Description: SANDAG’s Land Layers are created for use in the Regional Growth Forecast to distribute projected growth for the San Diego region to suitable subareas. These land layers include existing land use, planned land use, land ownership, land available for development, and lands available for redevelopment and infill. The land layers inventory is updated when new information is available. Many of these data sets are built from the San Diego Geographic Information Source (SanGIS) landbase. The land use information has been updated continuously since 2000 using aerial photography, the County Assessor Master Property Records file, and other ancillary information. The land use information was reviewed by each of the local jurisdictions and the County of San Diego to ensure its accuracy. Although this inventory contains more categorical detail and has better positional accuracy than previous land use inventories, users should be aware that this data may be too generalized for some local planning projects. Since each General Plan/Community Plan Land Use Elements have their own individualized way of categorizing their future land use designations, an aggregate planned land use code was devised (PLU). Each General Plan/Community Plan land use designation was cross-walked to a SANDAG PLU code. Adjacent parcel polygons with the same land use have been aggregated (dissolved) into a single feature.
Copyright Text: SANDAG Land Layers Inventory.
Mapping Source: SanGIS landbase (i.e. parcels), SANDAG, County Assessor's Master Property Records file, Cleveland National Forest, Bureau of Land Management (BLM), State Parks, other public agency contacts, and local agency review.
Description: This dataset is a collection of the current base zone designations applied to property in the City of San Diego, as per the Official Zoning Map adopted by the City Council on February 28, 2006, and all subsequent updates.
Copyright Text: City of San Diego, using rezone ordinances adopted by City Council, and map data provided by the Development Services Department (DSD)
Description: Tree canopy was derived from 2014 high-resolution remotely sensed data. Object-based image analysis techniques (OBIA) were employed to extract potential tree canopy and trees using the best available remotely sensed and vector GIS datasets. OBIA systems work by grouping pixels into meaningful objects based on their spectral and spatial properties, while taking into account boundaries imposed by existing vector datasets. Within the OBIA environment a rule-based expert system was designed to effectively mimic the process of manual image analysis by incorporating the elements of image interpretation (color/tone, texture, pattern, location, size, and shape) into the classification process. A series of morphological procedures were employed to insure that the end product is both accurate and cartographically pleasing. Following the automated OBIA mapping a detailed manual review of the dataset was carried out at a scale of 1:3000 and all observable errors were corrected.
Copyright Text: University of Vermont Spatial Analysis Laboratory in colaboration with City of San Diego.