urisa journal volume 21 no.1 2009

8/8/2019 URISA Journal Volume 21 No.1 2009

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September 29October 2, 200947th Annual URISA Conference & Exposition

Anaheim, California

November 1618, 2009GIS in Transit Conference

St Petersburg, Florida

December 711, 2009URISA Leadership Academy

Seattle, Washington

March 811, 201014th Annual GIS/CAMA Technologies Conference

Little Rock, Arkansas

September 28October 1, 2010

48th Annual URISA Conference & ExpositionOrlando, Florida

November 30December 3, 2010URISAs 5th Caribbean GIS Conference

Port of Spain, Trinidad

Upcoming

conferences


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Volume 21 No. 1 2009

Journal of the Urban and Regional Information Systems Association

Contents

RefeReed

5 GeoFIS Flood Insurance System or Trinidad: A Case Study or San Juan

DownstreamF. Canisius and C. Nancy

11 St. Kitts Land Resource Analysis

Edsel B. Daniel, Derek L. Bryant, James P. Dobbins, Ilis Watts, Alan P. Mills,

and Mark D. Abkowitz

21 A Data Model and Internet GIS Framework or Sae Routes to School

Ruihong Huang and Dawn Hawley

31 Modernizing the Register o Deeds in Dane County, Wisconsin

Jane Licht and J. David Staneld

41 Evaluating Spatial Impacts o Changes to Coastal Hazard Policy Language

Ana Puszkin-Chevlin and Ann-Margaret Esnard

51 GIS in Hazard Mapping and Vulnerability Assessment on Montserrat

Lavern Ryan

57 The Land-use Evolution and Impact Assessment Model: A Comprehensive

Urban Planning Support System

Zhanli Sun, Brian Deal, and Varkki George Pallathucheril


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2 URISA Journal Vol. 21, No. 1 2009

Jural

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US ISSN 1045-8077

Publisher: Urban and Regional Inormation Systems Association

Editor-in-Chie: Jochen Albrecht

Journal Coordinator: Wendy Nelson

Electronic Journal: http://www.urisa.org/journal.htm


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URISA Journal Vol. 21, No. 1 2009 3

URISA Journal EditorEditor-in-Chief

Jochen Albrecht, Department oGeography, Hunter College City Universityo New York

Tematic Editors

Editor-Urban and Regional Information

Science

Vacant

Editor-Applications Research

Lyna Wiggins, Department o Planning,Rutgers University

Editor-Social, Organizational, Lega l,and Economic Sciences

Ian Masser, Department o Urban Planningand Management, ITC (Netherlands)

Editor-Geographic Information Science

Mark Harrower, Department o Geography,University o Wisconsin Madison

Editor-Information and Media Sciences

Michael Shier, Department o Planning,Massachusetts Institute o Technology

Editor-Spatial Data Acquisition andIntegration

Gary Hunter, Department o Geomatics,University o Melbourne (Australia)

Editor-Geography, Cartography, and

Cognitive Science

Vacant

Editor-Education

Karen Kemp, Director, International MastersProgram in GIS, University o Redlands

Section Editors

Software Review Editor

Jay Lee, Department o Geography, Kent State

University

Book Review Editor

David Tulloch, Department o LandscapeArchitecture, Rutgers University

Article Review BoardPeggy Agouris, Department o Spatial InormationScience and Engineering, University o Maine

Grenville Barnes, Geomatics Program, Universityo Florida

Michael Batty,Centre for Advanced Spatial Analysis,University College London (United Kingdom)

Kate Beard, Department o SpatialInormation Science and Engineering,University o Maine

Yvan Bdard, Centre or Research in Geomatics,Laval University (Canada)

Barbara P. Butteneld, Department oGeography, University o Colorado

Keith C. Clarke, Department o Geography,University o Caliornia-Santa Barbara

David Coleman, Department o Geodesy andGeomatics Engineering, University o NewBrunswick (Canada)

David J. Cowen, Department o Geography,University o South Carolina

Massimo Craglia, Department o Town &Regional Planning, University o Sheeld(United Kingdom)

William J. Craig, Center or Urban and

Regional Afairs, University o MinnesotaRobert G. Cromley, Department o Geography,University o Connecticut

Kenneth J. Dueker, Urban Studies andPlanning, Portland State University

Georey Dutton, Spatial Efects

Max J. Egenhofer,Department o Spatial InormationScience and Engineering, University o Maine

Manfred Ehlers, Research Center orGeoinormatics and Remote Sensing, University oOsnabrueck (Germany)

Manfred M. Fischer, Economics, Geography &Geoinormatics, Vienna University o Economics

and Business Administration (Austria)Myke Gluck, Department o Math andComputer Science, Virginia Military Institute

Michael Goodchild, Department o Geography,University o Caliornia-Santa Barbara

Michael Gould, Department o InormationSystems Universitat Jaume I (Spain)

Daniel A. Grith, Department o Geography,Syracuse University

Francis J. Harvey, Department o Geography,University o Minnesota

Kingsley E. Haynes, Public Policy andGeography, George Mason University

Eric J. Heikkila, School o Policy, Planning, andDevelopment, University o Southern Caliornia

Stephen C. Hirtle, Department o InormationScience and Telecommunications, University oPittsburgh

Gary Jeress, Department o GeographicalInormation Science, Texas A&M University-Corpus Christi

Richard E. Klosterman, Department oGeography and Planning, University o Akron

Robert Laurini, Claude Bernard University oLyon (France)

omas M. Lillesand, Environmental

Remote Sensing Center, University o Wisconsin-Madison

Paul Longley,Centre or Advanced Spatial Analysis,University College, London (United Kingdom)

Xavier R. Lopez, Oracle Corporation

David Maguire, Environmental Systems ResearchInstitute

Harvey J. Miller, Department o Geography,University o Utah

Zorica Nedovic-Budic, Department o Urbanand Regional Planning,University o Illinois-Champaign/Urbana

Atsuyuki Okabe, Department o Urban

Engineering, University o Tokyo (Japan)Harlan Onsrud, Spatial Inormation Scienceand Engineering, University o Maine

Jerey K. Pinto, School o Business, Penn State Erie

Gerard Rushton, Department o Geography,University o Iowa

Jie Shan, School o Civil Engineering,Purdue University

Bruce D. Spear, Federal Highway Administration

Jonathan Sperling, Policy Development &Research, U.S. Department o Housing andUrban Development

David J. Unwin, School o Geography, Birkbeck

College, London (United Kingdom)Stephen J. Ventura, Department oEnvironmental Studies and Soil Science,University o Wisconsin-Madison

Nancy von Meyer, Fairview Industries

Barry Wellar, Department o Geography,University o Ottawa (Canada)

Michael F. Worboys, Department o ComputerScience, Keele University (United Kingdom)

F. Benjamin Zhan, Department o Geography,Texas State University-San Marcos

editoRsand ReviewBoaRd


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URISA Journal Canisius and Nancy 5

IntroductIon

Flood is one of the most common natural disasters resultingin threats to life and property throughout the world (Sharmaand Priya 2001). Flooding occurs when heavy and continuousrainfall exceeds the absorbing capacity of the soil or the ow ofthe water is greater than the normal carrying capacity of a streamchannel. Statistically, streams equal or exceed the mean annual

ood level once every 2.33 years (Leopold et al. 1964) and causestreams to overow their banks onto anking lands. Flood oftenaccompanies other natural disasters such as brief torrential rain,monsoonal rain, cyclones, hurricanes, or tidal surges (Brakenridgeet al. 2004). In addition, increasing impermeable layers, such asroads, residential buildings, and industrial complexes, reduce the

lands natural ability to absorb water, which increases runo aswell as disturbs the natural water ow, thus increasing the risk ofooding (Ramroop 2005).

In Trinidad, ood is one o the major hazards aecting thecountry every year and during all seasons (Ramroop 2005). In re-cent years, the number o ood occurrences has increased through-out the country. In addition to the previously mentioned commoncauses, actors contributing to ood occurrences in Trinidad areparticularly indiscriminate dumping into streams and improper orillegal hillside land development and agricultural practices (WRA/MIN. Env. 2001). Flood damages can be categorized as physicaldamages to houses and inrastructure, casualties o people and

livestock as a result o drowning, spreading o diseases, scarcityo clean drinking water because o water contamination, anddamages to ood crops (Mileti 1999). According to Mileti (1999),ood hazards severely impede the economy o the United States;translated into the context o Trinidad, damage caused by oodingevents in 1993, 2002, and 2006 are $580,000, $3,300,000, and$2,500,000, respectively (WRA/MIN. Env. 2001, Brakenridgeet al. 2003, Brakenridge et al. 2007).

