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Simulating Future Suburban Development in Connecticut

Simulating Future Suburban Development in Connecticut. Jason Parent, Daniel Civco, and James Hurd jason.parent@uconn.edu Center for Land Use Education and Research Dept. of Natural Resources Management and Engineering University of Connecticut. Study Objectives.

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Simulating Future Suburban Development in Connecticut

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  1. Simulating Future Suburban Development in Connecticut Jason Parent, Daniel Civco, and James Hurd jason.parent@uconn.edu Center for Land Use Education and Research Dept. of Natural Resources Management and Engineering University of Connecticut

  2. Study Objectives • Simulate suburban development over the next 30 years • Analyze impact of simulated development on forest fragmentation

  3. Salmon River Watershed The Study Area

  4. Study Area Properties • 150 sq. miles in area • Heavily forested (approx. 72% in 2002) • Population is growing • 18.6% increase between 1990 and 2000 • Urban and associated land cover is increasing • Urban land increase of 16.6% between 1985 and 2002 • Turf / grass increase of 18.6% between 1985 and 2002

  5. General Approach • Multi-Criteria Evaluation • create suitability maps • Buildout Analysis • identify potential building sites • TimeScope Analysis • assign build dates to potential building sites

  6. General Approach • Buffer potential buildings • account for land cover change associated with building • Update land cover map • Forest Fragmentation Model(Riitters et al. 2000) • identify types of forest present with respect to fragmentation

  7. Two categories (suitable, unsuitable) Grid cells have values of 0 or 1 0 = feature present (unsuitable) 1 = feature absent (suitable) i.e. water More than two categories Grid cells have a range of values (criterion scores): 0 to 100 100 = most suitable categories 0 = least suitable categories i.e. soil types Multi-Criteria Evaluation • Based on grids depicting relevant criteria • Two kinds of criteria: constraints and factors Factors Constraints

  8. Floodzones Hydrography Wetlands Wetlands (50’ buffer) Hydrography (50’ buffer) Floodzones (50’ buffer) Multi-Criteria EvaluationConstraint Grids

  9. Municipal Open space Slope > 20% DEP land Multi-Criteria EvaluationConstraint Grids

  10. septic potential min. slope rockiness score reduction hydric no high < 8 not rocky 0 stony or rocky no medium 8 to 15 10 very stony or very rocky no low or very low NA 20 no extremely low extremely stony 30 > 15 100 yes NA NA rock outcrop Multi-Criteria Evaluation:Factors: Soil Types Criterion scores systematically assigned to each soil type • Initially assigned maximum scores (100) to each soil type • Reduced score for negative properties of soil types

  11. Multi-Criteria Evaluation:Soil Factor Shapefile depicting criterion scores for soil types

  12. Multi-Criteria Evaluation:Roads Factor Structures tend to be located within a certain range of distances from a road • Satisfy home owner preference • Reduce development costs • Mandatory setback Maximum development approximately 100 feet from the nearest road

  13. low high calculate % land developed in each class Map distances from roads scale from 0 to 100 Group into classes with 20’ intervals Grid depicting criterion scores Multi-Criteria Evaluation:Roads Factor major roads

  14. Multi-Criteria Evaluation:Calculation of Suitability Values binary raster format (unsuitable = 0) Constraint 1 multiply all constraints (unsuitable = 0) Constraint 2 Constraint 3 multiply suitability raster x weight1 Factor 1 add weighted factor combination x weight2 Factor 2 raster format; values range from 0 to 100 Sum of weights = 1

  15. Roads factor Soils factor Suitability Map Constraints (water, wetland)

  16. unsuitable highly suitable Multi-Criteria Evaluation:Suitability Map (Bolton)

  17. BuildoutAnalysis • Community Viz’s Scenario 360 Buildout tool • Places points at all potential future building locations • uses zoning information to determine lot size, building separation distance, etc. • excludes constraint areas from analysis • works with parcel data

