What is a GIS?
The most common way that spatial data is processed and analyzed is using a GIS, or, geographic information system. These are programs or a combination of programs that work together to help users make sense of their spatial data. This includes management, manipulation and customization, analysis, and creating visual displays. A user will typically use multiple spatial datasets at one time and compare them or combine them with one another. Each spatial dataset may be referred to as a layer.
If you were using GIS for a municipality project, you might have vector data like street data (lines), neighbourhood boundary data (polygons), and high school locations (points). Each dataset would exist as its own layer in your GIS. Placement of layers is important for visual purposes as it will help you understand the various types of data and present your findings in an easily understandable way. In this case, you would want to make sure that high school points and street lines are layers above neighbourhood boundaries. Otherwise, you would not be able to see them.
The field and study of GIS extends much further than digital mapping and cartography. It consists of a variety of categories including spatial analysis, remote sensing, and geovisualization. In these GIS fields, the spatial data becomes much more complex and difficult to use.
In addition to raster and vector data, there is also LiDAR data (also known as point clouds) and 3D data. LiDAR data is data that is collected via satellites, drones, or other aerial devices. 3D data is data that extends the typical latitude and longitude 2-D coordinates and incorporates elevation and or depth into the data. While complex, this data is rich with information and can be used to solve a variety of problems pertaining to the Earth’s surface.
Using Spatial Data for Graphics
Maps are a common practice of presenting spatial data as they can easily communicate complex topics. They can help validate or provide evidence for decision making, teach others about historical events in an area, or help provide an understanding of natural and human-made phenomena.
When creating visuals, graphics, or maps with spatial data, there are a variety of geographic elements to consider. One of the most important and coincidentally most problematic elements is projection. The projection of a map describes the way that the Earth’s surface, a three-dimensional shape, is flattened and presented on a two-dimensional surface. No projection is perfect and depending on your projection you may be sacrificing accuracy in shape, area, distance, or direction.
Maps can also be used to present what are typically non-visual elements of society. For example, the occurrence of certain events, income level, any demographic descriptor, or relationships like the number of heat strokes in an area compared to temperature. A simple display method is a classification map, also known as a choropleth map.
Choropleth maps easily communicate differences, consistencies, or patterns across space. Classified areas in a choropleth map will have distinct boundaries whereas heat maps, which demonstrate the concentration or density of a phenomenon, have indistinct boundaries. Classification or heat maps can be used as the bottom layer for other variables like car accidents or crime to highlight certain trends and potential correlations.
Using Spatial Data for Statistics
As it is with any data, to truly make sense of spatial data and understand what it is saying you must perform some level of statistical analysis. These processes will help you uncover answers and lead you to make better decisions for your organization. The major difference between spatial data and all other types of data when it comes to statistical analysis is the need to account for factors like elevation, distance, and area in your analytical process.
While needing to account for additional variables about a location may be intimidating, many spatial statistic processes are quite similar to basic statistical methods. For example, interpolation can help you estimate or predict the value of a sample, and spatial interpolation can help you estimate or predict the value of a variable in a sample location. Similarly, spatial autocorrelation measures the degree of similarity between sample locations just like typical autocorrelation is done.
Additional Types of Spatial Data
While spatial data has long been used for analyzing and presenting the Earth’s surface, it is not limited to the outdoor environment. There are many architectural, engineering, and construction (AEC) companies that use CAD (computer-aided design) and BIM (building information model) data in their day-to-day activities. While CAD and BIM may not necessarily be thought of as traditional spatial data, they and other AEC formats also need to consider many spatial elements to understand their work.
Mapping is also no longer limited to the natural world. Indoor mapping and wayfinding are becoming much more popular especially in large buildings and institutions like malls, arenas, hospitals, and campuses. This field of study is new but shows no signs of stopping. Everyone has a smartphone these days and uses it to help them navigate the natural world, so why not help people navigate the indoors too?