Ater a decade o economic growth, mainly driven by theenergy sector (IMF Country Report 2005), housing development

in Trinidad has increased considerably even in ood-prone areas.

Economic values o houses have increased with the use o costlyfxtures, which urther add to the losses. Unortunately, ood in-

surance has not kept up with housing development and insuranceproviders lack the tools to properly predict potential losses andrecommend mechanisms to beneft both parties in the insurancemarket. The insurer, more oten than not, is an agent in a chaino transer o premiums in return or potential compensation.This kind o risk transer is depicted in Figure 1.

However, potential clients are not readily purchasing ood-insurance policies because o high premiums (Browne and Hoyt2000, Miller 1997, Preist et al. 2005). Thus, implementing ood

insurance or private households with aordable premiums is in

the best case difcult and in the worst case plainly not proftable(Miller 1997). For these reasons, it is very important to classiyareas based on their ood risk. Geographic inormation systems(GIS) can be used to categorize ood-risk zones by analyzingcomplex spatial data sets rom dierent sources (Gangai et al.2003). In this study, GIS orms the basis or a private house-hold ood-insurance system or Trinidad to calculate premiums

based on household exposure to ood risk and to speed up theunderwriting process.

GoFIS Foo Insan Sys fo tinia:

A cas Sy fo San Jan downsa

F. Canisius and C. Nancy

Abstract: Floods, among the most severe natural perils causing risk to lie and property in every corner o the world, have becomemore requent in recent years because o increasing alterations o the environment. Damages caused by foods create great loss to

individuals. Without insurance, it is dicult to recover rom the impacts. In some countries, though, insurance companies charge

premiums based on region rather than on the location o the individual property. In the case o Trinidad, this general way o

premium calculation is in practice combined with standard property insurance. By integrating geographic inormation systems

(GIS) to the food-risk assessment o each and every individual private house, a more equitable premium can be calculated. This

is exemplied in this study, where a GIS-based food insurance system was developed or Trinidad to handle food insurance or

private houses. The system uses food and house inormation rom a GIS database and client-provided inormation to calculate

reasonable premiums. This benets both clients and providers o food-insurance policies.

Figure 1. Insurance Model


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Flood-prone AreAS In

trInIdAd

Trinidad is situated at the southernmost end of the Caribbean

island chain located at latitude 10.5 N, longitude 61.5 W, and

is approximately 5,126 km2 in size. e climate of Trinidad istropical wet, with an average rainfall of 2,200 mm (WRA/MIN.Env. 2001) and its monsoonal character results in high-intensity

rainfall and subsequent frequent ooding (Bryce 2007).The ood history o Trinidad shows that the requency and

intensity o ooding events is increasing (Bryce 2007). Based oninormation collected rom newspaper articles (Maharaj 2006),the Water and Sewerage Authority (WASA), and the Ofce orDisaster Preparation and Management (ODPM), we mapped

more than 100 locations in Trinidad that have been ooded in19862006 (see Figure 2). In our locations, oods have occurredten or more times within the past 20 years. More than 30 o theselocations are in high-density settlement areas and oods in theseareas cause signifcant economic damages. Typically, they occurin brie storms associated with sheet or surace ow (Baban and

Kantarsingh 2005).It is widely documented (e.g., Chan 1997, Smith 1991,

Baban and Canisius 2007) that alluvial planes prone to oodingalso are oten densely populated and contain highly built-up areasvulnerable to ooding. Figure 2 shows that this holds true orTrinidad as well.

FunctIonAlIty oF GeoFIS

Based on Figure 2, signicant areas in Trinidad are ood proneand coincide with residential developments. erefore, a needexists for introducing a ood-insurance system for Trinidad tocover nancial losses caused by ooding. e adaptation of the

British ood-insurance system has proven unsuitable, for manyhouseholders who are living out of a ood-prone area would haveto pay higher insurance premiums. is is because UK insurers

traditionally determine ood-risk premiums on the basis of ad-ministrative boundaries/postcode bands rather than on particular

addresses (Ordnance Survey 2007).The GeoFIS ood-insurance system simplifes the process o

risk assessment o private households by integrating GIS, allowinginsurers to veriy and evaluate the ood-risk level o a propertyand to fx a premium. Based on a GIS, the operator may zoomin on the house to be insured or a visual clarifcation. There arefve main components to this system (see Figure 3): (1) spatiallyidentiy a particular property located in a ood-prone area; (2)analyze the vicinity o ood boundaries to predict uture chances

or ooding; (3) classiy the ood-risk level o the house based onthe ood-prone area and considering previous ood-event state-ments by clients and number o insurance claims; (4) estimatearea, age, and number o stories o the house and calculate thehouses value, including other house inormation, such as con-struction o the house and permanently installed fxtures; and (5)calculate the premium based on the ood-risk class.

Flood-rISk AnAlySIS

To classify ood risk, a houses location is identied to determinewhether it is located inside or outside the ood zone. If the houseis identied as lying in a ood zone, the ood-recurrence interval

Figure 2. Flood-prone Areas and Flood Locations in Trinidad Figure 3. System Flowchart


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of square feet, permanently installed xtures, and construction ofthe house, and the percentage of the premium, the percentage ofthe discount of the ood-risk classes, and the percentage of thehouse value with regard to the age of the house.

Case Study o San Juan DownstreamWe selected San Juan as a study area for GeoFIS; it is the thirdlargest city of the country and undergoes sizable developments,even in oodplain areas.

Data CollectionFlood data, houses, roads, rivers, elevation data, and aerial pho-tos (shown in Figure 4) were collected from the Department ofSurveying and Land Information, University of the West Indies.e 1994 and 2003 aerial photos were used to update house dataand to estimate the number of stories and the ages of houses. Asite visit was performed for some ground truthing. is includedgetting experts to estimate the square-foot market value. In addi-tion, personal-level information about the client and the house

from the application les was obtained.

Acquire Area, Age, and Number o Stories o

HouseTo calculate the area of each house, we updated our les based ona 2003 aerial photograph mosaic that we created using ERMappersoftware. e house data then were digitized and updated using

ArcView (see Figure 5A).

is analyzed in a second step. If the house is located outside theood zone, the likelihood for ooding is determined by calculating

the elevation dierence between the property and the nearest atplain, where river and drainage channels pass through.

Three ArcView Avenue scripts implement the outlined ap-proach: (1) identifcation o a property location on a oodplain,(2) calculation o the distance to the oodplain, and (3) determin-ing the oodplain in the frst place:

To identiy whether a house is located inside a oodplain, frstretrieve the address polygon using the address ID. Next create xand y coordinates or the retrieved address polygon and create a

point eature or it. Then intersect the created house point with theood boundary and determine whether the house is located insidethe ood boundary. Finally, check the ood-recurrence intervalo the house that was identifed inside the ood boundary.

I the house is located outside the ood boundary, thendetermine the elevation dierence between the house elevationand the nearest ood boundary elevation. Obtain the elevationo the house by intersecting the house point with the average

elevation. Then fnd the elevation o the ood boundary by in-

tersecting the ood polygon with the buered house polygon bythe calculated minimum distance and obtain the smaller value othe two. Next create a point as described previously to intersectthe point with the average elevation and determine the elevationo the ood boundary. Finally, calculate the elevation dierenceby subtracting the elevation o the house rom the elevation othe ood boundary.

The location o a house in a oodplain, where river and

drainage channels pass through, is identifed in the ollowing steps.First, fnd that the house is located in a oodplain by intersectingthe house point calculated in (1) or (2) with the oodplain poly-gon. Then intersect the river or drainage channels polygon and

retrieve the at plain polygon to ensure that the river or drainagechannel crosses the identifed oodplain.

ASSeSS HouSe VAlue And

cAlculAte InSurAnce

premIum

In the assessment of the house value, its size, age, and numberof stories are used. With the assessed house value, the value ofpermanent xtures (built-in dishwasher, hot-water heaters, shelv-ing and cabinetry, plumbing xtures, stoves, ovens, refrigerator,and air conditioner) and the construction of the house (varieties

of wall, oor, roof, and window) are added to calculate the totalvalue of the house. Using MS Access, derive the area of the houseand multiply the derived area by the number of stories to obtain

the total area of the house. Next, multiply the total area of thehouse by the market price of square feet. At this point, consider-ing the age of the house, add the percentage of the house valueand calculate the total value of the house. e area, number ofstories, age, and total value of the house are subjected to verica-tion by the client. Extra tables and procedures are encoded toupdate market prices and changes in the variability of the price

Figure 4. Flood Map


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To determine the age o a house, we employed multidateaerial photographs. A RGB color composite was developed usingmultidate aerial photographs obtained in 1994 and 2003 (red:1994, green: 2003, blue: 1994). We then classifed houses as eitherless than ten years old (green color house in Figure 5B) or morethan ten years old (white color in Figure 5B).

