Download presentation

Presentation is loading. Please wait.

Published byConrad Axe Modified over 2 years ago

1
Jeff Onsted AAG Annual Meeting New York, NY February 28, 2012

2
Building a better Excluded Layer Paper is -Onsted, J., K. Clarke. 2011. "Using Cellular automata to forecast enrollment in differential assessment programs" Environment and Planning B 38(5): 829-849. Originally SLEUTH only allowed a binary classification of excluded areas: totally excluded from development or totally open for development. More recent versions, however, permit resistance probabilities ranging from 0 to 100%. But what resistance should we give them? - Natural examples include wetlands or flood-prone areas, fire hazard areas, landslide prone areas, areas near eroding sea cliffs, or sites in other harsh or environmentally unfriendly conditions. -Regulatory examples include areas not currently zoned for development or other parcels under non-permanent protection or temporary exclusion from development.

3
Differential Assessment When a parcel of land is taxed at a lower than assessed value In many cases this is used to protect certain lands from development, particularly farmland Every state in the nation (except Michigan) practices some form of DA in order to protect farmland Each state’s program has its own particular rules and regulations My example comes from CA’s Williamson Act

4
WA Background In 2002, 59% of California’s 11.3 million hectares of total farmland were enrolled in the program (California (CA) Department of Conservation, 2003). Farmers agree not to develop their land and to keep it productive in return for lower property taxes Open Space Subvention Act (CA Government Code Section 16140-16154), partially compensates local governments for their lost tax revenue. (or it used to) To enroll, farmers in participating counties must join for a rolling 10-year period.

5
How should WA be treated in SLEUTH? These lands, which in some states comprise a very high percentage of agricultural land, are at present, off-limits to development. Since these programs are often voluntary, the landowner can choose to opt out of them, opening up their lands for urbanization. The two most obvious approaches: (a) ignore these lands or (b) assign them full exclusion. OR assign them a user-defined resistance to development (Teitz et. al. 2005), which, though often subjective, comes closer to expressing the reality of their tenuously protected nature.

7
2003

8
1986

9
1988

10
1990

11
1992

12
1994

13
1996

14
1998

15
2000

16
2002

18
Williamson Act Termination Simulation By simultaneously examining the growth of development and the spread of FWA parcels on the assembled GIS maps, the spread of FWA parcels appeared to respond to the same stimuli that would encourage urban growth. In particular, proximity to urban areas and transportation corridors appeared to have strong correspondence with the decision to leave the Act. Therefore, it was decided to use SLEUTH’s built-in calibration to quantify what was being observed.

19
WA Gov-Scape (6 classes) SLEUTH input layer substitutions for Simulating Williamson Act termination and enrollment Lands in the WA / not in the WA Former WA and Urban Are Replaced With: No Substitution Excluded layer used in SAWA scenario Typical

20
Urban/Former WA layer for Former Williamson Act (FWA) growth.

21
Excluded Layer used for Former Williamson Act growth.

22
Metric NameDescription ProductAll other scores multiplied together Compare Modeled population for final year/actual population for final year, or IF Pmodeled > Pactual { 1 – (modeled population for final year/actual population for final year)} PopLeast squares regression score for modeled urbanization compared to actual urbanization for the control years EdgesLeast squares regression score for modeled urban edge count compared to actual urban edge count for the control years Clusters Least squares regression score for modeled urban clustering compared to known urban clustering for the control years Cluster size Least squares regression score for modeled average urban cluster size compared to known average urban cluster size for the control years Lee-SalleeA shape index, a measurement of spatial fit between the model’s growth and the known urban extent for the control years Slope Least squares regression of average slope for modeled urbanized cells compared to average slope of known urban cells for the control years % urbanLeast squares regression of percentage of available pixels urbanized compared to the urbanized pixels for the control years X-mean Least squares regression of average x_values for modeled urbanized cells compared to average x_values of known urban cells for the control years Y-mean Least squares regression of average y_values for modeled urbanized cells compared to average y_values of known urban cells for the control years RadLeast squares regression of average radius of the circle which encloses the urban pixels OSMProduct of Compare, Pop, Edges, Clusters, Slope, X-Mean, Y-Mean (Dietzel, 2004) Table 3 SLEUTH ’ s built-in Metrics. These Can Be Used to Evaluate the Goodness of Fit between simulated urban growth and actual urban growth for each round of calibration (as excerpted from Teitz et. al, 2005) Note: Italicized text indicates additional metric added by author.

