Development of a Model to Quantify and Map Urban Growth Emily H. Wilson, James D. Hurd, Daniel L. Civco Center for Land Use Education and Research (CLEAR) Department of Natural Resources Management & Engineering The University of Connecticut U-4087, Room 308, 1376 Storrs Road Storrs, CT

Outline IntroductionIntroduction Justification and BackgroundJustification and Background Urban Growth ClassesUrban Growth Classes MethodsMethods Examples of Growth TypesExamples of Growth Types ApplicationsApplications Considerations and ConclusionConsiderations and Conclusion

Northeast Applications of Useable Technology In Land planning for Urban Sprawl A NASA Regional Earth Science Applications Center (RESAC) A NASA Regional Earth Science Applications Center (RESAC) Introduction

To educate the general public on the value and utility of geospatial technologies, particularly RS information. Our RESAC Mission To make the power of remote sensing technology available, accessible and useable to local land use decision makers as they plan their communities. Introduction

NAUTILUS Research Better land cover mapping and change detection Urban growth models and metrics Forest fragmentation models and metrics Improved impervious cover estimates Introduction

NAUTILUS Research Better land cover mapping and change detection Urban growth models and metrics Forest fragmentation models and metrics Improved impervious cover estimates “A Forest Fragmentation Index to Quantify the Rate of Forest Change “A Forest Fragmentation Index to Quantify the Rate of Forest Change”, James D. Hurd Natural Resources: Ecological Modeling Thursday, April 25 at 10:30 AM Natural Resources: Ecological Modeling Thursday, April 25 at 10:30 AM Introduction

Outline IntroductionIntroduction Justification and BackgroundJustification and Background –Justification: Negative Impacts of Urban Sprawl –The “Urban Sprawl” Term –Characteristics of a Good Model Urban Growth ClassesUrban Growth Classes MethodsMethods Examples of Growth TypesExamples of Growth Types ApplicationsApplications Considerations and ConclusionConsiderations and Conclusion

Negative Impacts of Sprawl Environmental DegradationEnvironmental Degradation –Increased impervious surfaces and degraded water quality –Loss of wildlife habitat and wildlife –Poor air quality City DegradationCity Degradation Social DegradationSocial Degradation Justification

Negative Impacts of Sprawl Environmental DegradationEnvironmental Degradation City DegradationCity Degradation –More traffic and longer commutes –Higher taxes –Loss of local businesses –Inner-city decay Social DegradationSocial Degradation Justification

Negative Impacts of Sprawl Environmental DegradationEnvironmental Degradation City DegradationCity Degradation Social DegradationSocial Degradation –Decline in economic opportunity –Loss of sense of place and community –Reduced access to open space –Reduced social interaction that threatens the way people live together Justification

What is Urban Sprawl? Four Ways to Define Using a Quantitative IndicatorUsing a Quantitative Indicator Using a Qualitative IndicatorUsing a Qualitative Indicator Attitudinal DefinitionAttitudinal Definition Specific Landscape PatternsSpecific Landscape Patterns Background

Problems with the “sprawl” term No universally accepted definition of sprawlNo universally accepted definition of sprawl Not all urban growth is considered to be sprawlNot all urban growth is considered to be sprawl The same development can be considered sprawl to some and not to othersThe same development can be considered sprawl to some and not to others The use of the term “urban sprawl” has a negative connotation and not all urban growth is necessarily unhealthyThe use of the term “urban sprawl” has a negative connotation and not all urban growth is necessarily unhealthy Some types of growth are actually remedies for sprawlSome types of growth are actually remedies for sprawl Background

Urban growth model instead of urban sprawl model Allows us to quantify the amount of land that has changed to urban usesAllows us to quantify the amount of land that has changed to urban uses Lets the user decide what he or she considers to be urban sprawlLets the user decide what he or she considers to be urban sprawl Background