To identiy the number o stories o a house, we developeda stereo model using two consecutive aerial photographs taken

directly one ater the other with about 60 percent overlap o thearea. This was done by DVP digital photogrammetry sotware, ad-

justing interior, relative, and absolute orientations. The height othe house was measured rom the 2003 stereo model (Figure 5C).

A height o less than 3.5 m was considered a single story and each2.5 m above a single story was considered one additional story.These houses heights were urther confrmed during feld visits tothe study area. The area, number o stories, and age o the house

were subjected to cross-check with the inormation provided by

the house owner beore calculating the house value.

InSurAnce ASSeSSmentWith the GeoFIS ood-insurance system, insurance premiumsfor a house are calculated based on its ood-risk class in relation

to the houses location (see Figure 6). To identify the ood-riskclasses, the following four criteria were used: (1) e house is lo-cated inside the ood boundary; (2) the ood-recurrence intervalof the ood boundary is less than or equal to ve years; (3) theelevation dierence between the house elevation and the eleva-tion of ood is less than two meters; and (4) a waterway crossesat land (less than 1 percent slope). If ooding in a particulararea is very frequent (the ood-recurrence interval of the oodboundary is less than or equal to ve), the houses in the ood

boundary are classied as very high or high risk. In our studyarea, ooding is very frequent; therefore, the houses in the oodboundary are classied as very high risk. is procedure is sum-marized in Figure 6.

When we applied our criteria to actual ooding data (Figure7 and Table 1), we ound that 1.47 percent o very low risk, 7.59percent o low risk, 17.8 percent o medium risk, and 36 percento very high risk classes were ooded in the past. These percent-ages are encouraging, although we would obviously preer to get

a better handle on those judged to be low risk. We assume that a

signifcant number o these houses were ooded because o otherreasons such as improper drainage or drainage blocks that werenot considered in this study.

Table 1. Classifcation o Houses into Risk Classes

Flood-riskClass

No. ofHouses

No. of Flood-ed Houses

% Flooded

Very low 681 10 1.47

Low 580 44 7.59

Moderate 680 121 17.8

High 0 0 0

Very high 100 36 36

Total number

of houses2041 211 10.34

dIScuSSIon

e GeoFIS ood-insurance system was developed to determinethe ood risk of private properties. e system requires high-resolution satellite and aerial imagery to derive a detailed oodmap, which would be expensive to implement for the entire coun-try. However, in Trinidad, the frequency of ooding, subsequent

nancial loss, and rapid development of built-up areas mandatethat this system be implemented.

According to the Federal Citizen Inormation Center (FCIC)in the United States, about 25 percent o all ood-insurance

claims come rom outside the Federal Emergency ManagementAgency (FEMA) classifcation o high-risk areas. Available oodmaps o Trinidad do not have the necessary resolution to trulyrepresent the actual probability o ood danger o each individualprivate home. Ramroop (2005) recommends that the NationalEmergency Management Association (NEMA) be authorized to

(A) (B) (C)Figure 5. Aerial Photographs o 1994 and 2003: (5A) 2003 Aerial

Photograph, (5B) R:G:B: 1994: 2003: 1994, (5C) 2003 Stereo Model

Figure 6. Flood-risk Analysis


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develop maps o ood-prone areas. SAR data is one possible sourceor the development o ood maps (Canisius et al. 1998). Non-governmental organizations (NGOs) in Trinidad also showed theirinterest in developing ood maps using hydrological models.

The system will require regular updates. For instance, in theten-year period rom 1994 to 2003, the Bamboo Grove settlementincreased by about 20 percent and the expansion o a highway mayhave changed oodplain conditions. This updating, however, willnot aect the core unction o the system, where separate lookuptables are used or variable parameters.

concluSIon

Insurance is a business of transferring risk. Understanding insur-ance in general and using GIS data in particular provides valuableinput to realistically analyzing ood risk. Higher accuracy in risk

assessment will help to prepare for likely increases in ood eventsthat will enable all parties to make use of ood insurance for theiradvantages. e GeoFIS ood-insurance system was developed byintegrating GIS into a general-purpose home-insurance systemto improve processing and calculate fair premiums based on theood-risk class of each property. Not only is this system useful forpremium calculation but it also educates and prepares the entities

of the insurance market about future ood perils.The system has classifed fve ood-risk classes; they are: veryhigh, high, moderate, low, and very low. By this classifcation, thesystem has provided clients a air premium discount according tothe vulnerability o their houses. This system oers advantages orboth parties o the ood-insurance market: Clients can obtain theood insurance and pay premiums based on the vulnerability othe ooding o their respective homes; insurers, on the other hand,can promote and sell their ood insurance to those homeowners

who promise a long-term proft.

About the Authors

Dr. Francis Canisius is currently a visiting scientist at CanadaCentre or Remote Sensing, Natural Resources Canada,and he was attached with the Department o Surveying andLand Inormation, University o the West Indies, Trinidadand Tobago.

Ms. Sophia Nancyreceived her BSc. in Inormation Systems andManagement rom University o London, UK and she is alicensing specialist at Adobe Systems Inc. Ottawa, Canada.

Acknowledgments

We express our sincere thanks to Dr. Jacob Opadeyi and Dr.Bheshem Ramlal of the Department of Surveying and Land In-formation, University of the West Indies, Trinidad and Tobago,for providing the data used in this study.

Reerences

Baban, S., and F. Canisius. 2007. GIS methodology or identiying

and mapping ood prone areas in Trinidad. In S. Baban, Ed.,Enduring geohazards in the Caribbean, Chapter 9. Trinidadand Tobago: UWI Press, 2007.

Baban, S. M. J., and R. Kantarsingh. 2005. Mapping oods inthe St. Joseph watershed, Trinidad, using GIS. International

Association o Hydrological Sciences 295: 254-64.Brakenridge, G. R., E. Anderson, and S. Caquard. 2003.

Global active archive o large ood events, 2002 global

register o extreme ood events. Hanover, NH: DartmouthFlood Observatory, Hanover NH, http://www.dartmouth.edu/~oods/Archives/2002sum.htm.

Brakenridge, G. R., E. Anderson, and S. Caquard. 2004. Globaland regional analyses, world atlas o large ood events.Hanover, NH: Dartmouth Flood Observatory, http://www.dartmouth.edu/~oods/archiveatlas/cause.htm.

Brakenridge, G. R., E. Anderson, and S. Caquard. 2007.Global active archive o large ood events, 2006 global

register o extreme ood events. Hanover, NH: DartmouthFlood Observatory, http://www.dartmouth.edu/~oods/

Archives/2006sum.htm.

Browne, M. J., and R. E. Hoyt. 2000. The demand orood insurance: empirical evidence. Journal o Risk andUncertainty 20(3): 291-306.

Bryce, R. 2007. Trinidad and Tobago report. Caribbean Land andWater Resources Network (CLAWRENET) and Hydrologistat the Ministry o Agriculture, Land and Marine Resources

(MALMR), Trinidad and Tobago, http://www.procicaribe.org/networks/clawrenet/reports/z_tt/tt.htm.

Canisius, F. X. J., H. Kiyoshi, M. K. Hazarika, and L. Samarakoon.1998. Flood monitoring in the central plain o Thailand

Figure 7. Flood-risk Classifcation o the Houses


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using NOAA/AVHRR and JERS-1 SAR data. 24th AnnualConerence and Exhibition o the Remote Sensing Society,UK, September 9-11, 1998.

Chan, N. W. 1997. Increasing ood risk in Malaysia: causesand solutions. Disaster Prevention and Management 6(2):72-86.

Gangai, J., J. B. Lee, Dewberry and Davis. 2003. A case study:

utilizing GIS tools to aid in the production o ood insurancerate maps or coastal communities, Proceedings o the 3rdBiennial Coastal GeoTools Conerence, Charleston, SC.

IMF Country Report. 2005. Trinidad and Tobago: selected issues.International Monetary Fund Report No. 05/6. Washington,D.C.: International Monetary Fund, Publication Services.

