses high-resolution, high-frequency data that is available globally across many years.

Development organizations are spending hundreds of millions of dollars each year on programs to achieve environmental and development goals. GIE has the potential to rapidly increase the amount of evidence available about a wide range of intervention and can be linked to other types of impact evaluations.

The Geospatial Component

To carry out GIE, we build a high-resolution geospatial and remote sensing database that allows for statistical matching and the construction of control areas across a suite of demographic, environmental, and other geographic attributes.

Demographic measures typically include the full range of census variables available such as race, income, family, size, economic activity, education level, etc. from official data. The environmental suite of data typically includes the land cover measures, forest measures, slope, soils, land disturbance measures, deforestation rates, biome data, ecoregion information, climate, etc. The other geographic attributes include rivers, roads, utility infrastructure, and the distance to each of these locations. This grouping also includes indigenous boundaries, protected lands, managed communities, and other municipal boundaries.

The goal is to build as complete a geographic dataset for the regions as possible and then match based on the variables that predict land cover change.

The Statistical Principle

Disentangling the direct effects of land management interventions from the background factors that drive both land cover change and the targeting of interventions is a major challenge. Both observable and unobservable differences between treatment areas that receive an intervention and control areas that do not receive an intervention can confound estimates of treatment effects.

We employ a matching approach to construct a set of control plots that have similar pre-treatment characteristics as the plots that were assigned to treatment under the interventions. As noted earlier, we compile spatial data on other variables that have strong associations with land cover change. We then iteratively search through sets of control observations outside of intervention areas to select a set of control units that have observed distributions for each covariate that are not observationally different from the distributions of the same covariates in the treatment plots, —ρ(Xi | Ti = 1) ≈ ρ(Xi | Ti = 0)— where ρ is the observed values in the selected set of treatment and control groups, X is a matrix of covariate values, and T is the treatment state. This is geospatial matching.

We carry out statistical matching using a genetic algorithm that both weights and discards control plots that do not serve as comparable units to the treatment plots. We apply a genetic algorithm that iteratively searches through many sets of potential control plots to maximize balance on the observed covariates. Good matched sets are those that have a large minimum p-value on a paired t-test for differences of means between treatment and control observations across all covariates. These sets are passed onto the next generation, along with mutated sets to ensure the full space of combinations of observations for the control set is explored.


The GIE Team

Our team of three university faculty specializes in geospatial impact evaluation and has experience working with international aid organizations. We blend expertise in remote sensing, geographic information systems, and statistical methods for impact evaluation. Over the last two years, we worked with the USAID Ecuador mission to complete a first-of-its-kind geospatial impact evaluation of a land tenure program for natural resource management. This study was recently accepted for publication in the top-ranked geography and environmental studies journal Global Environmental Change, and showcases the promise of geospatial impact evaluation as a tool for understanding results where other methods are prohibitively expensive or logistically challenging.

Stuart Hamilton, Ph.D

Assistant Professor, Department of Geography, Salisbury University, Salisbury, MD

Stuart Hamilton is Assistant Professor of Geography and Geosciences at Salisbury University. He has published research in Land Use Science on international aid and mangrove deforestation, in Global Environmental Change on land tenure and tropical forests, and in Bulletin of Marine Science and BioScience on the role of aquaculture in driving deforestation. He is currently serving as a Prometeo Fellow within the Ministry of Environment in Ecuador and helped establish the GIS lab at the National Fisheries Resources Research Institute in Uganda.

Mark Buntaine, Ph.D

Assistant Professor, Department of Political Science, University of California, Santa Barbara, CA

Mark Buntaine is Assistant Professor of Environmental Policy at the University of California, Santa Barbara. He has published research on the management and impacts of foreign aid in a variety of leading journals, including Global Environmental Change, International Studies Quarterly, World Development, and Climate Policy. He is currently working with a number of implementing partners in Uganda, India, and China to design and execute impact evaluations related to environmental management and governance.

Marco, Millones, Ph.D

Assistant Professor, Department of Geography, University of Mary Washington, Fredericksburg, VA

Marco Millones is an Adjunct Assistant Professor of Geospatial Information Sciences at the University of Dallas School of Economic, Political and Policy Sciences. He has 10 years of academic and professional experience applying spatial analysis and remote sensing to build evidence on the drivers of land cover change for governments, non-profit organizations, and research institutes in several Latin American countries. He has published research in the International Journal of Remote Sensing, Transactions in GIS, Applied Geography and GEC.

Publications and Datasets

GIE & Aid Articles

Buntaine, M.T., Hamilton, S.E. & Millones, M.M. (2015). Titling Community Land to Prevent Deforestation: An Evaluation of a Best-Case Program in Morona-Santiago, Ecuador. Global Environmental Change. 33(0): 32-43.

Buntaine, M.T. (2015). Accountability in Global Governance: Civil Society Claims for Environmental Performance at the World Bank. International Studies Quarterly. 59(1): 99-111.

Hamilton, S.E. & Lovette, J.P. (2015). Ecuador's mangrove forest carbon stocks: A spatiotemporal analysis of living carbon holdings and their depletion since the advent of commercial aquaculture. PLoS One. 10(3).

Buntaine, M.T. & Pizer, W.A. (2015). Encouraging Clean Energy Investment in Developing Countries: What Role for Aid? Climate Policy.

Buntaine, M.T. & Parks, B.C. (2015). When Do Environmentally Focused Assistance Projects Achieve their Objectives? Evidence from World Bank Post-Project Evaluations. Global Environmental Politics. 13(2), 65-88.

Hamilton, S.E. (2015). No evidence that shrimp aquaculture is responsible for minimal mangrove deforestation. BioScience. 65(5), 457.

Cuba, N., Bebbington, A., Rogan, J., & Millones, M.M. (2014). Extractive industries, livelihoods and natural resource competition: Mapping overlapping claims in Peru and Ghana. Applied Geography. 54(1), 250-261.

Hamilton, S.E. & Stankwitz, C. (2012). Examining the relationship between international aid and mangrove deforestation in coastal Ecuador from 1970 to 2006. Journal of Land Use Science. 7(2), 177-202.

Neeti, N. et al inc. Millones, M.M. (2011). Mapping seasonal trends in vegetation using AVHRR-NDVI time series in the Yucatán Peninsula, Mexico. Remote Sensing Letters. 33(5), 433-432.

Buntaine, M.T. (2011). Does the Asian Development Bank Respond to Past Environmental Performance When Allocating Environmentally Risky Financing? World Development. 39(3), 336-350.

Rogan, J. et al Iinc. Millones, M.M. (2011). Hurricane disturbance mapping using MODIS EVI data in the southeastern Yucatán, Mexico. Remote Sensing Letters. 33(5), 259-267.

GIE & Aid Working Papers

Buntaine, M., Hamilton, S.E., & Millones, M.M. (2014). Titling Community Land to Prevent Deforestation: No Reduction in Forest Loss in Morona-Santiago, Ecuador. AidData Working Paper Series. 1(2).

Buch, M.P., Buntaine, M.T., & Parks, B.C. (2014). Aiming at the wrong targets: the difficulty of improving domestic institutions with international aid. AidData Working Paper Series. 1(4).

GIS Datasets

Hamilton, S.E. and Casey, D. (2016). Creation of a high spatiotemporal resolution global database of continuous mangrove forest cover for the 21st Century (CGMFC-21): A big-data fusion approach. Global Ecology and Biogeography,

The Ecuador GIE GIS data is now hosted by Global Environmental Change as a supplemental submission.