Prioritization of coastal properties for conservation in New York State

Prioritization of coastal properties for conservationin New York State

Yuri Gorokhovich & Andrei Voustianiouk

Received: 12 August 2009 /Accepted: 17 November 2009 /Published online: 22 December 2009# Springer Science+Business Media B.V. 2009

Abstract Conservation of coastal lands reduces non-pointsource pollution loads into oceans and estuaries, retainsnatural areas and saves ecological communities fromdisappearance and change. A recent agreement for protec-tion of Long Island Sound waters in New York andConnecticut established 30 environmental and managementgoals. One of them is establishment of a listing of existingundeveloped properties and their prioritization for naturalresource conservation and outdoor recreation. The optimalprioritization approach poses strong constraints and meth-odological challenges on selection of data for analysis,assignment of a priority score to each property unit and theassessment of this assignment. To be a practical tool, theprioritization model should be reproducible and include amechanism for evaluation of obtained prioritization scenar-ios. Presented study uses Geographic Information System(GIS) to assign conservation priority scores to unprotectedand undeveloped parcels greater than five acres in sizewithin New York’s Long Island Sound coastal area. Themethod combines spatial multi-criteria analysis and statis-tical methods. The results of this project include identifica-tion and prioritization of more than 700 undevelopedproperties on New York coast. The most important finding

of GIS analysis was the discovery of clusters of vacantparcels that together form large areas available for futureconservation. These results offer new conservation toolsand strategies to coastal managers and government in NewYork State.

Keywords GIS . Coastal lands . Conservation .

Prioritization .Modeling

Introduction

On December 4, 2002, the Long Island Sound Study (LISS)Policy Committee executed the Long Island Sound 2003(LIS2003) Agreement, which established 30 environmentaland management goals with specific targets extending overa five-year period. LIS2003 commitments related to coastalconservation included establishment of a coordinatedstrategy for developing a Long Island Sound StewardshipSystem to promote conservation of open space, landscapesand ecosystems, improve access to the Sound, establish alisting of existing undeveloped properties and furtherprioritize property types for natural resource conservationand natural resource-based outdoor recreation.

The conservation of open vacant space within the NewYork’s coastal area of the Long Island Sound refers tounprotected and undeveloped (i.e., vacant) land parcelsgreater than five acres in size. The evaluation criteriaproposed by LIS2003 include proximity to waterfront,protected open space, Long Island Sound Stewardshipareas, tidal wetlands, freshwater wetlands, publicly ownedland, and hydrologic features. Selected land parcels shouldbe the most significant ones that warrant conservation. Thisapproach is common in coastal management practicesby governments (Cronk 2005) and requires Geographic

Y. Gorokhovich (*)Department of Environmental,Geographic and Geological Sciences, Lehman College,Gillet Hall 315,Bronx, NY 10468, USAe-mail: [email protected]

A. VoustianioukNew York University,530 1st Ave, Suite 9 Q,New York, NY 10016, USAe-mail: [email protected]

J Coast Conserv (2010) 14:41–51DOI 10.1007/s11852-009-0081-8

Information System (GIS) to collect and analyze adminis-trative, real estate and environmental spatial data.

For the past 10 years coastal counties of New Yorkacquired and made available to public land parcel data withinformation on their size, ownership, land class, location, etc.These data are available via the Internet free of charge(Westchester County), by purchase (Bronx and QueensCounties), or through special agreement and license (Nassauand Suffolk Counties). Federal agencies, such as UnitedStates Geological Survey (USGS) and National Oceano-graphic and Atmospheric Agency (NOAA), enable publicaccess to extensive collections of geographic spatial data onhydrology, terrain and imagery via their web sites and web-based mapping interfaces. Integration of land parcel infor-mation and geographic data provided the basis for GISanalysis that included estimations of land parcel areas andtheir proximities to waterfront, protected open space, LongIsland Sound Stewardship areas, tidal wetlands, freshwaterwetlands, publicly owned land, roads, and hydrologicfeatures. Combinations of measured areas, proximities andweights assigned to data categories created a spatial matrixfor evaluation of the prioritization scoring system.

One way to deal with prioritization of heterogeneousgeographic data is to use quantitative multi-criteria model-ing with weights assigned to normalized spatial data onproximities and areas. Multiplication of each normalizedvalue by its assigned weight produces a unique numericscore that can be used in prioritization scheme. Moredetailed statistical and methodological information of multi-criteria GIS analysis is contained in Malczewski (2004,1999). Examples of application of this methodology to landmanagement practices can be found in Phua and Minowa(2005), Geneletti (2004), Malczewski et al. (2003).