Characteristics of a Good Model Spatially detailed data with fine spatial grain and avoids spatial averagingSpatially detailed data with fine spatial grain and avoids spatial averaging Examines the whole landscape and assesses urban growth in all areasExamines the whole landscape and assesses urban growth in all areas Displays the emergence of growth over timeDisplays the emergence of growth over time Broadly available to allow for regional planningBroadly available to allow for regional planning Has historical depth and is consistent over timeHas historical depth and is consistent over time Is quantifiableIs quantifiable Maintains spatial pattern and configurationMaintains spatial pattern and configuration Is intuitive, interpretable, easy to calculate, and does not require much data inputIs intuitive, interpretable, easy to calculate, and does not require much data input Background

Limitations of Other Methods Some other techniques:Some other techniques: –Average change data over a geographic area –View statistics according to a sharp boundary –Calculate information over a given area resulting in a loss of spatial integrity This modelThis model –Information is calculated per pixel resulting in a map that provides a more realistic representation of the landscape –Maintains spatial pattern and arrangement –The output urban growth map identifies how much and what kind of change has occurred, and its relation to other landscape features Background

Benefits of Using Satellite Imagery Satellite data are spatially registered with reasonable spatial grain or resolutionSatellite data are spatially registered with reasonable spatial grain or resolution Satellite imagery provides an excellent data source for landscape coverage and scenes do not stop at political boundariesSatellite imagery provides an excellent data source for landscape coverage and scenes do not stop at political boundaries More than three decades of data (Landsat) is availableMore than three decades of data (Landsat) is available Background

Outline IntroductionIntroduction Justification and BackgroundJustification and Background –Urban Growth Classes InfillInfill ExpansionExpansion OutlyingOutlying –Isolated –Linear Branching –Clustered Branching MethodsMethods Examples of Growth TypesExamples of Growth Types ApplicationsApplications Considerations and ConclusionConsiderations and Conclusion

InfillInfill A non-urban pixel is converted to urban and is surrounded by at least 40% existing urban pixelsA non-urban pixel is converted to urban and is surrounded by at least 40% existing urban pixels Defined as the development of a small area surrounded by existing developed landDefined as the development of a small area surrounded by existing developed land ExpansionExpansion OutlyingOutlying –Isolated –Linear Branching –Clustered Branching InfillInfill A non-urban pixel is converted to urban and is surrounded by at least 40% existing urban pixelsA non-urban pixel is converted to urban and is surrounded by at least 40% existing urban pixels Defined as the development of a small area surrounded by existing developed landDefined as the development of a small area surrounded by existing developed land ExpansionExpansion OutlyingOutlying –Isolated –Linear Branching –Clustered Branching Urban Growth Classes Background

InfillInfill ExpansionExpansion Characterized by a non-urban pixel being converted to urban use and surrounded by no more than 40% existing urban pixels.Characterized by a non-urban pixel being converted to urban use and surrounded by no more than 40% existing urban pixels. Defined as the spreading out of urban land cover from existing developed landDefined as the spreading out of urban land cover from existing developed land OutlyingOutlying –Isolated –Linear Branching –Clustered Branching InfillInfill ExpansionExpansion Characterized by a non-urban pixel being converted to urban use and surrounded by no more than 40% existing urban pixels.Characterized by a non-urban pixel being converted to urban use and surrounded by no more than 40% existing urban pixels. Defined as the spreading out of urban land cover from existing developed landDefined as the spreading out of urban land cover from existing developed land OutlyingOutlying –Isolated –Linear Branching –Clustered Branching Urban Growth Classes Background

InfillInfill ExpansionExpansion OutlyingOutlying Characterized by a change from non-urban to urban land cover occurring beyond existing urban areasCharacterized by a change from non-urban to urban land cover occurring beyond existing urban areas –Isolated –Linear Branching –Clustered Branching InfillInfill ExpansionExpansion OutlyingOutlying Characterized by a change from non-urban to urban land cover occurring beyond existing urban areasCharacterized by a change from non-urban to urban land cover occurring beyond existing urban areas –Isolated –Linear Branching –Clustered Branching Background

