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Linda Waite

Evaluating the effects of land-use development policies on ex-urban forest cover: An integrated agent-based GIS approach

Publication Abstract

Robinson, D.T., and Daniel Brown. 2009. "Evaluating the effects of land-use development policies on ex-urban forest cover: An integrated agent-based GIS approach." International Journal of Geographical Information Science, 23(9): 1211-1232.

We use a GIS-based agent-based model (ABM), named dynamic ecological exurban development (DEED), with spatial data in hypothetical scenarios to evaluate the individual and interacting effects of lot-size zoning and municipal land-acquisition strategies on possible forest-cover outcomes in Scio Township, a municipality in Southeastern Michigan. Agent types, characteristics, behavioural methods, and landscape perceptions (i.e. landscape aesthetics) are empirically informed using survey data, spatial analyses, and a USDA methodology for mapping landscape aesthetic quality. Results from our scenario experiments computationally verified literature that show large lot-size zoning policies lead to greater sprawl, and large lot-size zoning policies can lead to increased forest cover, although we found this effect to be small relative to municipal land acquisition. The return on land acquisition for forest conservation was strongly affected by the location strategy used to select parcels for conservation. Furthermore, the location strategy for forest conservation land acquisition was more effective at increasing aggregate forest levels than the independent zoning policies, the quantity of area acquired for forest conservation, and any combination of the two. The results using an integrated GIS and ABM framework for evaluating land-use development policies on forest cover provide additional insight into how these types of policies may act out over time and what aspects of the policies were more influential towards the goal of maximising forest cover.

DOI:10.1080/13658810802344101 (Full Text)

Country of focus: United States of America.

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