23
WA RunsUrban Runs integrating 2002 WA into Excluded Layer Coarse: a= 4 b= 3125 Growth Parameters Range Step c 1–100 25 d 1–100 25 e 1–100 25 f 1–100 25 g 1–100 25Resulting Metrics OSM = 0.616536 Coarse: a = 3 b = 3,125 Growth Parameters Range Step c 1–100 25 d 1–100 25 e 1–100 25 f 1–100 25 g 1–100 25Resulting Metrics OSM = 0.86165 Fine: a = 7 b = 7776 Growth Parameters Range Step c 1–50 10 d 1–50 10 e 75-100 5 f 25-75 10 g 1-50 10 Resulting Metrics OSM = 0.619798 Fine: a =5 b = 7776 Growth Parameters Range Step c 75–100 5 d 75-100 5 e 0-25 5 f 0-50 10 g 50-75 5 Resulting Metrics OSM = 0.905062

24
Final: a = 8 b = 7776 Growth Parameters Range Step c 1–25 5 d 1–25 5 e 85-100 3 f 50-75 5 g 1-25 5 Resulting Metrics OSM = 0.637348 Final: a=8 b = 7776 Growth Parameters Range Step c 85-100 3 d 80-90 2 e 0-10 2 f 30-40 4 g 50-60 2 Resulting Metrics OSM = 0.895966

26
2030 WA Gov-Scape

27
Urban Growth Simulation using FWA results The use of a probabilistic excluded layer based on a complementary run to forecast WA behavior, to our knowledge, is. Three different Williamson Act-based scenarios were created using three different excluded layers for Tulare County’s projected urban growth and land use change. These three scenarios were named: (a) Strict Adherence to the WA (SAWA), which freezes 2002 enrollment and allows no additional enrollment or termination; (b) Abolition of the WA (AWA), which removes all WA protections, leaving all agricultural land available for development; and (c) Business As Usual (BAU), which relies on the use of the FWA growth modeling. For urban growth calibration purposes, the excluded layer that included the 2002 WA lands was used, along with other appropriately disqualified lands, such as parks and National Forests, etc. Just as in the FWA growth modeling, the OSM was chosen for calibration evaluation between rounds (Tables 2 and 3).

28
Excluded Layer used for Strict Adherence to the WA (SAWA) scenario.

29
Excluded Layer used for Abolition of the WA (AWA) scenario.

30
A new use for the Hillshade Layer Hillshade is essentially an inert layer. The Hillshade layer is included to form a user-controlled background for the urban growth forecasts. Since SLEUTH uses Monte Carlo (MC) simulation, any number of iterations can take place, with the number of successful urbanizations normally overlaying the pixels in the Hillshade layer. Usually, the output of this simulation would be different colored cells reflecting the number of times they were selected for development. By removing the Hillshade layer and replacing it with the Excluded layer used in the SAWA scenario from the urban growth modeling, the stage is set for the creation of the BAU urban growth Excluded layer. Note that SLEUTH’s naming conventions demand this layer still be called Hillshade, even though it is representing something else in this case. The resulting overwritten image is then the new Excluded layer to be used for the BAU scenario in urban growth modeling.