Urban Growth Classes InfillInfill ExpansionExpansion OutlyingOutlying –Isolated One or several non-urban pixels away from an existing urban area is/are converted to urban useOne or several non-urban pixels away from an existing urban area is/are converted to urban use Defined as a new, small area of construction surrounded by non-urban land and some distance from other developed areasDefined as a new, small area of construction surrounded by non-urban land and some distance from other developed areas –Linear Branching –Clustered Branching InfillInfill ExpansionExpansion OutlyingOutlying –Isolated One or several non-urban pixels away from an existing urban area is/are converted to urban useOne or several non-urban pixels away from an existing urban area is/are converted to urban use Defined as a new, small area of construction surrounded by non-urban land and some distance from other developed areasDefined as a new, small area of construction surrounded by non-urban land and some distance from other developed areas –Linear Branching –Clustered Branching Background

Urban Growth Classes InfillInfill ExpansionExpansion OutlyingOutlying –Isolated –Linear Branching Defined as a new road, corridor, or linear development surrounded by non-urban and some distance from other urban areasDefined as a new road, corridor, or linear development surrounded by non-urban and some distance from other urban areas Different from an isolated growth in that the pixels that changed to urban are connected in a linear fashionDifferent from an isolated growth in that the pixels that changed to urban are connected in a linear fashion –Clustered Branching InfillInfill ExpansionExpansion OutlyingOutlying –Isolated –Linear Branching Defined as a new road, corridor, or linear development surrounded by non-urban and some distance from other urban areasDefined as a new road, corridor, or linear development surrounded by non-urban and some distance from other urban areas Different from an isolated growth in that the pixels that changed to urban are connected in a linear fashionDifferent from an isolated growth in that the pixels that changed to urban are connected in a linear fashion –Clustered Branching Background

Urban Growth Classes InfillInfill ExpansionExpansion OutlyingOutlying –Isolated –Linear Branching –Clustered Branching A new urban growth that is neither linear nor isolated, but instead a cluster or a groupA new urban growth that is neither linear nor isolated, but instead a cluster or a group Defined as a new, large and dense development in a previously undeveloped areaDefined as a new, large and dense development in a previously undeveloped area InfillInfill ExpansionExpansion OutlyingOutlying –Isolated –Linear Branching –Clustered Branching A new urban growth that is neither linear nor isolated, but instead a cluster or a groupA new urban growth that is neither linear nor isolated, but instead a cluster or a group Defined as a new, large and dense development in a previously undeveloped areaDefined as a new, large and dense development in a previously undeveloped area Background

Outline IntroductionIntroduction Justification and BackgroundJustification and Background Urban Growth ClassesUrban Growth Classes MethodsMethods –Fragmentation Model –Urban Change Map –Urban Growth Map –“Clump” Step –Graphical Interface Examples of Growth TypesExamples of Growth Types ApplicationsApplications Considerations and ConclusionConsiderations and Conclusion

Methods Roots of the Urban Growth Model lie in a forest fragmentation model developed by Riitters et al (2000) that assigns forest pixels to five fragmentation categories: interior, edge, perforated, transitional, and patchRoots of the Urban Growth Model lie in a forest fragmentation model developed by Riitters et al (2000) that assigns forest pixels to five fragmentation categories: interior, edge, perforated, transitional, and patch One idea was utilized for the urban growth model – that of proportion of forest (Pf)One idea was utilized for the urban growth model – that of proportion of forest (Pf) Pf is created from a moving window that quantifies the total number of forest pixels in the window as compared to total number of non-water pixelsPf is created from a moving window that quantifies the total number of forest pixels in the window as compared to total number of non-water pixels Methods

Methods Adaptations:Adaptations: –Perforated and edge combined and called perforated (Pf>0.6) –Patch and transitional combined and called patch (Pf <= 0.6) –Input image changed from forest/non-forest binary image to non-urban/urban binary image Pf is further referred to as the proportion of non-urbanPf is further referred to as the proportion of non-urban Methods Riitters et al, 2000