Leopold, L. B., M. G. Wolman, and J. P. Miller. 1964. Fluvialprocesses in geomorphology. San Francisco, CA: W. H.

Freeman.Maharaj, A. N. 2006. Methodology or identiying and mapping

ood prone areas in Trinidad using GIS. BSc Research Project,University o the West Indies, Trinidad and Tobago.

Miller, J. 1997. Floods: people at risk, strategies or preservation.

New York: United Nations.Mileti, D. S. 1999. Disasters by design. Washington, D.C.: NAS

Joseph Hentry Press.

Ordnance Survey. 2007. Case studies. Great Britains nationalmapping agency, http://www.ordnancesurvey.co.uk/oswebsite/business/sectors/insurance/news/casestudies/raisingstandardoodrisk.htm.

Priest, S. J., M. J. Clark, and E. J. Treby. 2005. Flood insurance:the challenge o the uninsured. International Journal oGeographical Inormation Science 37(3): 295-302.

Ramroop, S. 2005. Proposed ooding analysis research using GISor sample areas in Trinidad and Tobago. American Congresson Surveying and Mapping, Caliornia Land Surveyors

Association, Nevada Association o Land Surveyors, WesternFederation o Proessional Surveyors, Conerence andTechnology Exhibition, Nevada, March 18-23, 2005.

Rosenbaum, W. 2005. The developmental and environmentalimpacts o the national ood insurance program: a review

o literature. Washington, D.C.: American Institutes orResearch.

Sharma, V., and T. Priya. 2001. Development strategies or oodprone areas, case study: Patna. India Disaster Prevention andManagement 10(2): 101-9.

Smith, K. 1991. Environmental hazards: assessing risk andreducing disaster. Routledge Taylor and Francis Group.

WRA/MIN. Env. 2001. Integrating the management owatersheds and coastal areas in Trinidad and Tobago. Water

Resource Agency, The Ministry o the Environment.


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URISA Journal Daniel, Bryant, Dobbins,Watts, Mills, Abkowitz 11

IntroductIon

Over the past 350 years, the Federation of St. Kitts and Nevis has

built its economy around agriculture, focusing primarily on sug-arcane production. Preferential market arrangements with Europehave played a signicant role in keeping the sugar industry activeas world sugar prices declined. A changing global economy has cre-ated the opportunity for new, more viable markets and increasedcompetition. e World Trade Organization (WTO) rulingsagainst preferential market arrangements has dealt a severe blowto the Caribbean sugar industry, not only for the Federation, but

for other eastern Caribbean islands (e.g., Dominica, St. Vincent,and St. Lucia) with similar preferential arrangements for otheragricultural products such as bananas. e resulting uctuationin commodity prices and reduced European Union trade prefer-ences have made the islands reliance on single-crop agriculturean economic vulnerability. To address such vulnerabilities, theseisland governments have focused on diversifying their economies,a trend that includes a growing number of island nations fromthe Caribbean to Asia and the Pacic (e.g., Malaysia) for similar

reasons (SLG 2000, FAO 2001, Gunasena 2001, SLG 2006,GML 2006, GMR 2006).

The Government o St. Kitts and Nevis (GoSKN) decided toclose the sugar industry on the island o St. Kitts and vigorouslypursue its economic diversifcation by emphasizing more viable

alternatives, such as tourism and nonsugar agriculture (e.g., feldcrops and livestock). Like other island governments, the majorchallenge o this situation is adopting careul planning that ensures

that the islands land resources previously utilized by these cropsare optimized or the long-term economic, social, and environ-mental sustainability o the country. With the assistance romthe UK Department or International Development (DFID),a land resource analysis was commissioned to identiy the mostsuitable land or the various nonsugar uses under consideration.The results were compared with the economic and social goals

o the governments transition plan to develop a strategy, area byarea, o preerred long-term land use.

This paper documents how geographic inormation technol-ogy (GIS) is utilized to perorm this land resource analysis. Themethod adopted and data used or perorming this analysis andfnal results are discussed. The discussion also highlights a ew

examples o other islands in the Caribbean and Asian/Pacifcregions with similar scenarios where these methods can be ap-plied. The paper concludes with a series o recommendations orurther work and improvements in other areas that relate to landmanagement issues in St. Kitts.

BAckGround oF St. kIttS

Location and Environmental CharacteristicsSt. Kitts (also called St. Christopher) is part of an independenttwin island federal state with the island of Nevis. ese islands arelocated in the northeast Caribbean Sea (see Figure 1). St. Kitts has

a land area of 168 km2. Soils throughout the island are extremelyfertile and have been used primarily for sugar production. Figure2 provides a general layout of St. Kitts.

Agricultural and Economic History

First colonized by the British in 1623, St. Kitts has been an im-portant sugar producer for 350 years. St. Kitts and Nevis achievedindependence in 1983 and currently are members of the BritishCommonwealth, the Organization of Eastern Caribbean States,and the Caribbean Community (CARICOM).

Sugar was the traditional mainstay o the St. Kitts economy

until the 1970s. Since then, the combination o improved inter-national connections at the airport and cruise dock have madetourism the islands main source o revenue. The governmenthas subsequently sought to nurture tourism with development

S. kis lan rso Anaysis

Edsel B. Daniel, Derek L. Bryant, James P. Dobbins, Ilis Watts, Alan P. Mills,and Mark D. Abkowitz

Abstract: Facing successive losses in the sugar industry and the imposition o a 39 percent price reduction in its primary export

market, the Government o St. Kitts and Nevis (GoSKN) decided to cease the production o sugar or export at the end o the

2005 production period. As part o the transition away rom sugar production, a land resource analysis project was undertaken

to complete a preliminary land-suitability analysis or proposed alternative activities. This evaluation included data collection

and environmental analysis showing the magnitude and location o areas suitable or alternative agriculture, ecological pres-

ervation, and commercial or industrial activity. Geographic inormation systems (GIS) provided suitable technology to enable

comprehensive environmental analysis and presentation o results. This project provided additional benets by serving as a pilot

project or demonstrating the value o GIS in Caribbean resource management and by building the oundation or a national

GIS. This paper presents project results and describes the utility o the analysis in the selection o preerred long-term land uses

and an overall sugar adaptation strategy or St. Kitts. It also highlights a ew examples o similar island nations in the Carib-

bean and Asian/Pacic regions under comparable economic circumstances where these methods can be applied.


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projects valued at more than $700 million (Douglas 2005). Thecontribution o the agriculture sector to real GDP declined rom15.6 percent in 1980 to 5.2 percent in 2004. Despite the thrusttoward tourism, however, agriculture is expected to play a vitalrole in the economic uture o St. Kitts.

St. Kitts and Nevis have a total cultivable land area o about22,000 acres, o which some 12,000 actually are cropped. Theclimatic conditions are suitable or a wide variety o crops, but

sugarcane occupies about 80 percent o the cropped area (seeFigure 3), despite its declining acreage since the early 1980s.

Agriculture and Land-use PoliciesAfter the closing of the sugar industry, the Physical PlanningDepartment (PPD) and the Department of Agriculture (DoA)have spearheaded the planning for the agricultural transition. iseort includes the preparation of a National Physical Develop-ment Plan (NPDP 2005), which spans the years 2005 to 2020and provides a blueprint that has been adopted for the future

development of the island of St. Kitts. e plan recommends poli-

cies, strategies, programs, and projects that can be implemented

to realize dened economic, social, and land-use goals at a sector,settlement, and national level (PPD 2005).

Several policies have been established by the developmentplan that aect the potential or postsugar agriculture, includ-ing:

The reservation o lands between the 500-oot to 1,000-ootcontour as priority areas or agricultural diversifcation andProvision o community grazing pastures or villages todistribute to individual herders.

The plan also proposes alternative economic activities andcorresponding land areas to be allotted to each activity (see Table

1) that must be considered in planning or postsugar develop-ment.

Table 1. Proposed Land Allocations According to the 2005 NPDP

Acres Purpose

1,250 Rum distillery and tourism center

5 Food-processing and packaging operations

5 Hydroponics operation

5,000 10 MW cogeneration of electricity, production ofethanol from cane juice and for animal feed

100 Small-scale food production

1,000 Vegetable cropping, etc., by commercial farmers

50 Peanut production

1,500 Beef cattle production

1,500 Small ruminant production

Methodologye methodology used for the land-suitability assessment isbased on the guidelines set forth by the Food and Agriculture

Organization of the United Nations (FAO 1976, 1983, 1985,

Figure 1. Location o St. Kitts

Figure 2. General Layout o St. Kitts

Figure 3. Lands under Sugar Production, 2005


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1991). ese guidelines have been widely used in determiningthe physical suitability of lands in support of land-use planning

and development of alternative land uses. Some examples ofstudies that employ this method include work by Kilic et al.(2005) and Ozcan et al. (2003) in Turkey, Kalogirou (2002) inGreece, Gaiser and Graef (2001) in Niger and Brazil, and Iguet al. (2000) in Benin.

