CICEET Progress Report for the period 3/15/05 Through 9/15/05

Project Title: Integrating Technologies to Monitor and Predict Patterns of Urban Growth
Principal Investigator(s): Fay Rubin, William Salas
Project Start Date:1 September 2002

Figures

Figure 1

Figure 2

Figure 3

Figure 4

Tables

Table 1

Progress on Tasks
Task 1: Continue to refine and test neural network model.
We have finalized our neural network model and applied this model to our forecasts of population and employment in 2020 and 2035. Results from our model are provided in section E. We have also used our model with a separate set of REMI forecasts to illustrate how the model responds to changes in demographic and economic forecasts.

Task 2: Complete dissemination project CD.
The project CD set has been developed, comprising one CD for Strafford County and a 2-CD set for Rockingham County. The contents include:

• README File, with basic project overview
• Aerial Imagery
• Land Use Data Sets
• Metadata (for imagery and land use data sets)
• Maps (.pdf format)
• Acreage Summaries (.pdf format)
• Modeling Results
• Project Bulletin
• Miscellaneous files to assist in data usage

A CD (or CD set) was sent to each of the 50 communities in the 2-county study area.

Tasks 3: Continue project outreach.
There has been continued interest in our modeling results and methodologies during this past project period. We have had discussions and met with several groups, including the Society for the Protection of New Hampshire Forests, Metcalf & Eddy, and RKG Associates.

Tasks 4: Prepare final report.
The final report is in preparation and will be submitted within 90 days of the end of the project on August 31, 2005.

Difficulties Encountered
No major difficulties were encountered during the past 6 month project period.

Anticipated Success in Meeting Project Objectives in Scheduled Project Period
N/A

Preliminary Data
Table 1 contains the results from our final economic, demographic and new built area forecasts out to 2020 and 2035. Figure 1 presents similarity index (SI) output from our final neural network model at full resolution. Figure 2 presents the mean SI values at the TAZ level. Mean SI values were calculated based on removing areas that cannot, or should not, be developed (e.g. wetland, conservation lands). Figure 3 and Figure 4 present our preliminary TAZ level development forecasts for 2020 and 2035. Note that the total development by 2020 and 2035 is constrained by our forecasts, given in Table 1, and allocated to each TAZ based on SI values, the relationship between SI and development probability and the availability of developable lands. While TAZ mean SI values are very high in towns like Salem, Derry and Londonderry, expected total development is not high because most of the eligible land has already been developed.

Tasks and Activities for Next Reporting Period

Tasks for the Next Reporting Period
None

Work Plan for Next Reporting Period
N/A

Concerns or Difficulties
N/A

Expenditures
N/A due to no-cost extension