Attractiveness Mapping Modeling Land Use Preference

Outline General Concepts in Attractiveness Modeling Refresher on Basic Raster Analysis Technical Implementation Issues

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

Best Case / Worst Case Identify abstract best/worst conditions Important perspectives  Legal  Physical (natural amenities or dis-amenities)  Fiscal  Social services Roleplay  developers  potential customers  citizens

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

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 = …

Raster GIS in ArcGIS A Spatial Analyst refresher

Raster Data Model Rasters are conceptually similar to pixels Instead of coding visual appearance as red/green/blue, encode spatial data Common Types of Rasters Categorical  e.g. land use code where 1 = urban, 2 = suburban Continuous and representing measured data  e.g. elevation, where 1 = 1 meter above sea level, 2 = 2 meters, etc. Continuous and representing preference  e.g “attractiveness to urban development” where 1 = least and 9 = most attractive

Operating on Rasters Quick and easy for the computer Generally, a set of raster GIS layers are designed to “line up” Same overall spatial extent Same raster grid cell size Most operations involve simple algebra Known as “map algebra” (Tomlin) Just as = =

Operating on Rasters 2 “NoData” critically important concept to understand in raster analysis *despite* the name, “NoData” in many cases represents areas which the user wishes to treat as transparent, empty or background Example In creating raster layers from vector features, the areas on the map between features are coded as “NoData” In this case, “absent”, “empty” or “background” are more appropriate conceptual meanings A systematic measurement *was* made and the mapped feature was *not* found

Why Worry About “NoData”? In ArcGIS map algebra, NoData is a special value NoData + anything = NoData Adding two maps in Spatial Analyst Get only areas which didn’t include NoData in either map For order-dependent overlay, need “Merge” command In this case, “NoData” in top layer = transparent Spread command (for distance calculations) only expands into NoData areas In this instance, treated as “background”

Spatial Analyst Review Enable the Extension Show Toolbar Adjust Options Working directory – local writeable Set Common Extent (bases civitas nova/study area) Cell Size (25m to start) Optionally, set mask to study_area_25m as well

Spatial Analyst Basics Conversions Reclassification “Selection” in raster Aggregation

Vector to Raster Vector files, including CAD files, can be directly converted into raster Only selected features are converted Remember to clear selection first Useful for converting only features meeting particular criteria (select first, then convert) A Raster Value Column must be specified These can be numeric (leading to predictable result) Can also be text (leading to automatic generation of raster code values based on sequential position) If you don’t have an appropriate pre-existing column, can simply create one Example: create integer column named “one” with value calculated to equal “1” for all features

Conversion Already have spatial geography you want, just need to extract & reformat Example: Have existing urban areas mapped from CAD, want all of these as a grid of “most attractive” with value of 1 Create a new column and calculate an appropriate code value for it e.g. create new integer column named “attractiveness” Calculate value of “attractiveness” = 1 Convert vector to raster using new column

Basic Spatial Relationships Overlay Sites inside “rural” zoning Sites outside of city limits Proximity Adjacency Near Far

Arithmetic Overlays Can Use “Raster Calculator” NoData +-/* Anything = NoData If NoData causing “dropouts” Reclass NoData to “0” Alternatively, can use “isnull()”

Example Map Algebra Overlays Which urban areas are over 5% slope? Have urban areas vectors, percent slope from base GIS Strategy: isolate desired components into “mask” maps (desired = 1, background = NoData) Add maps in Map Calculator

Proximity Relationships in Raster GIS Proximity Adjacency hard in raster – usually better to develop appropriate “near” criteria Near/Far Can be absolute or relative  Within 100m = near?  For relative, prior analysis can calculate distribution  (Relatively) near primary schools could be based on standard deviations of existing distances to primary schools

Simple Proximity To start, we’ll use Euclidean Distance Aka “as the crow flies” Later, Cost Distance Requires transit data grooming Much more time consuming Points to remember Euc Distance spreads only into “NoData” cells Objects are “0” distance from themselves Buffers in raster usually a 2-step Distance / Reclass

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

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

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”

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”

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

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

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

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

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