The FAO method allows the user to determine the suit-ability o land parcels or potential land uses by rating a series oland quality and characteristic actors. Examples o these actorsinclude available soil nutrients, land slope, and the amount o

precipitation a land parcel receives. The actors incorporatedinto the evaluation are selected based on their relevance to thestudy area. These actors then are evaluated based on the require-ments or individual land uses (e.g., pineapple production versuslivestock grazing). This evaluation involves classiying each landparcel as highly suitable, moderately suitable, marginally suitable,or unsuitable or each actor in each land-use being evaluated. Theratings then are aggregated using a weighting system correspond-

ing to the relative importance o each actor to each land use.

This aggregation yields land-suitability scores or each potentialland use, which then can be used to create suitability maps orthe land area under consideration.

The potential land uses under consideration in this studywere taken rom recommendations made by CARDI (2005) andthe St. Kitts and Nevis DoA (2001, 2005) or postsugar agricul-ture. Because o insufcient data or the needs o the individualcrops under consideration, potential land uses were grouped

according to similar environmental requirements (see Table 2).These groupings were ormulated under the guidance o senior-level sta at the DoA (Jackson 2005, Stanley 2005).

In creating the list o actors or use in determining land

suitability or each land-use group, there was a need to balancethe inclusion o actors with data availability. For example, soilpH and nutrient availability are two actors commonly includedin land-suitability assessments. These data are not available or

soils in St. Kitts, however, and both actors had to be excludedrom the list. Further discussion on data availability is presented inthe Data Management section that ollows. The actors used inthis study are summarized in Table 3. These actors were verifedas being important to land-suitability assessments in St. Kitts bymeans o interviews with senior DoA ofcials and through feldreconnaissance o potential agriculture lands with members o thePPD and ormer senior sta o the St. Kitts Sugar Manuacturing

Corporation (SSMC). Note that because the amount o precipita-tion received by an area on St. Kitts is proportional to the areaselevation, variances in precipitation have been accounted or inthe evaluation actors or crop agriculture. The ocus o this study

was placed on rain-ed, rather than irrigated, agriculture basedon the absence o additional water resources, irrigation technol-ogy, and irrigation inrastructure in St. Kitts or the oreseeableuture (Thomas 2005).

Table 2. Evaluated Land-use Groups

Land-use Group Crops/Land Uses Included

Pineapples Pineapples

Field CropsDasheen, cassava, sweet potato,yam

Fruit Tree Crops

Sugar apple, custard apple, car-

ambola, guava, Indian jujube,wax apple

Vegetable CropsOnions, peanuts, cucumber,tomato, sweet/hot pepper, stringbeans

Pasture/Grass CropsGrass for feeding livestock (e.g.,guinea grass) or sugarcane

Livestock Production Beef, pork, or mutton (goats andsheep)

Table 3. Land Quality/Characteristic Factors Evaluated

Crop Agriculture Livestock Production1. Precipitation 1. Elevation

2. Soil Type 2. Flood Hazard

3. Elevation 3. Land Slope

4. Flood Hazard 4. Soil Erosion Hazard

5. Land Slope

5. Proximity to Water StorageFacilities

6. Soil Erosion Hazard6. Proximity to Residential

Areas

7. Wind Hazard

8. Soil (Ease of Mechaniza-tion/Cultivability)

To defne the critical levels or each actor listed in Table3 (i.e., what levels constitute highly suitable versus moderatelyversus unsuitable), surveys were conducted o senior-level staat the DoA. This approach allowed or the beneft o applyinglocal agriculture knowledge to the model. Similar surveys wereplanned or local armers and various agricultural personnel, but

were eliminated because o project time constraints. The surveys

asked participants to list values o each actor, or each land-use group, under the headings highly suitable, moderately

suitable, and unsuitable. The classifcation corresponding tomarginally suitable rom the FAO methodology was droppedrom the survey to decrease the burden o survey participants andbecause o difculty in obtaining meaningul and precise data ormore than three suitability classes. A second component o thesurveys was to determine the relative importance o each model

actor. The survey participants were asked to label each actor asvery important, moderately important, or less important indetermining the suitability o a land parcel or a given use.


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To aggregate actor ratings or each land-use group, the

results o the survey were transormed into quantitative values.Critical level ratings were given values o 2, 1, and 0 or highly

suitable, moderately suitable, and unsuitable, respectively.Relative importance results were transormed to values o 3, 2, and1, corresponding to very important, moderately important,or less important, respectively. The use o the 0-to-2 scale orcritical level ratings, as opposed to the 1-to-3 scale o relativeimportance, assured that actors rated as unsuitable or a given

land area would not artifcially increase the suitability score othat parcel.

The actor ratings were aggregated or each land parcel by

multiplying the actor rating by the actor importance and sum-

ming or all actors, as seen in the ollowing equation:

(1)

where Riis the critical actor rating o the ith actor and I

iis

its corresponding importance. The result o this aggregation island-suitability index values or each land parcel, which were usedto create suitability maps using GIS technology.

GIS was utilized or overlaying spatial data representing theactors listed in Table 3 to delineate the relative suitability o landparcels or each land-use group. This operation resulted in distinct

able 4. GIS Layers Utilized from the NGIS Laboratory

Layer name Description Source

Precipitation Polygon layer showing mean annual rain fall

distribution across the entire island; data missingfrom the data set was accounted for by means ofextrapolation

St. Kitts Water Department

Soil Type General soil classication PPD from soils maps that were created by Lang andCaroll (1966)

Soil Cultivatability Ease of mechanical land preparation accordingto amount of stones and boulders on site

PPD from soils maps that were created by Lang andCaroll (1966)

Elevation A contour layer with ten-meter intervals gener-ated from a 2002 aerial photograph; GIS pro-cessing tools were used to create a new polygon

contour layer with 100-foot (30-meter) ranges

PPD

Land Slope General slope in degrees from horizontal Derived from contour layer above

Flood Hazard Flood prone area determined according to localwater depth resulting from a 100-year returnperiod storm

Post-Georges Disaster Mitigation Project (PGDM),www.oas.org/pgdm

Inland Erosion Ha-zard

Composite erosion hazard classes including gul-lying and landslide/rock fall hazard of bare soilareas

PGDM, www.oas.org/pgdm

Wind Hazard Areas of storm wind hazards for a 100-year

return period

PGDM, www.oas.org/pgdm

Settlements Inhabited areas, predominantly the residentialagglomerations

PPD

Wells and Water

Storage

Drinking water source locations including allreservoirs, wells, and springs

St. Kitts Water Department


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land areas, each with its own land-suitability index value or eachmodel actor. Land areas in restricted zones, such as within 60eet (18 meters) o a ghaut or above the 1,000-oot (300-meter)contour, then were removed rom the layers. The remaining landareas were grouped according to an equal-interval classifcation otheir index values (i.e., the entire range o index values representedor each land use is broken into ranges o equal size rom mini-

mum to maximum) into categories o unsuitable, moderatelysuitable, suitable, and highly suitable.

dAtA mAnAGement

Primary GIS data collection took place at the National GIS(NGIS) Laboratory, PPD. A total of ten GIS data sets were used,each of which is briey described in Table 4. Other data sets withinformation recommended by the FAO but not available for thisstudy were soil pH, soil nutrients, and water salinity. Additionalpotentially useful GIS data sets to supplement the FAO method

would have been property boundaries (cadastral information),waterlines, reserved lands for major tourism and related future

developments (e.g., Basseterre Valley Aquifer Park), location andextent of existing farmlands, and water-table depth. ese data sets

were either unavailable or in a format that could not be utilizedby the project team. It also should be noted that the land resourceanalysis excluded the reserved areas of:

Settlements,The Southeast Peninsula (reserved or tourism develop-ment),

Forest reserve above 1,000 eet,

Brimstone Hill area (UNESCO World Heritage site), and

60-oot (18-meter) buer area around ghats.

reSultS

e total land area delineated as suitable and highly suitable forthe six agricultural land uses (pineapples, eld crops, fruit tree

crops, vegetable crops, pasture corps and livestock production) islisted in Table 5. An example suitability map for fruit tree cropsis shown in Figure 4.