Overall Methodology Non-urban Fragmentation Maps Date 1 Date 2 Map of Change Map of Urban Growth Land Cover Maps Date 1 Date 2 Methods

Land Cover Maps Methods Date 1 Date 2 Land Cover Urban Agriculture Deciduous Forest Coniferous Forest Water Wetland Barren 3 class minimum: - Water - Urban - Non-urban

Overall Methodology Non-urban Fragmentation Maps Date 1 Date 2 Map of Change Map of Urban Growth Land Cover Maps Date 1 Date 2 Methods

Non-urban Fragmentation Map Input:Input: –Land cover map that separates urban, non-urban and water –User can choose what land cover types are “urban” Each center pixel is classified as:Each center pixel is classified as: –Interior non-urban: All pixels in 5x5 window are non-urban –Perforated non-urban: Between 60% and 100% of pixels in 5x5 window are non-urban –Patch non-urban: Fewer than 60% of pixels in a 5x5 window are non-urban Methods

Non-urban Fragmentation Map Methods Date 1 Date 2 Fragmentation Water Urban Interior Patch Perforated

Overall Methodology Non-urban Fragmentation Maps Date 1 Date 2 Map of Change Map of Urban Growth Land Cover Maps Date 1 Date 2 Methods

Change Class Date 1 Date 2 Urban – No change UrbanUrban Water – No change WaterWater Interior – No change InteriorInterior Fragmentation Class – No change Perforated/patch Same fragmentation class as first date Change within fragmentation class Perforated/patch Fragmentation class different from first date Urban to fragmentation class UrbanPerforated/patch Change to Interior UrbanInterior Interior to Urban InteriorUrban Interior to Perforated InteriorPerforated Interior to Patch InteriorPatch Perforated to Urban PerforatedUrban Patch to Urban PatchUrban Map of Change Use fragmentation maps from 2 dates Use fragmentation maps from 2 dates NO CHANGE CLASSES REGROWTH CLASSES (many cases are classification errors) Methods

Map of Change Methods Change Water – no change Urban – no change Interior – no change Fragmentation – no change Interior to Urban Interior to Perforated Interior to Patch Change within fragmentation Perforated to Urban Patch to Urban Change to Interior

Overall Methodology Non-urban Fragmentation Maps Date 1 Date 2 Map of Change Map of Urban Growth Land Cover Maps Date 1 Date 2 Methods

Map of Change Significant Changes Type of Growth Patch to urban In-fill Growth Perforated to urban Expansion Growth Interior to Urban “Outlying” Growth Isolated Linear Branching Clustered Branching Methods

Outlying Growth Defined Outlying Growth Defined IsolatedIsolated Requires a small area of change many interior-to- perforated pixels and few or no interior-to-patch pixelsRequires a small area of change many interior-to- perforated pixels and few or no interior-to-patch pixels Linear BranchingLinear Branching Not limited by sizeNot limited by size Occurs when there is an extended border between the outlying growth pixels and the non-urban pixels requires many interior-to-perforated pixelsOccurs when there is an extended border between the outlying growth pixels and the non-urban pixels requires many interior-to-perforated pixels The number of interior-to-patch pixels limits the linear class.The number of interior-to-patch pixels limits the linear class. Clustered BranchingClustered Branching Occurs when outlying growth pixels are close together requires interior-to-patch pixels (opposite of linear branch)Occurs when outlying growth pixels are close together requires interior-to-patch pixels (opposite of linear branch) Pixels are close together low occurrence of interior-to- perforated pixelsPixels are close together low occurrence of interior-to- perforated pixels Methods

Urban Growth Map Methods Urban Growth Urban Water Interior In-fill Expansion Isolated Linear Branch Clustered Branch