Multi-criteria GIS analysis helps to reconcile differencesin physical nature of data (e.g. wetlands are different fromwatercourses, tidal wetlands are different from freshwaterwetlands, etc.) and evaluate them in numerical way tocalculate a score according to the conservation values ofundeveloped land parcels. Similar principles were appliedin development of vulnerability and sensitivity indexes incoastal studies by Thieler and Hammar-Klose (1999), Shawet al. (1998) and Gornitz et al. (1994), and in groundwaterstudies by Aller et al. (1985).

The proposed methodology uses multi-criteria GIS ap-proach combined with decision support system based onevaluation of multiple management scenarios. GIS datacategories and geographic parameters receive differentweights depending on their importance in the decisionmakingprocess. For example, roads as GIS category can beinterpreted in management terms as “access” or “publicaccess”; water bodies and watercourses can be linked to theidea of “natural preservation”; parcels area can be interpretedas “price”, etc. These semantic interpretations served as a

basis for assigning weights to various GIS data categories indevelopment of a scoring system for selected 744 vacant landparcels on the coast of New York State. For example, if weneed to make a decision regarding public access to conserva-tion sites, road category would receive the highest weight.

Spatial analysis of highly distributed data, such as landparcels, often reveals spatial patterns, defined as contiguousareas with common characteristics. The present study wasundertaken with a goal of selecting specific land parcels forconservation. Therefore, the study’s aim was to find themost appropriate land parcels, not their aggregates. Afterland parcel data were collected and analyzed with GIS, theyrevealed existence of contiguous areas (clusters) formed byvacant land parcels suitable for conservation. This findingoffers coastal managers and New York state officials a newalternative (i.e., focusing on conserving vacant area clustersrather than separate vacant parcels), which is probably morebeneficial for conservation purposes in terms of long-termconservation planning.

Area of study

The geographic extent of the study included coastal zone ofNew York metropolitan area and the northern shore of LongIsland. Administrative areas of study included five counties(from north to south) of Westchester, Bronx, Queens,Nassau and Suffolk (Fig. 1) that share their coastline withLong Island Sound (LIS) and have extensive populations(Table 1). The most populated county is Queens and theleast populated one is Westchester. The largest area isoccupied by the Suffolk County and the smallest one by theBronx. Population density in this coastal area is highest inthe Bronx and Westchester.

The coastal area boundary in this study was adaptedfrom LISS (2003). According to the report, this boundarywas based on climatological and topographical features, andon political jurisdictions. In New York, the project boundaryfollows the Harbor Hill moraine through Queens, Nassau, andSuffolk Counties. The western extent of the project boundaryis the Triboro Bridge span that crosses the East River fromQueens to the Bronx. The project boundary in the Bronx andWestchester Counties was drawn to follow a portion of theBronx River and the Hutchinson River Parkway.

Methodology

The analytical framework in this study consisted of threemain components:

1. Preparation of input data for spatial analysis with GISand determination of proximities from vacant parcels

42 Y. Gorokhovich, A. Voustianiouk

(in this study called primary GIS datasets) to roads,coastline, tidal wetlands, streams, public lands, fresh-water bodies and designated stewardship areas in otherGIS datasets (in this study called secondary GISdatasets). Areas of the vacant parcels were considereda separate attribute in analysis, in addition to measuredproximities.

2. Normalization of data for use in calculation of thepriority scores, which was accomplished by creating aspreadsheet with information about each vacant parcel(identified by a unique ID number), including its areaand calculated proximities to spatial features in thesecondary GIS datasets, i.e. the shortest distance fromthe vacant parcel to the feature in a secondary GISdataset. These proximities and areas were normalized toreconcile differences in ranges and units of measure-

ments (proximity units are meters while area units aresquare meters).

3. Examination of various conservation scenarios anddetermination of corresponding priority scores for eachvacant parcel with the help of custom-written decision-making software.

