Research Data

Data Description Author
Global Inter-calibrated Nighttime lights

Compressed archives containing global inter-calibrated nighttime lights (NTLs) (1992 - 2012) can be accessed from the following link:

https://yale.box.com/s/xwai7cjwpfuenqze8…

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 Sensing54(10), 5821 – 5831. http://doi.org/10.1109/TGRS.2016.2572724

Qingling Zhang, Bhartendu Pandey, Karen C. Seto
Historical Urban Population Growth Data

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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. 

Urban Expansion Meta-Analysis

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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.

Urban expansion forecasts to 2030

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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.

agLOSS

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Related Paper

AgLOSS is an automated algorithm that detects phenological changes in crop cycles that indicate agricultural land abandonment due to urban land expansion. It was developed in conjunction with “An automated algorithm to detect timing of urban conversion of agricultural land with high temporal frequency MODIS NDVI data.”

Bhartendu Pandey