“Outlying” Growth Problem The ProblemThe Problem –All growth areas were bordered by linear and/or isolated pixels –Most linear branches had some isolated and/or cluster pixels –The resulting image was confusing with minimal meaning The SolutionThe Solution –Each outlying growth should be one type (isolated OR linear branching OR clustered branching) –Group each area of “outlying” growth type change and assign only one type MethodsInteriorIn-fill Expansion Isolated Linear Branch Clustered Branch

“Clump” Rules Within Each Clump More isolated pixels than linear branch or clustered branch Isolated More clustered branch pixels than isolated or linear branch Clustered Branch More linear pixels than isolated or clustered AND Clustered Branch More linear pixels than isolated or clustered AND Linear Branch Methods First: “clump” the outlying growth pixels based on neighbors Then: Determine what each clump should be

“Outlying” Growth Clumped Urban Change Clump Urban Growth Urban Growth After Clump Methods IKONOS

“Outlying” Growth Clumped Urban Change Clump Urban Growth After Clump Methods IKONOS

Transferability The methods and graphical models for each step in the creation of the urban growth map had been developedThe methods and graphical models for each step in the creation of the urban growth map had been developed We wanted to make it easy to implement for our other data as well as data of othersWe wanted to make it easy to implement for our other data as well as data of others Developed a user interface for ERDAS ImagineDeveloped a user interface for ERDAS Imagine Methods

Urban Growth Graphical Interface Methods

Outline IntroductionIntroduction Justification and BackgroundJustification and Background Urban Growth ClassesUrban Growth Classes MethodsMethods Examples of Growth TypesExamples of Growth Types –In-Fill –Expansion –Isolated Growth –Linear Branch –Clustered Branch ApplicationsApplications Considerations and ConclusionConsiderations and Conclusion

In-fill Growth TM Date 1 Date 2 TM Growth Types Change Fragmentation Land Cover Growth

Legends Land Cover Urban Agriculture Deciduous Forest Coniferous Forest Water Wetland BarrenFragmentationWater Urban Interior Patch Transitional Perforated Edge Change Water – no change Urban – no change Interior – no change Fragmentation – no change Interior to Urban Interior to Perforated Interior to Patch Change within fragmentation Perforated to Urban Patch to Urban Change to Interior Urban Growth Urban Water Interior In-fill Expansion Isolated Linear Branch Clustered Branch Growth Types

Expansion Growth TM Date 2 TM Date 1 Growth Types Fragmentation Land Cover Change IKONOS Growth

Isolated Growth TM Date 2 TM Date 1 Growth Types Land Cover Fragmentation Change IKONOS Growth

Linear Branching Growth Change TM Date 2 TM Fragmentation Date 1 Land Cover Growth Types IKONOS Growth

Clustered Branching Growth TM Date 2 TM Date 1 Growth Types Land Cover Change Fragmentation Digital Orthophoto Growth

Outline IntroductionIntroduction Justification and BackgroundJustification and Background Urban Growth ClassesUrban Growth Classes MethodsMethods Examples of Growth TypesExamples of Growth Types ApplicationsApplications –Fragmentation of forest and/or open space –Agricultural Land Lost –Forest Land Lost –Natural Resource Issues –Landscape Dynamics and Decision Making –Visualizations –Urban Growth Dynamics Considerations and ConclusionConsiderations and Conclusion

Applications Fragmentation of Forest and/or Open Space IKONOS Applications

Applications Agricultural Land Lost 1985 TM 1985 Land Cover 1990 TM Growth 2000 Spring IKONOS Applications

Applications Forest Land Lost and Growth Dynamics Date 1 Applications Landsat TM/ETM Land Cover Fragmentation Change Urban Growth

Applications Natural Resources and Growth Dynamics Growth 1985 Land Cover 1985 TM 1990 TM Growth 1995 TM Growth 1999 ETM 2000 Spring IKONOS Applications

