Collaborating Authors

Mining GIS Data to Predict Urban Sprawl Artificial Intelligence

This paper addresses the interesting problem of processing and analyzing data in geographic information systems (GIS) to achieve a clear perspective on urban sprawl. The term urban sprawl refers to overgrowth and expansion of low-density areas with issues such as car dependency and segregation between residential versus commercial use. Sprawl has impacts on the environment and public health. In our work, spatiotemporal features related to real GIS data on urban sprawl such as population growth and demographics are mined to discover knowledge for decision support. We adapt data mining algorithms, Apriori for association rule mining and J4.8 for decision tree classification to geospatial analysis, deploying the ArcGIS tool for mapping. Knowledge discovered by mining this spatiotemporal data is used to implement a prototype spatial decision support system (SDSS). This SDSS predicts whether urban sprawl is likely to occur. Further, it estimates the values of pertinent variables to understand how the variables impact each other. The SDSS can help decision-makers identify problems and create solutions for avoiding future sprawl occurrence and conducting urban planning where sprawl already occurs, thus aiding sustainable development. This work falls in the broad realm of geospatial intelligence and sets the stage for designing a large scale SDSS to process big data in complex environments, which constitutes part of our future work.

Facebook uses satellite imagery machine learning and AI


Facebook uses satellite imagery machine learning and AI to prepare maps for locating unconnected communities across the world. Maps tell us so much more than how to get from A to B, or where C is in relation to D. They can be tools of power and snapshots of history; they can give urban planners the information to plan infrastructure. After a disaster, population density and crisis maps help to direct aid and aid workers. Throughout time, different cultures and industries have produced radically different images of the world. Today there are more than 7 billion humans sprawling across Earth.

New Earth Surveillance Tech Is About to Change Everything, Including Us


On Christmas Eve, 1968, the astronauts aboard NASA's Apollo 8 spacecraft became the first humans to behold the entirety of Earth with their own eyes. That day, crew member Bill Anders took an iconic photograph called "Earthrise'' that captured our home world emerging from behind the Moon's horizon. "We came all this way to explore the Moon, and the most important thing is that we discovered the Earth," Anders famously said of his mission. More than 50 years later, Earth is being rediscovered from space once again, but this time it is through the "eyes" of satellites, supercomputers, and artificial intelligence (AI) networks. Geospatial science, a sprawling and multifaceted field dedicated to resolving ever-finer details about Earth and its systems, is poised to undergo an unprecedented growth spurt powered by this confluence of technologies across both the public and private sectors. "With the proliferation of satellite platforms, essentially this is something that's almost become impossible to keep a handle on because there are so many new systems being launched and developed by so many different actors globally," said Jonathan Chipman, director of Dartmouth College's Citrin Family GIS/Applied Spatial Analysis Laboratory, in a call. "It's just mind-boggling the amount of data that's now being collected from low-Earth orbit." The feeling of epiphanic connection with the planet experienced by astronauts gazing at Earth is known popularly as "the overview effect," a term coined by author Frank White in his book of the same name. The new geospatial view of Earth, however, may offer something closer to an "overwhelm effect," as our home world is imaged, valued, and monitored by millions of sensors on thousands of spacecraft orbiting Earth. How will we deal with the petabytes of Earth-observation data that may document the collapse of whole ecosystems or the wreckage of natural disasters? What will we do with geospatial information that predicts such dire outcomes but also demands nimble and dramatic changes to our lifestyles? It will take foresight to ensure that the deluge of information is managed in a way that equitably benefits communities and ecosystems around the world, and remains as accessible to the public as possible. "The biggest challenge will be in making sense of all these data," said Dawn Wright, chief scientist of the Environmental Systems Research Institute (Esri), a major geospatial software and data science company, in an email. "It is one thing to store, to distribute, even to analyze, but how do truly understand it?

Geospatial Principal Data Scientist at NielsenIQ - Madrid, Spain


NielsenIQ is a global measurement and data analytics company providing the most complete and trusted view of consumers and markets in 90 countries covering 90% of the world's population. Focusing on consumer-packaged goods manufacturers and FMCG and retailers, we enable customers to defy what's possible. We combine unparalleled datasets, pioneering technology, and the industry's top talent to create insights that unlock innovation. Join us and change the landscape. Our Data Science teams help to provide NielsenIQ's clients with the most complete understanding of the market and its consumers.

Mapping and Describing Geospatial Data to Generalize Complex Mapping and Describing Geospatial Data to Generalize Complex Models: The Case of LittoSIM-GEN Models Artificial Intelligence

For some scientific questions, empirical data are essential to develop reliable simulation models. These data usually come from different sources with diverse and heterogeneous formats. The design of complex data-driven models is often shaped by the structure of the data available in research projects. Hence, applying such models to other case studies requires either to get similar data or to transform new data to fit the model inputs. It is the case of agent-based models (ABMs) that use advanced data structures such as Geographic Information Systems data. We faced this problem in the LittoSIM-GEN project when generalizing our participatory flooding model (LittoSIM) to new territories. From this experience, we provide a mapping approach to structure, describe, and automatize the integration of geospatial data into ABMs.