  18. Buildout AnalysisConstraints • Unsuitable land • suitability value = 0 • Fully developed parcels • Parcels containing a structure and less than 240,000 sq ft (6 builders’ acres) • 50 ft buffer around existing structures

  19. Zoning regulations for East Hampton Buildout AnalysisZoning Regulations Required zoning information: • Zone name • Minimum lot size (acres) • Building efficiency • The percentage of available land that can be built upon • Efficiency less than 100% because of roads, open space requirements, etc. • Building separation distance • Minimum distance between the center points of two buildings

  20. Buildout AnalysisResults

  21. TimeScope Analysis • Community Viz’s Scenario 360 TimeScope tool • Assigns a build date to each buildout “building” • Simulates the order in which building construction will occur over a specified period of time • Based on parcel suitability • Building growth rate is specified by user • For a given time step (i.e. year), the value of the current time step is assigned to: • A number of building locations, equal to the annual building growth rate • Building locations for which a build date has not already been assigned • Locations with the highest remaining suitability value

  22. TimeScope AnalysisBuilding Growth Rates • Building growth was assumed to parallel population growth • Census data indicate that population growth has been linear over the past 40 years • Population extrapolated out to 2036 by linear regression of past census data

  23. Pt2036 - Ht2004 At Gt = 2036 - 2004 Estimated Population and Building Growth TimeScope AnalysisBuilding Growth Rates Gt = annual number of houses constructed Pt2036 = predicted population in 2036 At = average number of people per house in 2000 Ht2004 = number of houses in 2004

  24. TimeScope AnalysisResults: Colchester

  25. TimeScope AnalysisResults: Colchester

  26. Buildout Building Buffers

  27. Future Land Cover Maps • Forecasted land cover for 2010, 2015, 2020, 2025, 2030, and 2036 • 2002 land cover base map • Derived from Landsat satellite imagery • Selected buffers, with the appropriate build dates, for each forecast year • Land cover grid cells, within the selected buffers, were converted to urban land cover

  28. Future Land Cover Maps: Marlborough

  29. Forest Fragmentation Analysis • Model developed by Riitters et al. (2000) • Identifies forest grid cells as one of 5 types based on the percentage of forest grid cells and connectivity of forest grid cells in the surrounding area: • Interior: all surrounding grid cells are forest • Edge: grid cell is on the exterior edge of a forest tract • Perforated: the interior edge of forest tract • Transitional: about half of the surrounding grid cells are forest • Patch: less than 40% of surrounding grid cells are forest • In this study, edge and perforated forest types were within 60 meters of the forest perimeter

  30. Forest Fragmentation Maps: East Haddam

  31. Changes Predicted to Occur, between 2002 and 2036, inLand Cover • 3% of forest cover will be converted to non-forested land cover • Urban land and associated turf will increase by approximately 18% • Agricultural land will decline by approximately 5.6%

  32. Changes Predicted to Occur between 2002 and 2036 inForest Fragmentation • Interior forest will decline by 28% • Perforated, transitional, and patch forest will increase by 67%, 10%, and 8% respectively • Edge forest will decline by 15.5%

  33. Discussion • Analysis does not account for road construction • Estimates of forest cover change are conservative • Perforated forest area is over-estimated while edge forest area is underestimated • Building growth rates used in the TimeScope analysis are probably not applicable to non-residential zones • The effect in this study should be minimal since the towns had little non-residential area • The analysis is applicable to regions in which the major forest fragmenting process is suburban development.

  34. Future Work • Incorporate a model to predict road development • Include socioeconomic data into the derivation of the suitability maps • Identify land availability • Derive building growth rate estimates applicable to non-residential zones • Compare of the effects of current zoning scenarios with low impact zoning scenarios

  35. Questions? Simulating Future Suburban Development in Connecticut Jason Parent, Daniel Civco, and James Hurd jason.parent@uconn.edu Center for Land Use Education and Research Dept. of Natural Resources Management and Engineering University of Connecticut

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