Table 5. Total Land Area Rated as Suitable or Highly Suitable byLand Use Group.

Evaluated Land Use

Group

Suitable

Areas

Highly

Suitable

Areas otal

Pineapples 56.9 47.4 104.3

Field Crops 66.5 38.9 105.5

Fruit Tree Crops 27.9 75.4 103.3

Vegetable Crops 64.2 15.5 79.7Pasture Crops 38.3 38.6 76.9

Livestock Production 25.8 3.2 29.0

aAll values are given in km2.

Suitability results were compared with the proposed landuses under the National Physical Development Plan (PPD, 2005)(see Figures 5) and the DoAs (2005) proposed land uses or landsunder sugar cane. Example results rom this analysis, or ruit treecrops, are shown in Figures 6 and 7.

The objective o perorming these comparisons is not torecommend the quantity and location o specifc agricultural

land uses. The goal is to highlight how current proposals ornational land use (e.g., housing tourism, industrial, residential,

commercial, agriculture, etc.) compare with suitable lands oralternative non-sugar agriculture uses. This comparison identi-fes and quantifes these overlaps. Subsequent studies can thenutilize these results in a beneft-cost analysis to acilitate decisionmaking on competing land uses. A more elaborate discussion othis fnding is provided in the Discussion section below.

Figure 5. Proposed Land Use Defned by the 2005 NPDP (Source:PPD 2005).

Figure 4. Example Land Suitability Map or Fruit Tree Crops.


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dIScuSSIone land resource analysis results show that a wide variety ofagriculture is compatible with the environmental conditionspresent in St. Kitts. Land-use suitability indexes calculated foreach crop grouping are skewed toward higher, or more suitable,values. is result indicates that most land areas are capable ofsuccessfully producing each of the crops under consideration.

ere is a relatively wide envelope of area where the croplandscan be potentially developed. Fruit tree crops would be successfulthroughout the agriculture belt, between the mountain reserveand coastal hinterland, apart from between Cayon and Basseterre.Pasture crops would be most successful just below the mountainreserve land. Vegetable and eld crops have a narrower band than

fruit crops but could still be widely distributed throughout theisland. Pineapple crops are similarly suited to growth over largeareas of the island.

Land in St. Kitts appears to be slightly less suitable or theproduction o livestock. Table 5 indicates that there is less landcategorized as suitable or highly suitable or livestock than thereis or the other evaluated land uses. The primary reason or thislower compatibility is that animal production acilities (e.g.,grazing area, eed lots, etc.), unlike crops, have restrictions ontheir placement within a given proximity to residential areas. Inthis analysis, at the recommendation o senior-level DoA sta

(Stanley 2005), a buer o a quarter mile was used to separate

livestock grounds rom populated areas or reasons o aestheticsand human health. Most o the area classifed as moderately suit-able or unsuitable occurs around settlements such as the capitalcity o Basseterre or on steeply sloping lands near the 1,000-oot(300-meter) contour land development limit.

Comparing the results o the suitability analysis with PPD/DoA-proposed land uses shows tree crops to be the most suitablecrop type or ormer sugar lands. The land-suitability map or

pasture crops shows that the areas most suitable or this type o

agriculture are at elevations that may not support bee production.

The sloping lands will compromise the quality o the bee by mak-ing the livestock too muscular. Such lands would be more suitable

or dairy production. An important observation is the high levelo agreement between the suitable livestock lands and the DoAsproposed livestock assignments or sugar lands. This agreementdemonstrates a level o consistency between the land-suitabilitymodeling results and GoSKNs proposed land uses or the sugarlands, serving as a rough validation o these results.

The comparison o suitable lands and proposed land usesillustrate some o the basic types o analyses that can be perormed

with the results produced under this project. However, the poten-

tial or using these layers in agricultural and economic planning

is ully realized when they are incorporated into the various plansor economic development, with regards to the transition romsugar agriculture and the more general national economy. Usingeconomic indicators, and by setting average yield amounts orhighly suitable and suitable categories, the area o land neededto make an enterprise viable can be calculated. This would aidland-use decision making by presenting alternatives complete with

projected fnancial results. Using the maps presented here, deci-sion makers can identiy preerred areas o cultivation or livestockproduction, strategically zone areas, and assist in identiying large-scale enterprise locations and/or smallholder plots or communityarming. The GIS layers produced by this study and the data theycontain could be urther utilized in economic planning by usingthem in concert with additional data layers, such as transportationinrastructure and processing-plant locations to determine the costo transporting goods. Such analyses would allow or analysis o

a wide range o costs and environmental impacts.While this study was ocused on the island o St. Kitts, it

provides a template or broader and generic application in otherisland states throughout and beyond the Caribbean with similaragriculture or land-use diversifcation eorts. In the Caribbean,the island states o Dominica, St. Lucia, and St. Vincent had simi-

Figure 6. Example Results o Intersection Analysis between FutureLand Use Areas and Suitable Areas or Fruit Tree Crops.

Figure 7. Example Results o Intersection Analysis between SugarLand Areas and Suitable Areas or Fruit Tree Crops.


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lar economies based primarily on a single crop (i.e., bananas). LikeSt. Kitts, these islands have experienced declining EU preerentialmarkets, orcing a restructuring o their economies. For example,the government o St. Vincent and the Grenadines (SVG) hasbeen making attempts to diversiy its economy by reviving theproduction o arrowroot and expanding the amount o landarea under root crops. This plan or agricultural diversifcation

is complicated, however, by inormal or squatter settlements inwhich residents illegally occupy publicly owned land.

In 2005, the government o SVG (2009) estimated thenumber o squatters to be 16,000, large enough to threaten the

welare o the islands orest reserve and watershed managementsystem. Consequently, in addition to the undamental issues oland access and arability, shits in agricultural policy must accountor the location o illegal settlements, the actors that drive them,

and potential impacts to the islands already endangered naturalresources. In an eort to address these central issues, SVG iscommitted to make arable land available to landless armers andintroduce appropriate land management policy SVG (2009).

Other islands around the world ace similar challenges o

diversifcation away rom monoculture. For example, in the In-dian Ocean, Mauritius has depended strongly on sugar as a crop.

Although at this time, it still invests and relies heavily on the bulksugar industry, it, too, has been conronting issues o diversifca-

tion and allocation o resources (Julien 1998, GMR 2006). InAsia, changing land use resulting rom new trade regimes guidedby regional and international agencies (e.g., WTO) presents itselin states in which agricultural production now encompasses theexpansion o crops such as ruits, tree nuts, and vegetables (Singh2001, GML 2006). Like St. Kitts and the previously mentionedCaribbean island states, these kinds o expansion will requirethe identifcation o potential land areas and evaluation o the

suitability o these lands to maximize production o these crops.For example, in its quest or economic diversity, the governmento Malaysia (2006) has seen agricultural land use increased rom5.9 to 6.4 million hectares during the period o 20002005. Thisincrease was largely because o the expansion o production inoil palm, coconuts, vegetables, and ruits. Despite this drive orincreased agricultural production, however, a total o 163,000hectares o agricultural land remained idle because o absenteeor aging landowners and difculties in consolidating native and

customary land units. It is the governments goal and expectationto increase agricultural land use at an average rate o 1.5 percentby maximizing the yield and allocating these lands or expanding

oil palm cultivation; large-scale production and precision armingsystems will be implemented in new production zones or ruits,vegetables, and livestock (GML 2006).

Another trend in the Asian region that presents similar land-allocation issues is the development o urban and peri-urbanagriculture (UPA), which oers an alternative or achieving ood

security (FFTC 2006). Rapid population growth in this region,

exceeding 3.5 percent in some Asian countries, and the accom-panying urban development and industrialization is projected toresult in a decline in suitable agricultural land availability (Gu-nasena 2001). To combat diminishing availability o traditionalarmland, a number o Asian island countries have begun experi-menting with UPA. The Japanese experience with UPA revealsthat about 1.1 million hectares o armland exist in urban-like

areas and are producing about $10 billion worth o agriculturalproducts or 29 percent o the national gross agricultural outputs(Tsubota 2007). To sustain and increase these benefts, Japan,like St. Kitts, aces the challenge o identiying arable land andresolving land-use conicts. In Japanese urban areas, land is ascarce and valuable commodity and the decision to allocate theseareas to agricultural production rather than other socioeconomicuses (e.g., tourism, housing) is extremely complex. According to

Tsubota (2007), Japan struggles with zoning and land-planningissues that require addressing competing interest or scarce landresources. He urther notes that other countries pursuing UPA,such as the Philippines, Indonesia, Vietnam, and Nepal, also areacing similar problems.

