Documentation and Use Cases

Presentations and uses of GeoQuery from around the world - including powerpoints, PDFs, tools and uses from other practitioners.

Tools and Code

The raw github code powering GeoQuery, and a suite of R-based examples and packages that assist in the use of data from GeoQuery.

Data providers & Licenses

Information about the sources of data we integrate into GeoQuery, our data federation model, and licenses for our software and content.

Data Providers and Licenses

List of Data Providers

UMD Global Land Cover Facility

The Global Land Cover Facility (GLCF; http://glcf.umd.edu) provides earth science data and products to help everyone to better understand global environmental systems. In particular, the GLCF develops and distributes remotely sensed satellite data and products that explain land cover from the local to global scales.  They are involved in the production of most of the land cover datasets currently available in GeoQuery.  While we passively collect data from the GLCF, researchers within the facility have worked closely with us to help overcome and understand limitations of the data.

AidData

AidData (www.aiddata.org) is a research lab based at William and Mary dedicated to tracking and analyzing flows of international aid.  All of the information regarding international aid currently in GeoQuery is provided by AidData.  AidData is a primary data provider to GeoQuery, and GeoQuery itself is based out of AidData.

NOAA Earth Observation Group

The NOAA EOG has provided nighttime lights imagery, including the DMSP and VIIRS data series.  While a passive data provider, NOAA scientists have provided us with coefficients for the DMSP products to make them more comparable over time.

UDEL Global Climate Resources

UDEL is a passive data provider to GeoQuery.  They describe themselves as:

In response to increasing demand, the UDEL Global Climate Resource site (http://climate.geog.udel.edu/~climate/) was developed primarily by Kenji Matsuura and C. Willmott to help distribute easily and widely the gridded climate data sets, documentation and related publications produced by Willmott, Matsuura and collaborators. Support from NASA is most appreciated, as is the many fundamental contributions made by colleagues and former graduate students. David Legates, Scott Robeson, Clint Rowe, Johan Feddema, Scott Webber, Mike Rawlins, Petra Zimmermann, Yale Mintz and Charlie Vorosmarty all have made significant contributions. 

Armed Conflict Location and Event Data Project

ACLED is a passive data provider to the GeoQuery project. They describe themselves as follows:

ACLED (Armed Conflict Location and Event Data Project) is designed for disaggregated conflict analysis and crisis mapping. This dataset codes the dates and locations of all reported political violence and protest events in over 60 developing countries in Africa and Asia. Political violence and protest includes events that occur within civil wars and periods of instability, public protest and regime breakdown. The project covers all African countries from 1997 to the present, and South and South-East Asia in real-time.

Uppsala Conflict Data Program (UCDP)

UCDP is a passive data provider for the GeoQuery project.  They describe themselves as follows:

The Uppsala Conflict Data Program (UCDP) has recorded ongoing violent conflicts since the 1970s. The data provided is one of the most accurate and well-used data-sources on global armed conflicts and its definition of armed conflict is becoming a standard in how conflicts are systematically defined and studied.

Click here for Additional License Information

Data License and Provisions of Use

Each dataset provided through GeoQuery comes from public, open datasets; however, the licenses governing each dataset are determined by the source data provider.  Metadata is provided to enable users to appropriately cite data sources with every data request put through the GeoQuery system.

GeoQuery Licenses

We release most products produced by the GeoQuery team under one of three licenses:

DataWhile we produce relatively little primary data, all databases we create as a part of the GeoQuery tool, related standards, and procedures for creating those databases insofar as they are considered a part of the data are licensed under the Open Data Commons Attribution License.  The Open Data Commons Attribution license is a license agreement intended to allow users to freely share, modify, and use this Database subject only to attribution requirements.

Software - All software produced by the GeoQuery project is licensed following the MIT license.  In cases where software copyrights belong to specific individuals on the project, such copyrights are noted on those software repositories.

Copyright (c) 2017 Seth Goodman, Dan Runfola, Ariel BenYishay
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

Documentation - Documents produced by the GeoQuery project are made available under the Creative Commons Attribution-ShareAlike 4.0 International Public License, except where we are prohibited to do so.  In cases where a document (i.e., an Academic Publication) is governed by a different license, it is explicitly noted.

Contributors to the GeoQuery Dataset

Inspired by the data federation model of the Research Applications Laboratory, GeoQuery is made possible through the voluntary contributions of data from dozens of institutions, ranging from government-funded agencies to universities and other research groups.  Without these contributors, GeoQuery would not be possible.  Each data extraction requested through GeoQuery provides full metadata on the sources of data - if you don't cite us, at least cite them!

Types of Data Providers

Data are contributed to the project following three tiers of engagement:

Passive Data Providers - Many research labs make their datasets openly available via FTP or other mechanisms.  In these cases, our scripts periodically download the data from these sources.

