Identifying potential data
The critical datasets for land-use analysis and decision making can be grouped into the seven broad categories described by Ian McHarg in Design with Nature.
This categories are listed below.
- Geophysical
- datasets that describe abiotic (nonliving) native characteristics, including geology, soils, hydrology, hydrogeography, climate, and aspect.
- Biological/Ecological
- datasets that describe biotic (living) native characteristics, including vegetation, animal habitat, species distribution, and measures of biological diversity.
- Demographic
- datasets that describe human populations and their distribution, including census, population densities, ethnicity, income levels, age, and people per household.
- Economic
- datasets that describe landownership, costs and associated cost trends, property parcels, market value per acre, assessed value per parcel, and year built.
- Political
- datasets that represent politically derived constructs like zoning districts, comprehensive plan units, city limits, county limits, water management district boundaries, regional planning council boundaries, state boundaries, and publicly owned conservation lands.
- Cultural
- datasets that capture the distribution and character of cultural features, including land use and land cover, national register historic sites, state register historic sites, eligible historic sites, and cultural features sorted by period of historic significance.
- Infrastructure
- datasets that represent the spatial distribution and character of the physical infrastructure needed to support human settlement like roads, sanitary sewers, storm sewers, airports, and railroads.
A quick and easy way to organize your data and to thoroughly consider potentially useful data is to set up a matrix with goals and objectives on one axis and the seven data categories on the other axis.