2026-06-24T06:22:23Z https://datacatalogue.cessda.eu/oai-pmh/v0/oai

b4ecacc636dfb61d45600ccc04087578c3cd89417d99c28db55c69bcfd98db92 2026-06-02T13:05:29Z language:enopenaire_data

Predictive land value modelling in Guatemala City using a geo-statistical approach and Space Syntax Predictive land value modelling in Guatemala City using a geo-statistical approach and Space Syntax doi:10.17026/DANS-23T-7A8Ueasy-dataset:163275 ANDRES Morales 2017-03-09 DANS Data Station Social Sciences and HumanitiesFaculty of Geo-information Science and Earth Observation (ITC), University of Twente 2020-06-05 Social Sciencesland value; Guatemala City; Space Syntax; regression-kriging; geostatistics; <p>The datasets were produced during the doctoral research titled "A modelling framework to estimate the effects of future transport interventions on land values". Specifically, the datasets were produced and prepared with the purpose of training a geostatistical model and construct a residential land value map for Guatemala City using a predictive approach. The file "gc_log" contains a set of spatial points representing parcel centroids of the observations that were used to train a regression-kriging model. A spatialized variable selection was implemented in order to retain only important variables. The "grid_prediction.csv" contains a set of spatial points representing the centroids of an hexagonal tessellation containing values for all the required predictors of land value. The "research_code.R" file contains the scripts developed to implement the spatial variable selection, train the regression-kriging model and construct the land value map.</p>Date: 2020-05-29 https://ssh.datastations.nl/oai doi:10.17026/DANS-23T-7A8U 2025-12-03T01:00:29Z ddi:codebook:2_5