DOI: 10.6073/pasta/5194fc22aaa4e0a5d58cfc6884498aa0
title: Mapping and Modeling Clandestine Drivers of Urban Expansion in Mexico City (2016-2019)

description: This dataset incorporates Mexico City related essential data files associated with Beth Tellman's dissertation: Mapping and Modeling Illicit and Clandestine Drivers of Land Use Change: Urban Expansion in Mexico City and Deforestation in Central America. It contains spatio-temporal datasets covering three domains; i) urban expansion from 1992-2015, ii) district and section electoral records for 6 elections from 2000-2015, iii) land titling (regularization) data for informal settlements from 1997-2012 on private and ejido land. The urban expansion data includes 30m resolution urban land cover for 1992 and 2013 (methods published in Goldblatt et al 2018), and a shapefile of digitized urban informal expansion in conservation land from 2000-2015 using the Worldview-2 satellite. The electoral records include shapefiles with the geospatial boundaries of electoral districts and sections for each election, and .csv files of the number of votes per party for mayoral, delegate, and legislature candidates. The private land titling data includes the approximate (in coordinates) location and date of titles given by the city government (DGRT) extracted from public records (Diario Oficial) from 1997-2012. The titling data on ejido land includes a shapefile of georeferenced polygons taken from photos in the CORETT office or ejido land that has been expropriated by the government, and including an accompany .csv from the National Agrarian Registry detailing the date and reason for expropriation from 1987-2007. Further details are provided in the dissertation and subsequent article publication (Tellman et al 2021). The Mexico City portion of these data were generated via a National Science Foundation sponsored project (No. 1657773, DDRI: Mapping and Modeling Clandestine Drivers of Urban Expansion in Mexico City). The project P.I. is Beth Tellman with collaborators at ASU (B.L Turner II and Hallie Eakin). Other collaborators include the National Autonomous University of Mexico (UNAM), at the Institute of Geography via Dr. Armando Peralta Higuera, who provided support for two students, Juan Alberto Guerra Moreno and Kimberly Mendez Gomez for validating the Landsat urbanization algorithm. Fidel Serrano-Candela, at the UNAM Laboratory of the National Laboratory for Sustainability Sciences (LANCIS) also provided support for urbanization algorithm development and validation, and Rodrigo Garcia Herrera, who provided support for hosting data at LANCIS (at: http://patung.lancis.ecologia.unam.mx/tellman/). Additional collaborators include Enrique Castelán, who provided support for the informal urbanization data from SEDEMA (Ministry of the Environmental for Mexico City). Electoral, land titling, and land zoning data were digitized with support from Juana Martinez, Natalia Hernandez, Alexia Macario Sanchez, Enrique Ruiz Durazo, in collaboration with Felipe de Alba, at CESOP (Center of Social Studies and Public Opinion, at the Mexican Legislative Assembly). The data include geospatial time series data regarding changes in urban land cover, digitized electoral results, land titling, land zoning, and public housing. Additional funding for this work was provided by NSF under Grant No. 1414052, CNH: The Dynamics of Multiscalar Adaptation in Megacities (PI H. Eakin), and the NSF-CONACYT GROW fellowship NSF No. 026257-001 and CONACYT number 291303 (PI Bojórquez). References: Tellman, B., Eakin, H., Janssen, M.A., Alba, F. De, Ii, B.L.T., 2021. The Role of Institutional Entrepreneurs and Informal Land Transactions in Mexico City’s Urban Expansion. World Dev. 140, 1–44. https://doi.org/10.1016/j.worlddev.2020.105374 Goldblatt, R., Stuhlmacher, M.F., Tellman, B., Clinton, N., Hanson, G., Georgescu, M., Wang, C., Serrano-Candela, F., Khandelwal, A.K., Cheng, W.-H., Balling, R.C., 2018. Using Landsat and nighttime lights for supervised pixel-based image classification of urban land cover. Remote Sens. Environ. 205, 253–275. https://doi.org/10.1016/j.rse.2017.11.026