Research Data
| Data Description | Author | |
|---|---|---|
Gender and authorship patterns in urban land science |
The bibliometric data for the literature of urban land science used for this study, and the code for content analysis can be downloaded at: |
Karen Chen, Karen Seto |
Urban Land Expansion and Heat Island Intensification by 2050 |
The spatially explicit probabilistic projections of urban land expansion and heat island intensification by 2050 can be accessed from the following links: Urban land expansion: https://figshare.com/articles/Global_Urban_Land_Expansion_by_2050/7897010 Urban heat island intensification: https://figshare.com/articles/Global_Urban_Heat_Island_Intensification/7897433 —————————- Huang, K., Li, X., Liu, X., Seto, K.C. 2019. Projecting global urban land expansion and heat island intensification through 2050. Environmental Research Letters. Volume 14, Number 11. |
Karen Seto |
Global Inter-calibrated Nighttime lights |
Compressed archives containing global inter-calibrated nighttime lights (NTLs) (1992 - 2012) can be accessed from the following link: https://www.dropbox.com/s/b1wjqx0iniq0tr… Global inter-calibrated nighttime lights (NTLs) have been generated from stable NTL annual composite product (version 4) using a novel “Ridgeline Sampling and Regression” method (Zhang et al., 2016). Before use, all images will need to be re-scaled by multiplying pixel values with a scaling factor of 0.01. For more information, refer to Zhang et al. (2016). Zhang, Q., Pandey, B., & Seto, K. C. (2016). A Robust Method to Generate a Consistent Time Series From DMSP/OLS Nighttime Light Data. IEEE Transactions on Geoscience and Remote Sensing, 54(10), 5821 – 5831. http://doi.org/10.1109/TGRS.2016.2572724 |
Qingling Zhang, Bhartendu Pandey, Karen Seto |
Historical Urban Population Growth DataClick to Download Related Paper |
The attached files contain the .csv files and script used to create the spatially explicit, historic, global, city-level population dataset featured in “Spatializing 6,000 years of global urbanization from 3700 BC to AD 2000” (Reba et al. 2016). The dataset was created by digitizing, transcribing, and geocoding historical, archaeological, and census-based urban population data previously published in tabular form by Tertius Chandler and George Modelski. Additionally, we created a reliability ranking for each geocoded location to assess the geographic uncertainty of each data point. |
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Urban Expansion Meta-AnalysisClick to Download |
The attached files contain point data siting the studies used in “A Meta-Analysis of Global Urban Land Expansion” (Seto et al., 2011) as well as the UN-defined macro-regions used in the paper in shapefile format. |
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Urban expansion forecasts to 2030Click to Download |
The attached raster provides probabilistic projections of global urban expansion to 2030. It is described further in “Global forecasts of urban expansion to 2030 and direct impacts on biodiversity and carbon pools” (Seto, Güneralp, and Hutyra, 2012). Please read the Fair Use Policy contained in the readme file. |
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agLOSSClick to Download |
AgLOSS is an automated algorithm that detects phenological changes in crop cycles that indicate agricultural land abandonment due to urban land expansion. |
Bhartendu Pandey |