GIS data compilation

Primary data for analysis included land parcels (alsoknown as tax maps) from five coastal counties. Release ofthese data for public use is still hampered by the proceduralor administrative restrictions and policies. The most generalportals for this data are the New York State GIS Clearing-house and the New York State Office of Real PropertyServices. However, data for both portals are provided by the

Table 1 Geographic characteristics of NY counties within the area of study

Westchester Bronx Queens Nassau Suffolk

Population, (US Census, 2007) 951,325 1,373,659 2,270,338 1,306,533 1,453,229

Coastal population 233,174 546,056 497,007 182,929 222,304

Coastal population (%) 24.5 39.7 21.8 14.0 15.2

Total area (sq. miles) 478 49 127 326 1162

Total number of properties 52,645 58,591 82,505 296,700a 94,223

Number of vacant properties 3,162 5,600 10,475 1,1832a 8,603

Number of vacant properties with area ≥5 acres 34 35 24 113 538

a Data for Nassau County were taken from non-spatial database and refer to all properties, including properties outside the coastal zone defined inthis study

Fig. 1 Study area and coastalboundary used in analysis

Prioritization of coastal properties for conservation in New York State 43

counties and are often incomplete or outdated. Therefore, forthis project data were obtained directly from the counties. Inaddition, The Nature Conservancy (TNC), North Shore LandAlliance (NSLA), and the Regional Planning Association(RPA) also provided data sets from their current projectsrelated to coastal lands conservation.

Parcel data in New York State have information onproperty class coding that consists of three digits. Vacantland is coded as 300, therefore vacant residential will havecodes 311, 312, etc., vacant rural 321, 322 and 323, etc. Inthis analysis class codes between 300 and 400 were used tocreate datasets with only the vacant land categoriesdesignated by New York State as:

300—VACANT LAND310—Residential311—Residential Vacant Land312—Residential Land Including a Small Improvement(not used for living accommodations)314—Rural Vacant Lots of 10 Acres or Less315—Underwater Vacant Land320—Rural321—Abandoned Agricultural Land322—Residential Vacant Land Over 10 Acres323—Other Rural Vacant Lands330—Vacant Land Located in Commercial Areas331—Commercial Vacant Land with Minor Improvements340—Vacant Land Located in Industrial Areas341—Industrial Vacant Land with Minor Improvements350—Urban Renewal or Slum Clearance380—Public Utility Vacant Land

The main errors in land parcels data include: 1. Parcelswith Property Class Code=0, missing property class value,outdated or occurring several times within one parcel area;2. Unrealistic (either too large or too small) areas of parcels.The process of checking parcel data attributes was based onuse of orthophoto imagery from 2004, available from theNew York State GIS Clearinghouse. This imagery helpedidentifying both, truly vacant property and also developedproperty which was mistakenly identified as vacant.

In addition to land parcels, underwater vacant lands werefound in the Bronx, Queens and Suffolk Counties (Fig. 2).According to New York City Department of Planning, someof these parcels were created with expectation that theywould be eventually filled in or developed, which wasnot uncommon until the 1970’s. Others were created inrelationship to commercial piers/marinas. For example, onCity Island there was an underwater land grant where thecommercial marina is located. In the past, in some munici-palities, developers with land that was partially under waterwere able to count this as "open space" in meeting variousdevelopment requirements regarding the ways they coulddevelop the property.

Stevenson (1914) mentioned an official document from1894 that stated that “… charter of the City of New Yorkgrants to the city land under water embraced by projectedstreets in public use or which may hereafter be opened forpublic use. … It has also been claimed that certain colonialcharters vested the title of lands under water within theirboundaries in the towns or cities to which they weregranted to the exclusion of any title in the state. Thus it hasbeen claimed that under the charters of Brookhaven,

Fig. 2 Underwater property(green outline) in the Bronx


Hempstead, Huntington, Gravesend, Flatlands, Flatbushand Bushwick, the lands under water in Port JeffersonBay, Hempstead Harbor, Huntington Harbor, Gravesend,Sheepshead and Jamaica Bays and Bushwick Inlet weregranted to the towns.”

Secondary data for the analysis included the followingcategories:

1. Public lands2. Stewardship areas3. Coastline4. Hydrologic network (streams)5. Hydrologic freshwater features (lakes, ponds)6. Main roads7. Tidal wetlands

These data were collected from the different federal,state and local (TNC, RPA, NSA) sources. Part of thesecondary data consisted also of aerial photography/orthophotography in color (infrared and visible spectrum)available from NYS GIS Clearinghouse. Two image data-sets were used: New York State high resolution digitalorthoimagery at 0.5–2 ft resolution (available for 2000,2004 and 2007) and 1 m statewide digital orthoimagery(available for 1994 – 1999). High resolution aerialphotography for New York City boroughs within LIS wasobtained from the Center of International Earth ScienceInformation Network (CIESIN).

