Overview
During an emergency situation, we often need geospatial information. For example, we might need to find our way out of an area that has been flooded or extensively damaged. In other situations, it might be safer to stay put, but we still need to know what is going on around us. For example, we can listen to the radio during a hurricane to find out where tornado and extreme wind warnings have been issued. However, to understand our actual danger level, we need to know how close those warnings are to our location. If the Internet and cell service are down, we're going to need another way to get this information (perhaps via a paper map, if we remembered to print or buy one).
While we recover from a disaster, assets that we need for rebuilding might need to be transported from one location to another. For example, if we know we need 500 meters of fiber optic cable to restore a critical switch to bring the Internet back online for a neighborhood, it would be helpful to know where the spool of cable is located and how to get it to the installation location. Plotting this information on a map would be a natural way to solve this problem.
In each of these examples, we're using map-based (or geospatial) data. However, we're using it for different purposes. As it stands today, we would probably try to get an offline GPS navigation app (like Organic Maps) to try to find our way when navigating. For understanding where we are and what is going on around us, we'd either have to use a paper map or an offline map viewer (such as Marble). When it comes to making maps or plotting geospatial information, we normally use a Geographic Information Systems (GIS) application like ArcGIS or QGIS.
Our current state of the art is designed around the assumption that the grid and the Internet are at least partially available. Navigation apps tend to run on phones, while offline map viewers can run on a variety of devices. GIS systems are designed to run either in the cloud or on relatively powerful workstations, either of which need the grid. GIS applications also have a pretty steep learning curve, which is a major reason you can obtain an entire GIS minor from CCU.
This greenfield project idea is to build a geospatial application that can run on a lightweight, low-power, off-grid machine. Out of the box, the application would be designed to be easy to use while supporting the following features:
- Location tracking using GPS and dead reckoning (using inertial navigation gyroscopes and/or accelerometers).
- Use of a common format (like ESRI shapefiles) for map data to make importing and exporting information easier.
- The ability to collect geospatial data in the field and put it onto the map.
- A limited capability to edit maps, making the system into a lightweight GIS tool that could run with minimal processing power and without a steep learning curve.
- An ability to plot geospatial data on top of the map using latitude/longitude, raster images, polar coordinates, and KML files.
- Navigation using Dijkstra's Algorithm with adjustable weights (for example, marking a road as closed and finding an alternate route).
Related Research
In the long run, this application could be used as a base to develop other research tools for field data collection, engineering/surveying, and geospatial analysis. There are likely a number of niche use cases where a lightweight system would be preferable to a full GIS application, but where a smartphone app would not be appropriate.