1. Attractiveness Mapping Modeling Land Use Preference

2. Outline General Concepts in Attractiveness Modeling Technical Implementation Issues

3. General Concepts & Methods in Attractiveness Modeling Identify abstract best/worst conditions Find geographical correlates for key factors Develop Factor Maps “Weight and rate” to Generate Single Output

4. Best Case / Worst Case Identify abstract best/worst conditions Important perspectives  Legal  Physical (natural amenities or disamenities)  Fiscal  Social services Roleplay  developers  potential customers/citizens  Note: we are *not* addressing environmental or other impacts *yet* (keep out of flood plain for self-interest, but not otherwise unless dictated by market or law)

5. Finding Geographic Correlates Often, data you might most want are not available Example: we have no land cost data layer Two options A) Ignore the factor entirely B) Generate a reasonable spatial approximation If Option B, how? Generally, use qualitative and relative versus quantitative or absolute factors  E.g. likely land cost = low, medium or high vs. land cost <= $133,456.34/ha Use proximity when appropriate  All other things being equal Near existing expensive might likely be expensive

6. Developing Factor Maps Factor Maps Express the main decision criteria spatially  Example: distance to nearest school, land price, travel time to employment Should be in common vocabulary/units/scale  Here, since output is given 1-9 scale, use that Depth versus Breadth and Spatial Autocorrelation  Better 3-5 spatially un-correlated factors than more  As in statistical regression, better to have few but solid explanatory variables  If sub-factors are needed, organize hierarchically  Example: Good Views = Ocean Views or Mountain Views, Ocean View = …

7. Technical Implementation Issues Summarizing existing conditions Categorical Variables Continuous Variables Expressing factors along equal scales Using reclass or slice Weighted overlays in ModelBuilder General operation Special cases

8. Summarizing existing conditions Categorical Variables  Usually can use zonal statistics run on land use mask  Code land use as “1”  Run zonal stats against land use  Careful with “area” column sums – often wrong Continuous Variables  Table summary stats ok  Can do in interface and record manually  Or can run with output to tables

9. Expressing factors along equal scales Generally need to convert arbitrary and mixed units into evaluation units Dealing with ranges  First, exclude unreasonable values  Then scale range of reasonable values  Flip if necessary (distance to water = good or bad?) Dealing with absolutes  Usually can use reclassify operation  Example: if being adjacent to airport is a dealbreaker then recode distances to airport within “tooclose” range to “1”

10. Weighted overlays in ModelBuilder In raster, could simply add factor maps Example:  “closeness to school” rated 1..9  “closeness to work” rated 1..9  Map Calculator sum  Value 2 = furthest from school and work  Value 18 = closest to both school and work  In between = equally weighted index Weighted overlay expresses two additional concepts Some factors are more important than others Some factors are “dealbreakers”

11. Weighted Overlay Demo Imagine a “tourist restaurant” land use Want to be visible to tourists Don’t want to pay more than necessary for land Best / Worst Relatively low cost but highly visible location Factor Maps Factor 1 = Resort & port accessibility Factor 2 = Land Cost

12. Tourist Restaurant Travel time versus Traffic Travel time Can be across existing roads network But since attractiveness models have roads as input, can also accommodate future road changes Local & global accessibility measures in “road accessibility lines polyline” shape file Accessibl = local (c. walking distance of 1.6m) Accessib0 = regional Values can be treated as an approximation of trips

13. Tourist Restaurant Accessibility Subfactor 1: Concept: Busy street Metric: Scaled Global accessibility Implementation: Use the natural log (ln) to massage highly skewed data distribution of global accessibility Take 9 equal interval slices Higher values = busier = more attractive Busiest sites at intersections, so use focal mean to summarize busyness in 3x3 cell area

17. Tourist Restaurant Accessibility Subfactor 2: Travel time to nearest resort (Not ideal because better might be average distance to all resorts within a theshold) Implementation Cost distance  From resorts  Over transit time surface  Base walking time = 4 miles/hour  Walking slope penalty = pcnt_slope^2 Results ‘manually’ reclassified within MB

22. General Concepts in Urban Simulation Basic Modeling Options Endogenous Attempt to simulate & predict market functions Based on “bid-rent” theory and transportation cost Exogenous Attempt to predict distribution (but not amount) of given types of development Form-based Models Gravity Models Diffusion-limited Aggregation