Recent Publications

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. A Simple Spatially Weighted Measure of Temporal Stability for Data with Limited Temporal Observations. In ISPRS Annals, 2017.

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. The World Spatiotemporal Analytics and Mapping Project (WSTAMP): Further Progress in Discovering, Exploring, and Mapping Spatiotemporal Patterns Across the World’s Largest Open Source Data Sets. In ISPRS Annals, 2017.

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. A Hybrid Dasymetric and Machine Learning Approach to High-Resolution Residential Electricity Consumption Modeling. In Advances in Geocomputation, 2017.

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. Attribute Portfolio Distance: A Dynamic Time Warping-Based Approach to Comparing and Detecting Common Spatiotemporal Patterns Among Multiattribute Data Portfolios. In Advances in Geocomputation, 2017.

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. Can Social Media Play a Role in the Development of Building Occupancy Curves?. In Advances in Geocomputation, 2017.

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Recent Posts

19 min read R, wbstats
The World Bank is a tremendous source of global socio-economic data; spanning several decades and dozens of topics, it has the potential to shed light on numerous global issues. The wbstats R package provides access to this data. This post is meant to serve as a reference for getting started with using wbstats. There are lots of things that aren’t mentioned, particularly several of the wb arguments that can be changed.

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