Python for Geospatial Data Analysis by Bonny P. McClain

Author:Bonny P. McClain [Bonny P. McClain]
Language: eng
Format: epub, pdf, mobi
Publisher: O'Reilly Media, Inc.
Published: 2022-05-24T16:00:00+00:00

Figure 1-8. Building types in NYC

Geographical systems can work with many types of data. Vector data is what we are referring to when we think of points, lines, or polygons (like a shapefile, for example) and LIDAR (light detection and ranging) surveys. When examining a GIS environment like a landscape, for example, everything within the range of your vision is a feature when rendered in an application like QGIS. Details about the features are captured as attributes.

When location data appears in a spreadsheet, the columns for latitude and longitude create a point. The air quality spreadsheet shown in Figure 1-9 has geographic (latitude and longitude) and non-geographic data (air quality measurement in the value column), allowing a GIS application to add information associated with a particular geographic location. A point feature has an X, Y, and (optionally) Z value. The most familiar coordinate projection system is longitude and latitude. You can be dropped anywhere in the world, and if you provide your longitude (X) and latitude (Y), your location is known -- that’s precisely what we mean by a point attribute. This coordinate accurately describes where a particular place is on the earth’s surface. Point attributes can be quantitative or qualitative descriptions.

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