The 'Health GIS' laboratory offers an extensive array of spatial (location-based) data services, including data collection apps, preparation, mapping, and advanced spatial and spatio-temporal analysis and modeling. Additionally, the lab offers consultation, workshops, training, and collaborative research opportunities. Here is a list of our services on SPARCRequest (https://sparc.musc.edu/):

1. DATA  COLLECTION  &  PREPARATION

• Enrich your data with demographics, environmental, and socio-economic variables.

• Field data collection using web or mobile devices, in both offline and online scenarios to incorporate location-based data or geo-tagged photos to your surveys.

• Geocoding (converting physical addresses to geographic coordinates for mapping)

• Georeferencing (assigning latitude and longitude to paper maps or images)

• Digitizing (conversion of analog or paper maps into GIS format).

• Spatial interpolation (to estimate values for unmeasured or unsampled locations considering spatial correlations)

• GPS data collection and mapping

2.  SPATIAL  MAPPING

After data collection and preparation, it is recommended to map the data. There are various high-quality maps we can generate:

• Static thematic maps (dot maps, choropleth maps, flow line maps, proportional symbol maps, contour maps, bi-variate maps, heat maps, etc)

• 3D Maps

• Animated or simulation maps

• Interactive web maps (enables users to explore and engage with maps through user-friendly web interfaces, offering interactive features like zooming, data query, navigating, and manipulation)

• GIS Dashboards (composed of configurable elements, such as maps, lists, charts, gauges, indicators, and tables that work together on a single screen, and allows you to monitor events, see trends, and enhance decision support)

• Story Maps (web-based maps that combine maps with narrative text, multimedia, and other content to tell a story or convey information)

3. SPATIAL  ANALYSIS

While mapping is a valuable tool for visualizing geographic data, spatial analysis is necessary to delve deeper into understanding and interpreting patterns and relationships within the data.

• Hotspot and cold spot detection (to detect areas with significantly high or low concentrations)

• Space-time clustering (to identify hotspots that are concentrated both in specific geographic locations and specific time periods)

• Site selection and overlay analysis (to choose the most appropriate locations considering various factors and criteria)

• Proximity analysis (such as buffer analysis, service area, nearest neighbors, closest facilities)

• Measuring healthcare accessibility (to identify areas with adequate or inadequate access to healthcare resources)

• Location-allocation analysis (to optimize the placement of facilities, resources, or services to maximize accessibility and minimize costs)

• Spatial modeling: to explore spatial relationships at the local levels.

4. AUTOMATING  GIS  WORKFLOWS

To streamline processes, reduce the need for manual, repetitive tasks and enhance the efficiency of spatial data management