govtech
What do city parks, mobile intelligence, and machine learning have in common? CITYDATA! CITYDATA.ai was featured at STIR [https://startupinresidence.org/] Demo Day in San Francisco last week. We showcased our "People Intelligence" platform for the public sector: smart cities and local government. Many cities have embraced
govtech
The Silicon Valley world of top tech companies and startups often comes under fire for being too male, too white and too wealthy. For several years, the privileged group of tech workers in the Bay has continued to gentrify many San Francisco neighborhoods. Subsequently rising rents and new, high-end restaurants/
govtech
From the 1950s onward, the U.S. has been a nation of car commuters. Our country developed alongside the rise of the automobile and the present-day urban geography reflects the car's influence. With an average daily commute of 25.4 minutes, 85% of Americans still drive to work
govtech
C [http://lotadata.com/]ITYDATA.ai transforms time and place into actionable context for smart cities, enterprise businesses, mobile apps and wearables. STIR 2016 One of the few companies selected to join STIR 2016, CITYDATA will bring location intelligence, predictive analytics and machine learning capabilities to the program. The STIR
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History Dallas is "the city without a reason to exist," the city that built itself up from its bootstraps. Thanks to the determined grit of early settlers, Dallas had the chance to thrive. Capitalizing on the opportunity, Dallas slowly grew into the main economic powerhouse of North Texas.
visualization
The U.S. Census Bureau [https://www.census.gov/en.html] provides a very intricate and sometimes unstructured hierarchy of geospatial geometries that link back to the U.S. demographic data. This post dives deeper into the geometries or polygons, as a follow up up to our previous post [http:
govtech
Intro What neighborhood related datasets are openly available, how good are they, and how can I use them? These are essential questions for any project looking to use or understand neighborhoods. This first of a 4-part series delves into the geometries of neighborhoods. The following three parts of this series
govtech
Our neighborhood rolls down the hills Through happy farms to the old mill Green oaks, red doors, enchanting collage The occasional tremor and a foot massage The old downtown's still smart and fine Inclusive, affluent, spiritual, divine Sumptuous restaurants, boisterous bars Drones, hoverboards and electric cars Kids, teens,
govtech
Pinpointing the composition, coordinates, and concept of a single neighborhood is a tough proposition. To understand these elements, we need more than simple data collection - we need a philosophical and sociological approach toward the definition. Neighborhood Composition Philosophically, something deeper than location ties a neighborhood together. After all, the
govtech
You are cordially invited to the spatiotemporal intelligence blog by LotaData. We are the experts in location-based predictive technologies. Our data platform is open to all and free to try. Our APIs and SDKs provide the situational context with relevant content to create highly personalized mobile experiences. Our business intelligence