GIS Map Gallery Christina
Geographic Information Systems (GIS) is an innovative way of visualizing data spatially to understand relationships, patterns and trends.
Third Coast CFAR offers GIS services to members through the BSIS Core. If you are interested in using GIS, please request a consultation using via the service request form.
Descriptive Maps are used for reference of geographic locations, or places.
Choropleth Maps use aggregate data to depict geographic distribution
Categorical Maps are used to show data or places that are different in kind rather than in amount.
Typical ways of depicting these differences is through color, symbol type, or size.
Buffer Analysis shows data within a specific distance or time of a point
Density Maps show high and low density areas of specific points.
Combining Map Types
To show possible relationships between two types of geographic data. Great for brainstorming new research questions and understanding resource allocations.
Story Slider maps are interactive
and allow you to show various types of maps across two different data points.
ArcGIS Online: Story Slider Maps
They are hosted on the Northwestern ArcGIS online platform. A link to the map for distribution will be provided.
Story Map Series are interactive
and allow you to present a series of related maps, videos, images and text in a story format.
ArcGIS Online: Story Map Series
They are hosted on the Northwestern ArcGIS online platform, but offer an HTML code block for your website.
Hot Spot Analysis identifies statistically significant hot spots and cold spots for a specific attribute.
Hot Spot Analysis
Average Nearest Neighbor
Average Nearest Neighbor measures the distance between a feature centroid (or point) and its nearest neighbor's centroid (or point). It then averages all the distances to determines if the distance between the points are based on random chance.
This Average Nearest Neighbor is computed from the CTA Bus Stops Map (see Density Map example)
Moran's I Spatial Autocorrelation
Moran's I measures spatial autocorrelation, or correlation of a variable with itself through space,
based on feature locations and values simultaneously. To measure this, it needs a set of features and an associated attribute. It then evaluates whether the pattern expressed is based on random chance.
This Moran's I Spatial Autocorrelation is computed from the CDPH 2013-14 Average Annual HIV Infections Map (see Choropleth Map example)