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Using Deep Learning To Forecast Spatiotemporal Crime Patterns, Shuzhan Fan
Using Deep Learning To Forecast Spatiotemporal Crime Patterns, Shuzhan Fan
LSU Doctoral Dissertations
The distributional patterns of crime occurrences are closely related to their spatial, temporal, and environmental contexts. It has been a hot topic for researchers and crime analysts to discover such complex relationships in order to forecast crime, both spatially and temporally. Many factors play a role in the occurrences of crimes. Conventional crime forecasting research has primarily relied on historical crime records and socioeconomic data, while ignoring the rich social media and other environmental context data. The large volume of data requires a more appropriate forecasting framework with the ability to take in massive multimodal data and possibly achieve better …
Widespread Infilling Of Tidal Channels And Navigable Waterways In The Human-Modified Tidal Deltaplain Of Southwest Bangladesh, C. Wilson, S. Goodbred, C. Small, J. Gilligan, S. Sams, B. Mallick, R. Hale
Widespread Infilling Of Tidal Channels And Navigable Waterways In The Human-Modified Tidal Deltaplain Of Southwest Bangladesh, C. Wilson, S. Goodbred, C. Small, J. Gilligan, S. Sams, B. Mallick, R. Hale
Faculty Publications
Since the 1960s, ~5000 km of tidal deltaplain in southwest Bangladesh has been embanked and converted to densely inhabited, agricultural islands (i.e., polders). This landscape is juxtaposed to the adjacent Sundarbans, a pristine mangrove forest, both well connected by a dense network of tidal channels that effectively convey water and sediment throughout the region. The extensive embanking in poldered areas, however, has greatly reduced the tidal prism (i.e., volume of water) transported through local channels. We reveal that >600 km of these major waterways have infilled in recent decades, converting to land through enhanced sedimentation and the direct blocking of …
Developing Risk Assessment Maps For Schistosoma Haematobium In Kenya Based On Climate Grids And Remotely Sensed Data, Kelsey Lee Mcnally
Developing Risk Assessment Maps For Schistosoma Haematobium In Kenya Based On Climate Grids And Remotely Sensed Data, Kelsey Lee Mcnally
LSU Master's Theses
It is important to be able to predict the potential spread of water borne diseases when building dams or redirecting rivers. This study was designed to test whether the use of a growing degree day (GDD) climate model and remotely sensed data (RS) within a geographic information system (GIS), could be used to predict both the distribution and severity of Schistosoma haematobium. Growing degree days are defined as the number of degrees centigrade over the minimum temperature required for development. The base temperature and the number of GDD required to complete one generation varies for each species. A monthly climate …