These brie examples o current global agricultural trendshighlight scenarios o competing agriculture and socioeconomicinterests or scarce land resources similar to those aced in St.Kitts. With adequate land characteristics and GIS resources, it is

possible or these island states to adopt and apply the methodsdemonstrated in this study to guide decisions and solutions that

will address these issues. The key is or these island states todevelop the necessary GIS capabilities and gather the necessaryspatial data (e.g., soil, rainall, etc). Land and planning authoritiesin these islands are taking the lead by implementing a variety oGIS-based projects or making GIS a major part o their activitiesand decision making. For example, the Physical Planning Depart-

ment in St. Vincent, with support rom the EU, is implementinga National Land Inormation Management project that is targetedat land titling, land registration, land-use planning, agriculturalzoning, state land management, and land valuation and taxation.Central activities in this project include GIS training or keygovernment sta, utilizing GIS or mapping agricultural landto determine potential land use, developing local area land-usemaps to regularize and manage the available lands, and revamp-ing the administration/registration o land titles (SVG 2009). In

Malaysia, Samat (2006) notes that land-use allocation still is beingconducted in a rather ad hoc manner, oten on the basis o theknowledge o a ew decision makers and local planners. However,

he also revealed that GIS is becoming a useul tool or land-useplanning in Malaysia. The Ministry o Housing and Lands inMauritius also have utilized GIS to optimize the identifcation oquality lands or sugar agriculture (Jhoty 1998) and currently areimplementing a Land Administration and Management Systemthat will modernize land administration and management systems

on the island (GMR 2009).


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concluSIonS And

recommendAtIonSBy combining GIS and scientic criteria setting with local expert

judgment, a viable method for delineating areas of successful

nonsugar agriculture was produced using existing information.While specic areas have been quantied based on suitability, anadded project benet has been the ability to map these locations

across the island. ese maps also support the comparison ofland resource analysis results with the focus areas identied inthe NPDP and the DoA strategic plan for postsugar productionin the former sugar lands. Such evaluations provide useful inputto the process for determining lands where specic agricultural

uses could be allocated.While care has been taken to use the most up-to-date and

accurate maps, the dynamic nature o the natural and built en-vironments suggests that changes may have occurred since thedigital source data were captured (e.g., ood events changing ghatsor construction o new housing developments). Also, the valuesused or environmental parameters are rom published sources,

but the dynamics o climate, and the summary resolution o thedata, inevitably means that microtemporal and spatial variations

have not been mapped. The time available to do the analysis inthis report was restricted, precluding the collection o quantitativeenvironmental data rom the feld.

With additional time and resources,a sensitivity analysiscould be perormed to enhance confdence in the results. In ad-dition, more detailed data relating to, or example, soil types, canbe included to enhance the quality o the analysis. Also, urtherrefnements o the criteria used and comparison with other eco-nomic, social, jurisdictional, and environmental inormation can

help to more precisely quantiy conditions o suitability.

Another potential refnement to the analysis involves thedefnition o reserve areas (e.g., ghats and residential areas, tourismdevelopment areas, and mountain reserves). There may be otherareas that the government would preer not to be considered inthe land resource analysis that were not specifcally addressed bythe governmental agencies involved in this study (e.g., runwayapproach, coastal protection, near industrial areas, new develop-

ments such as White Gate Development and Basseterre ValleyAquier Park). Given the parameters, these areas could easily beintegrated into an enhanced study.

In terms o environmental monitoring, more time to gatherand update inormation rom scientifc studies and key stake-holders on the island would improve the analysis and refne theresults or decision making. In particular, an updated soil survey(identiying soil type, pH, chemistry, and conductivity) would bequite benefcial. A study o existing water resources would be a

useul addition as well, covering current agricultural, residential,industrial, sewage, and tourist consumption, along with require-ments or conserving the natural environment. Such a studyshould include the logistical and economic viability o irrigationor producing specialist, high-yield, high-value crops. Finally,modeling o key habitats o St. Kitts auna and ora, identiy-

ing the location and nature o historical and cultural sites, andlandscape analysis (e.g., to conserve the aesthetics o the naturaland cultural environments) are three additional areas o data thatcould be incorporated to ensure a more holistic analysis. Oncethe physical parameters have been identifed, the results can beintegrated with human and economic inormation, such as landownership, settlement, and strategic planning zones, to refne

the mapped areas. With or without any o these refnements, theprotocol defned in this paper is a useul template or comparingphysical parameters or cropping to defne suitable areas and pro-vide a vital inormation eed into strategic government decisionmaking and planning.

About the Authors

Edsel B. Daniel, Ph.D., GISP, is a national o St. Kitts andNevis where he served as a Planning Ofcer in the Ministry

o Sustainable Development. He is a Research AssistantProessor in the Department o Civil and EnvironmentalEngineering at Vanderbilt University. He is currentlyinvolved in and completed several GIS projects or U.S.ederal and state agencies and developing countries in theCaribbean and South Atlantic regions.

Corresponding Address:Department o Civil & Environmental EngineeringVanderbilt UniversityBox 1831, Station B

Nashville, TN 37235Phone: (615)-322-3459Fax: (615)[email protected]

Derek L. Bryant, Ph.D., P.G., participated in this research asa doctoral candidate in the environmental managementprogram at Vanderbilt University. He is currently a consul-tant with Visual Risk Technologies, Inc., where his interestsinclude assessing risks associated with the transportation o

hazardous materials and evacuation planning.

Corresponding Address:Visual Risk Technologies, Inc.210 25th Ave. N.Suite 1015Nashville, TN 37203Phone: (615) 321-4848

Fax: (615) [email protected]

James P. Dobbins, Ph.D., is a Research Assistant Proessor inthe Department o Civil and Environmental Engineering

at Vanderbilt University. His research interests includemarine transportation, geographic inormation systems(GIS), intermodal reight transportation modeling and riskmanagement.


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Corresponding Address:Department o Civil & Environmental EngineeringVanderbilt UniversityBox 1831, Station BNashville, TN 37235Phone: (615)-322-3459Fax: (615)-322-0430

[email protected] T. Watts, M.Sc. is a consultant agronomist in her native St.

Kitts. She is particularly interested in issues pertaining toood security, agriculture and how international trade poli-cies have inuenced agricultural development o small islandstates in recent years.

Corresponding Address:#43 Conaree FlatsConareeSt. Kitts

Phone: (869) 466-7505Cell: (869) 664-4547

[email protected] P. Mills, M.Sc., is a reelance geographer and GIS consul-tant based in Kent, UK. He is primarily interested in thedevelopment and use o GIS and inormation managementor small island developing states in the Caribbean, South

Atlantic and Indian Ocean.

Corresponding Address:110 Bow Rd

Wateringbury, KentUnited KingdomPhone: (44) 1634 [email protected]

Mark D. Abkowitz, Ph.D., holds an appointment as Proessor oCivil & Environmental Engineering at Vanderbilt University,and serves as Director o the Vanderbilt Center or Environ-mental Management Studies. Dr. Abkowitz specializes in

managing risk management, hazardous materials transporta-tion, and applications o advanced inormation technologies.He is the author o Operational Risk Management - A CaseStudy Approach to Eective Planning and Response, recentlypublished by John Wiley & Sons.

Corresponding Address:Department o Civil & Environmental EngineeringVanderbilt University

Box 1831, Station BNashville, TN 37235Phone: (615) 343-3436

Fax: (615)[email protected]

Acknowledgments

e authors wish to express their gratitude to the UK Departmentfor International Development (DFID) for funding that madethis research possible, and to the following people who helped invarious roles to ensure the successful completion of this project:Hilary Hazel, Shirley Skeritt, Kimberly Tucker, DoA sta (Jerome

omas, omas Jackson, Allistair Edwards, Ashton Stanley),PPD sta (Ellis Hazel, Patrick Williams, Quincy Alexander,Graeme Brown), Cromwell omas, Sugar Transition Ocesta (Gordon Alert, Keith Phillip), SSMC sta (Osbert Martin,

Hyrum Williams, Euclin Clarke Walters, Sam Baley, Alton Bass),Conrad Kelly, Cassandra Benjamin, Errol Rawlins, Janey Smith,Hugh Grandsta, Gotz Gaschutt, and Harry Shutt.