Active Data Providers - In some cases, datasets are not openly available on the web.  In these instances, we work directly with data providers to acquire relevant datasets for inclusion into GeoQuery.

Primary Data Providers - Being based out of the AidData research lab, we work with affiliates of AidData to directly collect and include novel information into GeoQuery (with a primary focus on the geographic location of international aid interventions).

Documentation & Uses

Whitepapers:
Goodman, S., BenYishay, A., Runfola, D., 2017. GeoQuery: Dynamic High Performance Computation for the Retrieval of Arbitrary Spatial Data. Summary Available online at http://www.geoquery.org/. DOI: 10.13140/RG.2.2.28363.59686
Marty, R., Goodman, S., LeFew, M., BenYishay, A., Runfola, D. 2016. GeoSIMEX: A R Package for Estimating Parameters in Linear Models incorporating Spatial Imprecision. https://github.com/itpir/geoSIMEX
Runfola, D., Lv, Miranda, BenYishay, A., 2017. GeoMatch: A R Package for Propensity-Score Matching in Conditions of Intervention Spillover. https://github.com/itpir/geoMatch
Reports:
World Bank Group - Macroeconomics & Fiscal Management. Myanmar Economic Monitor, October 2017.
Batra, G., Anand, A., Goodman, S., BenYishay, A., Nyoteshwar, J., Runfola, D., 2017. A Value for Money Analysis of GEF Interventions in Land Degradation and Biodiversity, http://www.gefieo.org/evaluations/value-money-analysis-gef-interventions-land-degradation-and-biodiversity
Runfola, D.M., BenYishay, A., Buchanan, G., Nagol, J., Tanner, J., Environmental Impact of World Bank Projects: A Case Study of Integrating Value for Money Analysis into Impact Evaluations. What Works? Value for Money in Impact Evaluations. Independent Evaluation Group, the World Bank.
Overseas Development Institute (ODI). Aid allocation within countries: Does it go to areas left behind? July 2017.
Expertgruppen för biståndsanalys (EBA). Geospatial analysis of aid: A new approach to aid evaluation. 2017. Isaksson, Ann-Sofie.
Academic Publications
BenYishay, A., Heuser, S., Runfola, D.M., Trichler, R. 2017. Indigenous land rights and deforestation: Evidence from the Brazilian Amazon. Journal of Environmental Economics and Management. https://doi.org/10.1016/j.jeem.2017.07.008
Runfola, D.M., Napier, A., 2016. “Migration, climate, and international aid: examining evidence of satellite, aid, and micro-census data.” Migration and Development.
Zhao, J., Kemper, P., Runfola, D., 2017. Quantifying Heterogeneous Causal Treatment Effects in World Bank Development Finance Projects. Data Mining and Knowledge Discovery (ECML PKDD). http://ecmlpkdd2017.ijs.si/papers/paperID507.pdf
Runfola, D., Ariel BenYishay, Jeffery Tanner, Graeme Buchanan, Jyoteshwar Nagol, Matthias Leu, Seth Goodman, Rachel Trichler and Robert Marty. 2017. A Top-Down Approach to Estimating Spatially Heterogeneous Impacts of Development Aid on Vegetative Carbon Sequestration. Sustainability 9(3), 409. doi:10.3390/su9030409. http://www.mdpi.com/2071-1050/9/3/409.
Zhao, J., Runfola, D., Kemper, P. 2017. Simulation Study in Quantifying Heterogeneous Causal Effects. WSC Proceedings. Available online at: http://informs-sim.org/wsc17papers/
Marty, R., Dolan, C., Leu, M., Runfola, D. 2017. Taking the Health Aid Debate to the Sub-National Level: The Impact and Allocation of Foreign Health Aid in Malawi. BMJ Global Health. DOI: 10.1136/bmjgh-2016-000129.
Other
Juha Uitto, Geeta Batra, Anupam Anand, Runfola, D., Ariel BenYishay and Jyothy Nagol, Geospatial Impact Evaluation and Valuation of Land Degradation Projects, World Bank Land and Poverty Conference, Spring 2017
Democracy Digest. Development and governance key to national security. 2017. https://www.demdigest.org/cutting-soft-power-undermines-cve-national-security-imperatives/
Learning as a Global Community: The Potential of Spatial Data in Development. Geospatial Day, World Bank, Fall 2017. Runfola, D.
High Performance Computation for Everyone: Introducing GeoQuery. GeoComputation, Leeds, UK, Fall 2017. Goodman, S., Lv, M., Runfola, D.
LeFew, M., Marty, R., Goodman, S., Runfola, D., A. Modeling Geographic Uncertainty in Aid Allocation for Linear Causal Inference, Association of American Geographers, April 2017.
YouthMappers. Introducing GeoQuery, Grace Grimsley, Nov 2017.
Virginia ACCORD Project. 2018. Science Use Cases.