GIS analysis and estimation of prioritization score

For vacant land prioritization, two main data attributes wereused as selection criteria: size of vacant parcels and theirproximity to natural features and local infrastructure (i.e.,roads). Therefore, results of spatial analysis included areaof each vacant property and the distances from the propertyto each of the nearest natural features and local infrastruc-ture elements. Both areas and distances were normalized tocreate a uniform scheme applied in the scoring system.Areas were normalized by the maximum value (i.e., the

larger the area of the vacant parcel, the higher itsconservation value and the higher the final score of thisparcel) and distances were normalized by the minimumvalue (i.e., the shorter the distance from the vacant parcel toany of the spatial features in the secondary GIS datasets, thehigher its conservation value and the higher the final scoreof this parcel).

The final score was computed as the sum of products ofeach normalized value and the weight assigned to thespecific spatial feature this value represented. Assignmentof weights is part of the decision making process that placesan arbitrarily level of importance on each secondary GISdataset. By assigning different weights to different spatialfeature attributes associated with the secondary GIS data-sets, coastal managers can change the final priority scorefor each vacant parcel. Implementation of this flexible toolwas achieved by creating a custom-written decision makinginterface.

Since many land parcel data contained errors in areadefinition, all areas of the selected vacant lands were re-calculated using GIS. Area values were then exported as aseparate field to a spreadsheet for subsequent normalizationand use in calculating prioritization scores. The distancebetween GIS features was defined as the shortest pathbetween the closest spatial elements of data. Thus, thedistance between a polygon (e.g., a vacant parcel) and aline (e.g., a coastline) was calculated by finding closestpoints in the outline of the polygon representing vacantparcel and on the line representing coastline.

Following collection and verification of land properties,data arrived for analysis in a spreadsheet format (Table 2).For each parcel, the table contained parcel identificationnumber (“Parcel ID”), area of the parcel (“Area, acres”),and distances from the parcel to the relevant landmarks,elements of landscape, structures, and areas of interest.

To enable meaningful comparison of specific contribu-tions by different criteria to the decision regarding parcelpriority, data within each criterion were normalized andtransformed to a linear scale with the minimal score of 0

Table 2 Example of data subset for multi-criteria analysis prior to normalization

Parcel ID Area, acres Distance tocoastline,meters

Distance toWetlands,meters

Distance tostreams, meters

Distance towaterbodies,meters

Distance to publiclands, meters

Distance tostewardshipsites, meters

Distance toroads, meters

1 5 392 325 2954 1808 4778 13781 2235

2 74 2010 5217 7105 4045 6487 19823 5806

3 68 7751 2559 1 3295 5150 12698 100

4 114 1 385 2923 2364 2843 12295 337

5 1255 10 385 2923 2364 2843 12295 337

… … … … … … … … …


and maximal of 1. Taking into account that parcel areashave positive effect on the parcel’s priority (i.e., the largerthe area of the parcel the better it is for conservationpurposes) while proximity to natural features (e.g., coast-line, lakes, wetlands, etc.) has negative effect (i.e., thesmaller the distance to the natural feature, the better it is forconservation purposes), area data were normalized bymaximum and distance data, by minimum:

Normalization by maximum: XNormalized ¼ XOriginal=

Max XOriginal

� �

Normalization by minimum: XNormalized ¼ Min XOriginal

� �=

XOriginal

For example, after GIS analysis area values were foundto be within a range of 5–1255 acres. Since areas arenormalized by maximum, a parcel with the smallest area(5 acres) will receive a normalized value of 5/1255=0.0040, while a parcel with the largest area (1255 acres)will receive a normalized value of 1255/1255=1. There-fore, while selecting priorities, a parcel with the largest areawill receive the highest score. In the same analysis,distances from parcels to coastline were found to be withina range of 1–7751 meters. Since distances are normalizedby minimum, a parcel with the shortest distance (1 m) willreceive a normalized value of 1/1=1, while a parcel withthe longest distance (7751 m) will receive a normalizedvalue of 1/7751=0.0001. Therefore, while selecting prior-ities, a parcel with the shortest distance will receive thehighest score. As a result, parcels with greater area andcloser proximity to the relevant elements of landscapewould receive higher normalized scores. An example ofnormalized data is shown in Table 3.

