This post features IT technologies to work with geographic and geographic data.
This post is an introduction to GIS.
Geospatial Data Structures
Geographic data structures featured on this post:
- GeoJSON
GeoJSON
GIS Digital Images
Types of GIS digital images:
- Raster
- Vector
Raster
A raster in GIS structures space into a series of discrete elements by means of a regular grid, generally composed of square cells, also called pixels.
It bases its functionality on an implicit conception of the neighborhood relationships between geographic objects.
Vector
A vector in GIS consists of lines or arcs, defined by their start and end points, and points where several arcs intersect, the nodes.
Geospatial Databases
Geospatial or geographic databases featured on this post:
- PostGIS
- Mongoose
PostGIS
PostGIS adds support for geographical objects in PostgreSQL databases.
It is FOSS.
Mongoose
Mongoose is an ODM (object-document mapper) for MongoDB.
Mongoose can handle GeoJSON.
You can read more about how to work with geospatial data in Mongoose on this external link.
Geographic Libraries
Geographic libraries:
- Python
- GeoPandas
- Shapely
- PyProj
- GDAL
Geographic Web Frameworks
Geographic web frameworks featured on this post:
- Django
- GeoDjango
GeoDjango
GeoDjango is a Django module. It is FOSS.
Geographic Information Regulation
Directive 2007/2/EC, commonly known as INSPIRE (Infrastructure for Spatial Information in Europe) is incorporated into Spanish law through Law 14/2010 on infrastructure and geographic information services in Spain (LISIGE).
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External References
- Paolo Melchiorre; “Database generated columns⁽³⁾: GeoDjango & PostGIS“; paulox.net