Spatial interpolation, data aggregation, data normalization, and data transformation are all commonly used data processing techniques in geographical databases.

Spatial interpolation is used to estimate values at unsampled locations based on the values at known locations.

Zonal statistics is a common data aggregation technique used to summarize the values of a raster dataset within the zones of a polygon feature class.

Data normalization is used to make data more consistent by converting it to a common format or scale.

Reprojection, resampling, generalization, and symbolization are all common data transformation techniques used in geographical databases.

Reprojection is used to change the coordinate system of a dataset so that it can be used with other datasets or displayed on a map.

Buffer analysis, network analysis, overlay analysis, and geostatistical analysis are all common spatial analysis techniques used in geographical databases.

Buffer analysis is used to create a zone around a feature, which can be used for a variety of purposes, such as finding nearby features or calculating the area of a zone.

Finding the shortest path between two points, finding the closest facility to a point, allocating resources to facilities, and optimizing the location of facilities are all common network analysis techniques used in geographical databases.

Overlay analysis is used to combine two or more datasets, find the intersection of two or more datasets, find the union of two or more datasets, and find the difference between two or more datasets.

Kriging, inverse distance weighting, trend surface analysis, and geostatistical simulation are all common geostatistical analysis techniques used in geographical databases.

Kriging is used to estimate values at unsampled locations based on the values at known locations, taking into account the spatial autocorrelation of the data.

Choropleth maps, dot maps, line maps, and pie charts are all common data visualization techniques used in geographical databases.

Choropleth maps are used to show the distribution of a quantitative variable across a geographic area, by coloring each area based on the value of the variable in that area.

Data warehousing, data mining, data quality control, and data security are all common data management techniques used in geographical databases.