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Department o Agriculture (DoA), St. Kitts. 2005. Strategic plan20052009. Basseterre, St. Kitts: Government o St. Kittsand Nevis.

Douglas, D. L. 2006. Prime Minister o the Federation o St. Kitts

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URISA Journal Huang and Hawley 21

BAckGroundIn 1969, approximately half of all students walked or bicycled toschools. But now, less than 15 percent of children do so; morethan half of the students arrive at schools by private automobiles(FHWA). Problems accompanying this change include childhoodobesity, trac congestion, air pollution, and pedestrian safetyissues. (NHTSA 2004, Frank et al. 2005, Lopez et al. 2006,

Hurvitz 2005, Crawford 2006, McMillan 2005, 2007). To addressthese issues, the Congress passed federal legislation to establisha National Safe Routes to School Program (SRTS) in 2005.e SRTS program is administered and guided by the FederalHighway Administration (FHWA) of the U.S. Department ofTransportation (USDOT). e FHWA recommends that SRTS

eorts in the United States incorporate, directly or indirectly, theve components, often referred to as the ve Es: engineering,education, enforcement, encouragement, and evaluation.

Inormation about walking and bicycling acility conditionso neighborhoods around schools is key to the implementationo the fve Es. For example, urban planners and public healthauthorities need the inormation to assess neighborhood walkingand bicycling saety conditions, transportation engineers needthe inormation or roadway and intersection improvement, lawenorcement ofcers need the inormation to respond to unsaeactors, law makers need the inormation to initiate new policies,

parents need the inormation to understand their neighborhood

saety and security conditions, and children also may need theinormation to guide their walking and bicycling activities.

Walking and bicycling saety data collection and assessmenthave been conducted by various interested parties such as urbanplanners, transportation engineers, and public health adminis-trators. A signifcant trend in such data collection is to provideenvironment attribute inormation to planners and to evaluatenew environmental and policy initiatives (Sallis et al. 1998, Ewing

et al. 2003, Frank and Engelke 2001, Leslie et al. 2007). For ex-

A daa mo an Inn GIS Fawo fo

Saf ros o Shoo

Ruihong Huang and Dawn Hawley

Abstract: Sae Routes to School projects are government and public participation eorts that require a variety o data on walking andbicycling saety and security measures o the environment. Urban planners, transportation engineers, and public health researchers

have developed a host o walkability/bikeability indexes. However, sae route to school-oriented data specications, storage solutions,

evaluation methods, and inormation distribution mechanisms are not available. This paper proposes a GIS data model and an

Internet GIS ramework to satisy these needs. The data model supports convenient storage and retrieval o diversied walking/

bicycling saety-related data and acilitates the development o various saety indexes. The Internet GIS ramework provides a series

o Web-based unctions such as walkability/bikeability evaluation, sae routeoriented network analysis, data communication, and

Web mapping to satisy the inormation needs o all users. The GIS data model and Internet GIS ramework are implemented in

a Sae Routes to School inormation system or the Sechrist Elementary School in Flagsta, Arizona.

ample, Schlossberg et al. (2006) use street networks around transit

stops and schools to quantitatively analyze local walkability andprovide useul planning and evaluation tools or transportation

planners interested in enhancing the local walkable environment.However, a good deal o existing pedestrian saety data collec-tion activities are orientated to an adult walking environment(McMillan 2007, Schlossberg et al. 2007). For instance, Leslieet al. (2007) measure eatures o the built environment that mayinuence adults physical activities and develop indexes o walk-ability at the local level. GIS technology has been used in somedata collection activities to obtain spatial measures o urban orm,transportation acilities, and resource accessibility (Schlossberg et

al. 2007, Leslie et al. 2007).

Transportation engineers ocus on individual transportationacilities at restricted locations. For example, a transportationproject targeted at improving a specifc street intersection or asegment o sidewalk surace may collect data in the geometry,trafc ow, pedestrians, and accidents at the construction sitebeore and ater the implementation o engineering measure-ments. Walking and bicycling saety checklists oten are used or

such project-specifc data collection.While walking and bicycling saety data collection is a com-

mon practice or urban planning and transportation engineeringprojects, similar activities dedicated to SRTS are rarely seen inliterature. Because most o the current data collection practices arenot school-trip oriented, direct participants o SRTS programs,including children, parents, and schools, are not involved, andtheir concerns are not reected. To date, there are no standards orspecifcations to guide comprehensive data collection or SRTS.

Given that SRTS is a widely embracing public participating e-ort involving participants rom a wide range o areas, includingschools, parents, children, planners, engineers, public health or-ganizations, and law enorcement institutions, keeping everybodyinormed is essential to the success o an SRTS program.

An Internet (or Web-based) geographic inormation system


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(GIS) has the potential to satisy the broad inormation needsor SRTS. This paper presents a data model or a GIS databaseand a ramework or Internet GIS applications that satisy SRTSdata collection, evaluation, analysis, and distribution. An SRTSdatabase can support convenient storage o diversifed walkingand bicycling saety measures and acilitates evaluation o walk-ability and bikeability conditions. Built on the GIS database,

Internet GIS provides advanced online inormation services suchas collection and dissemination o walking and bicycling saetydata as well as sae route planning. It also provides a means ocommunication between dierent parties involved in an SRTSproject. An Internet GIS, thereore, can serve as a platorm on

which every party can play a role in SRTS.

WAlkABIlIty And BIkeABIlIty

IndIcAtorS

Supposedly, good urban form can lead to a reduction of totaltransportation costs and automobile usage, resulting in more

livable communities (e Victoria Transportation Policy Insti-tute 2007). McMillan (2005, 2007) maintains that urban formis a primary factor aecting childrens travel behavior to school.Schlossberg et al. (2006) not only believe that urban form is afactor that aects students transportation modes but also sug-gest that it can help predict school travel modes. Furthermore,Schlossberg (2007) proposed a series of urban form measuresbased on TIGER les in a GIS. ese urban form measures fallinto three categories containing a total of 13 measures: quality

(e.g., minor road density, minor/major road ratio), proximity (e.g.,pedestrian catchment area, impeded pedestrian catchment area),and connectivity (e.g., intersection density, dead-end density). In

studying general walkability of local communities, Leslie et al.(2007) propose a walkability index of Census Collection District(CCD) based on four environmental attributes: dwelling density,connectivity (using road centerline and intersection data), land-use accessibility and diversity of uses (entropy of land-use mix),

and net area retail (shopping centers). ey also argue the impor-tance of objective measures of walkability factors in urban areas.McMillan (2007), however, pays more attention to perceptualaspects of urban forms and safety by surveying caregivers for theirperceptions of a number of variables, including neighborhoodsafety, trac safety, household transportation options, sociocul-tural norms, attitudes, and sociodemographics. Although land use

was regarded an important factor of neighborhood walkability in

the study of Leslie et al., it is excluded from considerations forschool trips by other researchers because the school is the onlydestination (McMillan 2007, Schlossberg 2007).

Transportation engineers are more interested in saetyconditions o transportation acilities, especially roadways andintersections, and they have proposed a host o indexes or

walking and bicycling saety. Examples o these indexes includePedestrian Level o Service (PLOS) (Sarkar 1993, Dixon 1995,Gallin 2001, Chu and Baltes 2001, Balts and Chu 2002), measure

o pedestrian environments (Khisty 1994), pedestrian environ-ment actor model (1000 Friends o Oregon 1993), pedestrianpotential index and defciency index (Portland Pedestrian MasterPlan, City o Portland 1998), Level o Service (LOS) (Botma1995), Bicycle Saety Index Rating (BSIR) (Davis 1987), roadwaycondition index (RCI), Bicyclist Stress Level (Sorton and Walsh1994), Intersection Hazard Score (IHS) (Landis 1994), Bicycle

Level o Service (BLOS) (Landis, et al. 1997), Bicycle Compat-ibility Index (BCI) (Harkey et al. 1998), intersection BLOS(Landis et al. 1997), Compatibility o Roads or Cyclists (CRC)(Noel et al. 2003). Some o these indexes ocus on roadways andothers emphasize intersections. Indexes usually are calculated asthe weighted sum o a number o objective or subjective saetyactors:

.......................... (1)

where Iis walkability or bikeability index,xiis the measure

o the i-th saety actor, and wiis the weight o the i-th actor. A

actor usually is measured on a scale o 0 to 4 or 5. For example,

Khisty (1994) proposed seven qualitati

urisa journal volume 21 no.1 2009

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