Urban GeoWeb 2
Health applications and data are becoming a greater part of the GeoWeb as more people start working with aggregated data sets to look for trends. of course, MIT is exerting some influence in this domain with their Health Infoscape project. From the project website:
Health InfoScape from MIT Senseable Cities and GE aims to create new ways of understanding human health in the United States. By analyzing data from over 7.2 million anonymized electronic medical records, taken from across the country, we are seeking to uncover statical relationships between space, geography and health.
We often have a tendency to think of illness as an isolated event, but our first analysis details the numerous (sometimes unexpected) associations that exist around any given condition. This gives us new insight as to how closely connected some seemingly un-related health conditions might be. Such results force us to re-examine conventional categories of disease classification, as the boundaries between traditional disease categories are thoroughly blurred.
Another big player in the GeoWeb domain is UCLA, with their URBANSENSING project, their vision:
We see a future in which we - individuals, neighbors, friends, and relatives - can use the technology around us to observe, discover, and act on the patterns that shape our lives. Whether your passion is personal or global, whether your interest is in health or the environment, whether you act alone or in a group, Urban Sensing is a new approach that empowers all of us to illuminate and change the world around us.
Within this context, there are two project that I think are particularly excellent: (1) Personalized Estimates of Environmental Exposure and Impact (PEIR), and; (2) Cycle Sense. PEIR seeks to document your environmental exposure within four categories - smog and fast food exposure, carbon impact and your impact on sensitive sites such as schools. This is accomplished through the use of location-based tech installed on your mobile devise.
Cycle Sense provide cyclists with current feedback on the quality and safety of the bike routes, and suggest modifications based on the time of day. This is accomplished within a larger community of cyclists who provide feedback by loggin their bike routes, and by providing geotagged annotations augmented by automatic sensor data common to many smart phones (sound, accelerometer) that can infer road roughness and traffic density.