Through an intelligent combination of geotagged social media, global location and natural language data, ORNL’s Junchuan Fan and Gautam Thakur developed MapSpace, a publicly available, scalable land-use modeling framework. By providing data characteristics broader and deeper than satellite imagery alone, MapSpace can generate population analytics invaluable for urban planning and disaster response. The researchers’ findings were published in the International Journal of Digital Earth.
“While the land cover—land, sea, water or desert—doesn’t change, the use of that land changes all the time,” said Thakur, leader of ORNL’s Location Intelligence group. “Understanding how land-use patterns change is essential for development of new services.“ Thakur said challenges with traditional satellite data are twofold. First, processing data accurately is an enormous task because of long download times and requirements of high-resolution imagery. Second, satellite images show only the tops of structures rather than their facades, which are more familiar vantage points. By using points of interest and geosocial data, Thakur and Fan can achieve multiple levels of semantic granularity, a measure of how accurately land use can be characterized.
For example, MapSpace could allow users to see that an area is commercial, zoom down to see it’s used for restaurants, then zoom in further to see the specific type of food served at a restaurant in the area. The ability to capture land-use characteristics includes not only how buildings and areas are used but also how their function can change over weeks or even throughout a single day. “Land-use layers are traditionally static, but there are various places in the world where the use of land changes during the day,” such as a town square used as a farmer’s market on weekend mornings, Thakur said. “How to capture these dynamics of spaces and how they change is also one of the things Junchuan has been spearheading. “By understanding change over time, researchers could suggest new land-use purposes that may not have been possible before. For example, desolate urban areas that once contained thriving shopping malls could potentially be put to better use for developing apartments or industrial centers.