Decision support system and prioritization of coastalparcels for conservation

Following normalization, the analyst working with the datais presented with the interactive selection program bymeans of which different weights can be assigned to thespecific criteria and, thus, various decision making scenar-ios can be modeled and examined. In the present study,

eight decision criteria were used for parcel prioritization;however, the selection program can accommodate anynumber of decision criteria, depending on the needs andspecifics of the analysis.

Assignment of weights to spatial features of thesecondary GIS datasets from which proximities to vacantparcels were determined allowed creating various decisionmaking scenarios. For example, in a scenario that considersproximity to coastline the most important factor, themanager would assign the highest weight to normalizedproximity to coastline and lesser weights to normalizedproximities to other natural features.

Once the criteria weights have been selected, thedecision score is computed for each parcel:

Score=Σ (Criterionj * Weightj),where j represents a numeric index of each criterion(spatial feature)

Parcels are sorted according to priority on the basis ofthe decision score (higher decision score indicates higherpriority) and the prioritized list is displayed. If needed forsubsequent use, this list can be stored as a file with thedesired degree of precision in the decision score (Table 4).Each scenario receives a numeric code that consists ofweights assigned to the parcel area, parcel’s proximities tothe secondary data (i.e. streams, wetlands, roads, etc.).Parcel ID represents a unique internal code assigned to eachparcel.

All steps of the analysis up to this point (original datahandling and normalization, decision scores calculation,parcel prioritization, and storage of prioritized parcellistings) are handled automatically by the custom-writtensoftware. An analyst controls the flow of analysis byselecting weights to be applied to the specific criteria.

Prioritization of parcels according to a single specificweight scenario represents an end in itself and, therefore, nofurther analysis is required or possible. In the event ananalyst desires to examine changing impact on prioritizationby various criteria as a result of different weight assignmentsand to investigate different scenarios, a stability analysis iscalled for.

Table 3 Example of data subset for multi-criteria analysis after normalization

Parcel ID Area Distance tocoastline

Distance towetlands

Distance tostreams

Distance towaterbodies

Distance topublic lands

Distance tostewardship sites

Distanceto roads

1 0.0040 0.0026 0.0003 0.0104 0.0004 0.0003 0.0002 0.0002

2 0.0590 0.0005 0.0002 0.0002 0.0003 0.0002 0.0015 0.0002

3 0.0542 0.0001 0.0002 0.0025 0.0084 0.0006 0.0007 0.0005

4 0.0908 1.0000 0.0003 0.0004 0.0011 0.0007 0.0031 0.0007

5 1.0000 0.1000 0.0152 0.0041 0.0027 0.0072 0.0082 0.0004

… … … … … … … … …


Stability analysis

Stability analysis identifies parcels that are most likely tomeet the priority cut-off criteria despite variations in theweights assigned to the specific categories. In most cases,an analyst would collect prioritization lists resulting fromextreme scenarios of weight assignment as the basis forsubsequent stability analysis. The number of scenariosincluded in the analysis depends on the analyst’s prefer-ence, although greater number of included scenarios willobviously enhance the validity of analysis. The secondanalyst-selectable variable is the priority cut-off: thenumber of the top priority parcels that will be consideredfor conservation.

Stability analysis can be conducted on the basis offrequency with which a parcel gets into the cut-off list(Method 1), position a parcel occupies in that list (Method 2),or probability matrix which combines the frequency andposition components (Method 3). These methods use standard

statistical functions related to frequency, position and proba-bility and can be used as stand-alone tools.

As an illustration, here are the results of analysis with acontrol and eight extreme weight scenarios tested and atop cut-off number set at 10, i.e. top 10 parcels will beconsidered for conservation. Nine prioritization lists willproduce a parcel matrix shown in Table 5. In the controlscenario, all criteria used in estimation of the finalprioritization score receive the same (lowest) weight. Inthe extreme weight scenarios, each criterion is sequentiallyassigned the highest weight while all the other criteriareceive the lowest. This approach allows to investigatestability of the prioritization sequence under differentweight scenarios, i.e., under changing priorities of themanagement.

The total number of parcels in the matrix may vary,in this case, from 10 (if all top choices are the sameregardless of the selected scenario) to 80 (8 times 10, ifevery scenario produces a completely different list of topchoices). Subjecting this matrix to a frequency analysis(Method 1) reveals that some parcels made it to the “top10” list more than half the time, making them goodcandidates for preservation (Table 6).

