Parcel Level Methodology

When land is bought, sold, and developed, it happens at the parcel level. Parcels are the policy relevant unit. Laws that control land use, such as zoning, control what can be done in each individual parcel.

A parcel-level methodology uses parcels as the unit of analysis. In the buildout analysis, the number of units that could be built were analyzed on a parcel-by-parcel basis. This is spelled out in detail in the methodology for buildout. A comparison of other methods of conducted buildout is also available in this section.

In the openspace analysis, scores were given to individual parcels. These scores help evaluate each individual parcel when it can potentially be purchased as openspace. This evaluation can be accomplished quickly and easily using GIS.

Critical Lands for Conservation is a similar study conducted to prioritize land for openspace acquisition. The point system on this web site was based on this study. Critical Lands was conducted statewide, therefore parcel level analysis was not possible. This is because there are not digital parcel coverages for the entire state.

Critical Lands uses a raster methodology instead of a parcel methodology. This methodology divides the land up into 100 by 100 foot cells. Each of these cells is given a score, similar to the scoring system used for parcels. The cells are broken up into groups depending on their score, and important areas are highlighted.

The disadvantage of this method is that it does not use the policy relevant units. The cells and the regions are useful for identifying areas, but there is still a lot information that is needed to purchase a parcel. Parcel level analysis are linked to the tax assessors database. Tax assessors data are useful because they provide information about lot ownership, assessed value, and current usage to name a few. All of this information is critical when protecting a parcel of land as openspace.

The one major disadvantage of working with parcel level data is the difficulty of working with vector data. Parcels are always represented as vector data sets. Vector data sets are discrete polygons with boundaries that end and begin at certain points.

The use of vector data makes analysis much more difficult and cumbersome. The methodology for both the buildout, and the openspace scoring system, show how vector data were used to accomplish the goals set out in this study. It was necessary to write Arc Macro Language programs to automate the process of coding parcels. This would not have been necessary if the analysis were conducted in raster format. There programs took a lot of computing power, and required the use of a UNIX server.