31
Excluded Layer used for Strict Adherence to the WA (SAWA) scenario.

32
Section XII. Part 3. PROBABILITY COLORTABLE FOR URBAN GROWTH # low, upper, hex, (Optional Name) PROBABILITY_COLOR= 0, 1,, #transparent PROBABILITY_COLOR= 1, 10, 0X00ff33, #green PROBABILITY_COLOR= 10, 20, 0X00cc33, # PROBABILITY_COLOR= 20, 30, 0X009933, # PROBABILITY_COLOR= 30, 40, 0X006666, #blue PROBABILITY_COLOR= 40, 50, 0X003366, # PROBABILITY_COLOR= 50, 60, 0X000066, # PROBABILITY_COLOR= 60, 70, 0XFF6A6A, #lt orange PROBABILITY_COLOR= 70, 80, 0Xff7F00, #dark range PROBABILITY_COLOR= 80, 90, 0Xff3E96, #violetred PROBABILITY_COLOR= 90, 100, 0Xff0033, #dark red Section XII. Part 3. PROBABILITY COLORTABLE FOR FWA growth. Grayscale output is absolutely essential. Though only 11 classes were used, 101 are possible. # low, upper, hex, (Optional Name) PROBABILITY_COLOR= 0, 10, 0X5F5F5F,#95 grayscale PROBABILITY_COLOR= 10, 20, 0X555555, #85 grayscale PROBABILITY_COLOR= 20, 30, 0X4B4B4B,#75 grayscale PROBABILITY_COLOR= 30, 40, 0X414141, #65 grayscale PROBABILITY_COLOR= 40, 50, 0X373737, #55 grayscale PROBABILITY_COLOR= 50, 60, 0X2D2D2D, #45grayscale PROBABILITY_COLOR= 60, 70, 0X232323, #35 grayscale PROBABILITY_COLOR= 70, 80, 0X191919, #25 grayscale PROBABILITY_COLOR= 80, 90, 0X0F0F0F, #15 grayscale PROBABILITY_COLOR= 90, 99, 0X050505, #5 grayscale PROBABILITY_COLOR= 100, 100, 0X000000, #0 grayscale You can actually have 101 different probability grayscale values.

33
SLEUTH Creates your excluded layer for you FWA 100 MC iterations is the output And becomes the input for Excluded layer used in Urban Growth Simulation

34
Excluded layer for Business As Usual (BAU) scenario. This was created from the 100 MC iterations of the Former WA growth simulation that overwrote the Strict Adherence to the WA excluded layer

35
2003

36
Strict Adherence to WA Excluded Layer

37
Strict Adherence WA 2030

38
Abolition of WA Excluded Layer

39
Abolition of WA 2030

40
Excluded layer for Business As Usual (BAU) scenario. This was created from the 100 MC iterations of the Former WA growth simulation that overwrote the Strict Adherence to the WA excluded layer

41
Business As Usual WA 2030

42
Contribution of Novel Method First, SLEUTH is endowed with greater spatial complexity. Although SLEUTH currently has the capacity to have “weighted resistance” to development this still must be programmed in by the user and is difficult to arbitrarily quantify from general policy knowledge (Dietzel and Clarke, 2004). Second, the addition of human decision making to SLEUTH is the greatest improvement offered in this research. During the FWA growth modeling, cells reflect the individual decisions of landowners to leave the Williamson Act The approach offered in this research has relevance for not only any county in California that is party to the WA, but any area in the nation employing differential

43
Thanks so Much… Keith, of course Claire and Gargi for putting this together and for great ideas for advancing SLEUTH All of you This research includes collaboration with the Florida Coastal Everglades Long-Term Ecological Research program under National Science Foundation Grant No. DEB-9910514. Colleagues at FIU

Similar presentations

OK

PCB 3043L - General Ecology Data Analysis. OUTLINE Organizing an ecological study Basic sampling terminology Statistical analysis of data –Why use statistics?

PCB 3043L - General Ecology Data Analysis. OUTLINE Organizing an ecological study Basic sampling terminology Statistical analysis of data –Why use statistics?

© 2018 SlidePlayer.com Inc.

All rights reserved.

Ads by Google

Ppt on power system stability Ppt on line drawing algorithm Ppt on central administrative tribunal new delhi Ppt on solids in maths Ppt on you can win Ppt on challenges of democracy in india Ppt on water pollution download Ppt on of studies by francis bacon Ppt on solar energy conservation Ppt on operation research in linear programming