In public health, remote sensing helps evaluate areas of mosquito infestation. As these examples show, remote sensing data is used in myriad professional and technical fields.
GIS is a powerful tool for creating, storing, and analyzing geographic data. GIS combines spatial data (i.e., the location of things) with attribute data (i.e., characteristics of things), essentially bringing the power of maps and spreadsheets together. GIS data is stored and viewed as layers, where each layer is a specific theme (figure 1.7). For instance, a municipal GIS database can have a layer of city trees with their location as well as attribute information on tree species, health, and height. Another layer can have sewer systems with attribute information on diameter and age. Another layer can have parcels with attributes on ownership, land-use zoning, and type of structure.
Figure 1.6.Satellite remote sensing imagery. Log in to your ArcGIS Online account to explore these maps. High resolution imagery of mall: https://arcg.is/1L5PWX. False color infrared imagery: http://arcg.is/2m4ByRF. Data sources: World Imagery basemap, Esri, DigitalGlobe, GeoEye, Earthstar Geographics, CNES/Airbus DS, USDA, USGS, AEX, Getmapping, Aerogrid, IGN, IGP, swisstopo, and the GIS User Community. Infrared vegetation. USA NAIP Imagery: False Color. Esri; data sources: Esri, USDA Farm Service Agency.
Figure 1.7.A geographic information system consists of layers of data, which can include land use, roads, parcels, buildings, vegetation, topography, and much more. Image by Naschy. Stock vector ID: 526267657, Shutterstock.
GIS is a powerful tool for studying spatial distributions and spatial relationships. By looking at a layer of mosquito habitat and comparing it to a layer of recent urban growth, public health officials can analyze and predict how many malaria infections are likely to occur. With a layer of household income, a layer of ethnicity, and a layer of population density, a company can find the best location to sell a product targeting an ethnic niche. For environmental analysis, a layer of roads and a layer of tree species can be used to predict where logging is likely to occur.
Because of the myriad uses of geospatial technologies, there are many employment opportunities for people with these skills. Private companies, such as insurers, market researchers, and environmental consultants, need people who can collect data and map it with geospatial technologies. Government agencies, such as in urban and community development, environmental protection, public health, public works, and economic development, need people with these skills as well. Nonprofit organizations that provide social services, protect the environment, and improve health and economies locally and internationally also hire many people with backgrounds in geospatial technology.
Data sources
Geographic data can be produced in a wide variety of ways. Private companies produce much data, as do governments and researchers at universities and think tanks.
Private companies often collect data on customers, such as their home addresses and purchasing history. With this data, they can produce maps showing the types of products and services people buy in different parts of cities. A more detailed picture of population can be mapped by adding census data collected by governments, which is based on household surveys and can include the number of people, race and ethnicity, income, education, and many other variables. Phone interviews and mail surveys can also be used to collect data and map people’s attitudes and opinions on public issues.
Geospatial technologies, such as GPS and airborne remote sensing, are also important sources of data. As mentioned previously, GPS units are used in the field to collect data on any number of things, such as the location of potholes in streets, graffiti locations, buildings in rural villages, well sites, vegetation clusters, and bird nests. Remote sensing technology uses satellites and aircraft to collect data on larger areas. With this technology, data on crop types and health, urban growth, deforestation, illegal construction, and more can be collected.
Field analysis of the cultural landscape is also commonly used by geographers. By going into the field and making observations of the cultural landscape, from how people move and interact in particular parts of the city to types of buildings and land uses in different locations to peoples’ perceptions of neighborhoods, geographers collect and map a wide range of data.
Data quality and metadata
With myriad sources of geographic data, users must be very careful when evaluating data quality. Many times, a GIS user will find interesting data that appears useful for a work project or class paper. However, without investigating the quality and source of the data, the user may end up with inaccurate or misleading analysis results.
The most common types of data quality issues include spatial, temporal, and attribute accuracy; completeness; and data source reliability.
Spatial accuracy
Are features in the correct location, and with what degree of precision? For instance, is a hospital mapped at the correct street address, or did it get placed at a similar address in the wrong city? Is a property boundary mapped at a survey level of precision down to centimeters, or is it mapped at a coarser scale, such as meters? If you are building a perimeter wall around a property, a dataset mapped with an accuracy of meters will not suffice.
Temporal accuracy
When was the data created? A map showing voting patterns by county can be very helpful in understanding attitudes toward social issues. However, the map user needs to know if the data is current or if it was created too long ago to be of use.
Attribute accuracy
Are the values in attribute fields correct? For instance, does a map of average income by ZIP code have the correct values? Poorly built databases may have errors, or the numbers presented may have wide margins of error that must be accounted for when interpreting patterns.
Completeness
Are all features included, or are some missing? For example, when mapping home burglaries, is data available for all parts of the city? If not, there may be a false impression that no burglaries occur in one area, while in reality, the absence of burglaries may be due to missing data.
Data source
The origin of the data can indicate level of quality. For instance, a dataset made by the US Census Bureau should be based on high data quality standards. A dataset made by an unknown blogger or for a class project may not be as reliable.
Data quality and other important information is part of a spatial dataset’s metadata. Metadata is information about a dataset. It can include data quality, as discussed, as well as information on data collection methods, who produced the data, projection and coordinate systems, and more. When evaluating spatial data, it is important to review the metadata.
Go to ArcGIS Online to complete exercise 1.1, “Introduction to ArcGIS Online.”
Map basics
To work well with geospatial technologies, it is important to understand maps and the various ways in which data is presented with them. Different map types are available for conveying different varieties of data, while map scale can influence levels of detail and the types of spatial processes observed. Map projections can influence the user’s perceptions of size, shape, and direction when reading maps, and various coordinate systems are used to describe where features are located. Count and rate data are often misunderstood