Applications Landscape Dynamics and Decision Making Town officials can view the collective results of many site- level decisionsTown officials can view the collective results of many site- level decisions Users can predict what the effect of pending or proposed developments might be on the town landscapeUsers can predict what the effect of pending or proposed developments might be on the town landscape Applications Colchester, CT Growth

Applications Visualizations and Growth Dynamics Applications

Outline IntroductionIntroduction Justification and BackgroundJustification and Background Urban Growth ClassesUrban Growth Classes MethodsMethods Examples of Growth TypesExamples of Growth Types ApplicationsApplications Considerations and ConclusionConsiderations and Conclusion

Considerations Model was developed using pixel-by- pixel Landsat TM-derived 30 meter classification of the Salmon River watershed in ConnecticutModel was developed using pixel-by- pixel Landsat TM-derived 30 meter classification of the Salmon River watershed in Connecticut –Varying moving window sizes, classification techniques and regions have not been thoroughly tested –Image registration –Snapshot of a point in time can be misleading Conclusion

Conclusions Remote sensing derived land cover information can be an excellent data source for examining, quantifying, categorizing and mapping urban growth.Remote sensing derived land cover information can be an excellent data source for examining, quantifying, categorizing and mapping urban growth. Past techniques have measured the amount of urban change but have failed to adequately categorize it.Past techniques have measured the amount of urban change but have failed to adequately categorize it. This model provides a systematic, standardized, and replicable methodology that can be used to describe the urbanization process that provides insight into changing and emerging landscapes and patterns.This model provides a systematic, standardized, and replicable methodology that can be used to describe the urbanization process that provides insight into changing and emerging landscapes and patterns. Conclusion

AcknowledgementAcknowledgement National Aeronautics and Space Administration Grant NAG /NRA-98-OES-08 RESAC- NAUTILUS, Better Land Use Planning for the Urbanizing Northeast: Creating a Network of Value- Added Geospatial Information, Tools, and Education for Land Use Decision Makers. Northeast Applications of Useable Technology In Land planning for Urban Sprawl

This presentation is available at resac.uconn.edu

Development of a Model to Quantify and Map Urban Growth Emily H. Wilson, James D. Hurd, Daniel L. Civco Center for Land Use Education and Research (CLEAR) Department of Natural Resources Management & Engineering The University of Connecticut U-4087, Room 308, 1376 Storrs Road Storrs, CT

How can there be so many definitions of urban sprawl and still no definition? Instead of getting hung up on a definition and contributing to the wealth of literature that discusses this topic... we developed an URBAN GROWTH model. WHAT IS URBAN SPRAWL?? Background

Why Urban Growth and not Urban Sprawl? There is no standard definition of urban sprawlThere is no standard definition of urban sprawl Urban sprawl has a “bad” connotationUrban sprawl has a “bad” connotation Not all urban growth is urban sprawlNot all urban growth is urban sprawl Not all urban growth is “bad”Not all urban growth is “bad” Certain types of growth are remedies for sprawlCertain types of growth are remedies for sprawl Urban growth allows us to quantify the amount of land that has changed to urban and lets the user decide what is “good” and what is “bad”Urban growth allows us to quantify the amount of land that has changed to urban and lets the user decide what is “good” and what is “bad” Background

Non-urban Fragmentation Map Single Date Fragmentation Map Output:Single Date Fragmentation Map Output: –Interior All pixels in 5x5 window areAll pixels in 5x5 window are non-urban non-urban –Perforated/edge Between 60% and 100%Between 60% and 100% of pixels in 5x5 window of pixels in 5x5 window are non-urban are non-urban –Transitional/Patch Fewer than 60% of pixels in a 5x5 window are non- urbanFewer than 60% of pixels in a 5x5 window are non- urban Methods

“Clump” Rules Isolated Linear Branching Clustered Branching Interior-to-urban Few (<5) More (>4) Many (>4) Interior-to-patchNone Few (<2) At least 1 (>1) Interior-to- perforated Many (>4) Many (>3) Few (<4) Methods

