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 equalNear 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 1 + 1 = 2 111 + 111 = 222

111 111 222

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

Legend

Raw Values of Local Accessibility

<VALUE>

0 - 8,073,901

8,073,902 - 16,147,803

16,147,804 - 24,221,704

24,221,705 - 32,295,605

32,295,606 - 40,369,507

40,369,508 - 48,443,408

48,443,409 - 56,517,309

56,517,310 - 64,591,211

64,591,212 - 72,665,112

Resorts

Legend

Resorts

Value

1 - Least Accessible

2

3

4

5

6

7

8

9 - Most Accessible

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

Legend

Resort Travel Time in Minutes

<VALUE>

0 - 1

1.1 - 5

5.1 - 10

10.1 - 15

15.1 - 20

20.1 - 25

25.1 - 30

30.1 - 45

45.1 - 1,838,485.3

Resorts

Legend

Resorts

restattract

Value

1

1.000000001 - 2

2.000000001 - 3

3.000000001 - 4

4.000000001 - 5

5.000000001 - 6

6.000000001 - 7

7.000000001 - 8

8.000000001 - 9

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