MAP LARGE, INC. — Department of Defense SBIR Phase I: SB132-002
MAP LARGE, INC. — SBIR Phase I award from Department of Defense.
Phase I SBIR feasibility signal
- Phase I awards fund proof-of-concept work. For capture teams, they mark early interest from Department of Defense in a technical approach.
- Watch for Phase II follow-ons from the same firm/topic family — that conversion path is where budgets and transition pressure rise.
- Obligated amount $99,731. Cross-check similar awards in the same agency and technology tags for going-rate context.
- Topic code SB132-002 links this award to a solicitation family — search the same topic stem for incumbents and recompete timing.
- Amount
- $99,731
- Agency
- Department of Defense · Defense Advanced Research Projects Agency
- Program / Phase
- SBIR · Phase I
- Topic
- SB132-002
- Solicitation
- 2013.2
- NAICS
- —
- Place of performance
- GA
- Period
- 2013-12-05 → 2014-09-05
Description
The emergence of low cost GPS enabled sensors and mobile devices has dramatically increased the volume of streaming spatial data available. We propose to build streaming tools capable of doing the following in real time: (1) automatically extracting spatial coordinates from streams of (a) structured values like latitude and longitude coordinates, and (b) extracting and geocoding location indicators from free text fields like place names, street addresses, IP addresses and other spatial indicators (2) the calculation of (a) nonspatial and spatial aggregate statistics using (b) spatial clustering methods tunable algorithms, point-in-polygon aggregation using either operator supplied polygons or dynamically computer polygons contours, (3) interactive visuals in the form of (a) maps, charts and tables, (b) that are zoomable, dynamically queryable so that analysts cannot just visualize but also iteratively ask questions and explore their data, and (c) provide a simple user interface for composing visual components into dashboards without programming and (d) a JavaScript API that allows moderately experienced web developers to extend the library of predefined visuals with their own components without requiring specialized big data or cloud computing skills.