Analysis of the placement in the “top 10” (Method 2),however, produces a somewhat different list of prioritizedparcels. In this example (Table 7), the cumulative placementscore was computed as the inverse sum of places (i.e., 10points for each 1st place, 9 points for each 2nd and so forth,1 point for each 10th, and no points for any place after that).

The probability analysis (Method 3), arguably the mostaccurate approach that takes into account both the frequencyof making the cut-off list and the position in that list,considerably decreases the number of stable parcels. Theprobability score is computed as a product of the inverse sumof places and relative frequency of making the cut-off listexpressed as the percentage (Table 8).

Table 4 Example of the prioritization process outcome

Scenario: 24112553a

Precision: 3

Parcel ID Priority Score

9 1 19.009

302 2 17.084

170 3 16.091

24 4 16.076

76 5 14.105

… … …

a Scenario code (assigned weights): Parcel Area=2; Proximities to:Coastline=4; Wetlands=1; Streams=1; Waterbodies=2; PublicLands=5; Stewardship Sites=5; Roads=3

Table 5 Results of prioritization under different weight scenarios to be used in stability analysis. Scenario code sequence represents weightsassigned to Parcel Area and Proximities to Coastline, Wetlands, Streams, Waterbodies, Public Lands, Stewardship Sites, and Roads

Priority ↓ Scenario → 11111111 81111111 18111111 11811111 11181111 11118111 11111811 11111181 11111118

1 302 9 9 302 302 302 302 302 302

2 118 279 282 118 118 118 118 9 118

3 9 282 281 282 282 164 9 24 9

4 282 30 291 281 281 240 282 279 105

5 281 302 242 291 291 243 281 280 98

6 291 281 24 242 242 30 291 76 204

7 242 273 164 164 164 33 242 172 104

8 24 291 240 240 240 22 24 86 99

9 164 33 243 243 243 21 106 118 103

10 240 118 106 106 106 26 76 282 100


Decision support system and management scenarios

The decision making process included eight different GISdatasets: roads, coastline, tidal wetland, streams, publiclands, freshwater bodies, stewardship areas and areas ofvacant parcels. Therefore weight values could range fromone to eight. For example, the decision to prioritize vacantlands on the basis of areas of vacant parcels and theirproximities to the coastline would result in assigning weightvalue of 8 to the normalized proximities to the coastline andareas of vacant parcels. Other attributes containing normal-ized proximities from vacant parcels to these features wouldreceive lower weight values, for example, 2 or 3. Differencesin the assigned weight values will ultimately producedifferences in the final priority scores. This techniqueallowed prioritization of vacant parcels according to variouscharacteristics of the surrounding natural features or roads.

This study introduced nine different scenarios to helpcoastal managers in decision-making process. Table 9shows the relationship between the nine scenarios and

the weights assigned to each criterion. All weights wereselected arbitrarily to introduce the difference betweencriteria in calculation of the final score. Therefore, the finalscore should reflect the role of each criterion and differ-ences between assigned weights in the results of analysis.The designed scenarios should help coastal managers toidentify the relationship between the criteria used andconservation goals.

Results

The total number of properties, number of vacant propertiesand number of vacant properties with areas equal to orgreater than five acres is shown for each county in Table 1.GIS-based multi-criteria analysis identified 744 vacantparcels within New York State coastal zone that areavailable for conservation. All parcels received priorityscores according to each of the nine scenarios used foranalysis. Using internal identification numbers of priori-tized vacant parcels coastal managers can evaluate andassess them in terms of future conservation. Because of theconfidential character of the database with undevelopedcoastal parcels we cannot identify specific parcels in thispaper.

The consequent analysis of maps and visualization ofidentified undeveloped properties revealed that manyproperties form contiguous or semi-contiguous (e.g., parcellots divided by roads) patterns or clusters (Fig. 3). Theseclusters were created in GIS by an aggregation process thatincluded dissolving boundaries between identified undevel-oped parcels. This was done by using a specific spatialthreshold between adjacent parcel areas. In this case, weused a maximum threshold of 300 ft. This distance wasselected after visual inspection of clusters and identificationof the maximum distance separating parcels and associatedwith widths of roads, highways and water courses. Thus,122 clusters of undeveloped parcels were identified in thearea of study. The same prioritization and stability analysismethod that was applied to individual vacant parcels alsowas applied to the identified clusters.