Change Growth Expansion Growth TM Date 1 Date 2 Land Cover TM Fragmentation Land Cover Growth Types

A Good Urban Growth Model Is quantifiableIs quantifiable Displays the emergence of urban growth over timeDisplays the emergence of urban growth over time Has historical depth and is consistent over timeHas historical depth and is consistent over time Displays spatially detailed data with fine spatial grainDisplays spatially detailed data with fine spatial grain Maintains spatial pattern and configurationMaintains spatial pattern and configuration Examines the whole landscape, is broadly available to allow for regional planning and assesses urban growth in all areasExamines the whole landscape, is broadly available to allow for regional planning and assesses urban growth in all areas Avoids spatial averagingAvoids spatial averaging Is intuitive, interpretable, easy to calculate, and does not require much data inputIs intuitive, interpretable, easy to calculate, and does not require much data input Background

Outlying Growth Requirements IsolatedIsolated –Few interior-to-urban pixels (<5) –Many interior-to-perforated pixels (>4) Linear BranchingLinear Branching –More interior-to-urban pixels than isolated (>4) –Few interior-to-patch (<2) –Many interior-to-perforated pixels (>3) Clustered BranchingClustered Branching –Many interior-to-urban pixels (>4) –Few interior-to-perforated (<4) –At least some interior-to-patch (>1) Methods

Applications Visualizations and Growth Dynamics Applications

Four Ways to Define Sprawl Quantitative IndicatorQuantitative Indicator –Change in population density where land consumption occurs faster than population growth –Increased vehicle miles of travel (VMT) or vehicle hours of travel (VHT) –Poor accessibility –Low housing density –Proportion of jobs in the city is greater than the proportion of population in the city Qualitative IndicatorQualitative Indicator Attitudinal DefinitionAttitudinal Definition Landscape PatternLandscape Pattern Background

Four Ways to Define Sprawl Quantitative IndicatorQuantitative Indicator Qualitative IndicatorQualitative Indicator –Consumption of resources and land in excess of what is needed for development –Scattering of urban settlement over the rural landscape –Unplanned growth Attitudinal DefinitionAttitudinal Definition Landscape PatternLandscape Pattern Background

Four Ways to Define Sprawl Quantitative IndicatorQuantitative Indicator Qualitative IndicatorQualitative Indicator Attitudinal DefinitionAttitudinal Definition –Unhealthy growth with negative impacts –Self-destructive growth that costs money, consumes land, causes traffic problems, and creates social inequity and isolation –The great urban explosion Landscape PatternLandscape Pattern Background

Four Ways to Define Sprawl Quantitative IndicatorQuantitative Indicator Qualitative IndicatorQualitative Indicator Attitudinal DefinitionAttitudinal Definition Landscape PatternLandscape Pattern –Low density development with dependence on cars –Geographic separation of work, home, school, and shopping –Scattered residential lots in outlying areas –Multi-lot, planned housing developments on new access roads in outlying areas –Large, pedestrian-unfriendly commercial strips Background

Applications Fragmentation of forest and/or open spaceFragmentation of forest and/or open space Agricultural Land LostAgricultural Land Lost Forest Land LostForest Land Lost Natural Resource IssuesNatural Resource Issues Landscape Dynamics and Decision MakingLandscape Dynamics and Decision Making VisualizationsVisualizations Urban Growth DynamicsUrban Growth Dynamics Applications

Examples of Growth Types In-FillIn-Fill ExpansionExpansion Isolated GrowthIsolated Growth Linear BranchLinear Branch Clustered BranchClustered Branch In-FillIn-Fill ExpansionExpansion Isolated GrowthIsolated Growth Linear BranchLinear Branch Clustered BranchClustered Branch Growth Types

Urban Growth Classes InfillInfill ExpansionExpansion OutlyingOutlying –Isolated –Linear Branching –Clustered Branching InfillInfill ExpansionExpansion OutlyingOutlying –Isolated –Linear Branching –Clustered Branching Background