Table 6 Stability analysis of data in Table 5 using frequencydistribution (Method 1), top choices

Parcel ID Number of appearancesamong the first 10

Frequency, %

118 8 89 %

302 8 89 %

282 7 78 %

9 6 67 %

281 6 67 %

291 6 67 %

24 5 56 %

164 5 56 %

240 5 56 %

242 5 56 %

Table 8 Stability analysis of data in Table 5 using probabilityapproach (Method 3), top choices

Parcel ID (Inverse sum)×(Frequency %)

302 68

118 51

282 37

9 37

281 26

291 21

Table 7 Stability analysis of data in Table 5 using placementdistribution (Method 2), top choices

Parcel ID Inverse sum of places (1st=10, 10th=1)

302 76

118 57

9 53

282 48

281 39

291 32

242 24

24 23

164 21


Discussion

GIS and multi-criteria methodology

The combination of GIS and multi-criteria analysis pro-vides a powerful tool for selecting land parcels forconservation. However, the results of any analysis are onlyas good as the quality of the data used. As Openshaw(1989) noted, “errors and uncertainty are facts of life in allinformation systems”. In the present study, the GIS analysiswas dependent on the quality of land parcel data (taxmaps). Errors associated with this database arise fromdifferent management of land records by counties andvarious GIS technical abilities of towns supplying landrecord data to counties. Therefore, prior to analysis allparcel data were verified using digital orthophotographyprovided by New York counties on periodical (4–5 years)basis. This required time-consuming verification of landrecords via visualization of the overlay of land parcelboundaries with digital orthophotos.

Secondary data used in analysis (streams, water bodies,roads, wetlands, coastline, public lands and stewardshipareas) were found to be at approximately the same scale1:24,000. Exceptions are public and stewardship lands thathave larger scale since their boundaries were obtained fromsurveyed land records. The consistency of scale minimizederrors and uncertainty in measuring proximities from landparcels to secondary data.

Multi-criteria analysis in this study used the most direct,linear approach to normalization of distances and areas. Thealternative would be to use a non-linear (e.g., exponential)method that would create different proximity values andtheir ranges. In the context of this study, it was impossibleto determine the mathematical function that would be bestin relating proximity of secondary data to potentialconservation lands with their conservation values. Thiswould require a special study that was outside of the scopeof the present project.

One of the difficulties in applying results of multi-criteria modeling in the decision process lies with the initialselection of land parcels that are most pertinent to thedecision goals. This difficulty arises from the arbitrary andsubjective assignment of weights by the coastal manager.On a practical level, weight assignment is a rather subjectiveprocess and it cannot be fully justified without a thoroughand expensive scientific analysis. Most public agencies donot have capabilities to perform this kind of analysis orresources to hire a contractor. Therefore, weights are oftenassigned according to values cited in literature or in anarbitrary manner. One of the possible solutions to thisproblem is to evaluate final results by examining multiplemanagement scenarios with different weights assigned toall data categories from high to low in a sequence. Thisapproach highlights parcels that receive the highest scoremore frequently or appear more often among the selectedrange of parcels under different management scenarios.

Table 9 Developed scenarios for prioritization of undeveloped parcels using various weights assigned to spatial variables

Scenario →Priority ↓

Influence bysize (proxyfor the price)

Proximityto naturalfeatures

Coastalproximity

Protectionby thegovernment

Influence bytransport(accessibility)

Protectinglarge areasclose to thecoast

Consideringmostly naturalfeatures and size

Influence bywaterbodies

Consideringrecreationalactivities

Area 8 3 1 1 1 8 8 4 8

Coastline 1 3 8 1 1 8 4 8 8

Wetlands 1 8 1 1 1 3 4 8 3

Streams 1 8 1 1 1 3 4 4 3

Waterbodies 1 8 1 1 1 3 4 8 3

Public Land 1 3 1 8 8 3 1 3 8

StewardshipSites

1 3 1 8 1 3 1 3 8

Roads 1 3 1 1 8 3 1 3 8

Fig. 3 An example of cluster pattern


Another possible problem may arise when managersfrom different agencies target the efforts at potentiallyconflicting aims. This can cause inconsistencies in theway they assign weight values. In this case, a pairwisecomparison approach based on the multi-criteria analysismethod may represent a valuable and fairly inexpensivesolution (Feick and Hall 2002; Saaty 1980). This concerndoes not immediately apply to our work, however, since allmanagers involved in selecting lands most valuable forconservation purposes pursue the same goal.

Conservation goals and land selection process

Purchase of land by the government, transfer orpurchase of development rights are examples of methodsthat can lead to protection of parcels with characteristicsthe public finds valuable, such as historical resources orecologically sensitive features (Kleppel et al. 2006). Thelimited funding available to most public agencies forcescoastal managers to consider purchases of stand-aloneparcels with specific criteria. This approach requiresprioritization of separate parcels of land to provide coastalmanagers with a method of site selection according to theperceived threat to the property’s resources (such asproposed development), prioritization scores, and dollarvalue, yet not necessarily according to habitat or biodi-versity value.

Unless the area of preserved parcel is large enough, it ishardly possible to ensure that selected parcel of land willcontribute to conservation of representative biodiversityand habitat, unless extensive, on-the-ground field inves-tigations are conducted. As Margules and Pressey (2000)noted, “The practice of conservation planning has generallynot been systematic and new reserves have often beenlocated in places that do not contribute to the represen-tation of biodiversity.” According to Soulé (1987), con-servation goals should ensure biodiversity, maintenanceof natural processes and survival of species. These goalscan be achieved by preserving large areas, sufficientenough for large animals, protecting variety of habitatsand mitigating negative effects from human settlements(Miller and Hobbs 2002).

Early studies (Forman et al. 1976; Galli et. al. 1976)showed species-area dependence and correlation betweenthe number of species (trees and birds) and the size ofpatches. Forman (1997) indicated that larger patches havemore species than smaller patches and much larger areas arerequired to sustain a species number over time. One of hisexamples is that fires or pest outbreaks will affect only partof the large area, allowing repopulation of the disturbedpatch from within the conservation patch.

Conclusions

The methodology presented in this study serves only as anadditional tool for coastal managers involved in conservation.The main advantages of the presented methodology are:

1. Repeatability (consistency) of the method, and itsreproducibility by any GIS skilled technical personnelwith use of only conventional tools.

2. Strict criteria and consistent scoring system, i.e.,calculation of the priority score is based on a simplemathematical formula;

3. Statistical verification of results;4. Method can be fully programmed in GIS and used as a

stand-alone tool for coastal management.

The main disadvantage of this methodology is thesubjectivity of scenarios chosen for parcel prioritization.This disadvantage affects the relative selection of parcels,while the advantages provide consistency and utility ofapplied methodology. Thus, the results of the prioritizationanalysis presented here should be used in combination withother considerations that might be outside the GIS andstatistical methodology. These results should be interpretedas aids to either existing or new decision processes.

The presented method will help coastal managers drawattention to specific vacant parcels and, moreover, explainwhy a particular parcel received a high priority score (and,therefore, deserves attention). It can also help in situationswhen coastal conservation priorities or management criteriachange, as it permits running new decision-making scenariosand receiving results in a consistent manner.

Taking into account conservation goals in relation tobiodiversity and sustainability of habitats, the selection ofundeveloped parcel clusters would be a more beneficialstrategy than acquisition of stand-alone land properties,even with high prioritization score. Use of land clusters forconservation would require much higher funding alloca-tions. However, there are two major benefits contributing tothe long-term success of coastal conservation using landclusters:

1. Large areas are more efficient for achieving conserva-tion goals, as they allow wider biodiversity and havehigher potential for habitat migration and survival;

2. Large areas can help in long-term governmental planningand development of future environmental policies, as wellas in community planning and management.

Special attention and interest should be brought to theunderwater properties located along the northern shoreof Long Island. These areas can potentially be used foraquaculture or water-related recreational activities.


Acknowledgements This project was supported by the grant fromNew England Interstate Water Pollution Control Commission(NEIWPCC) through a Cooperative Agreement between NEIWPCCand the U.S. Environmental Protection Agency. Ms Karen Chytalo(NYSDEC) and Ms. Louis Harrison provided invaluable help insteering this project and technical expertise. The North Shore LandAlliance, The Nature Conservancy and the Regional PlanningAssociation provided some GIS data for analysis. Ms. Ana Hiraldoand Mr. Sam Wear from Westchester Planning Department providedinvaluable help with Westchester County parcel data verification andanalysis. The Center for International Earth Science InformationNetwork of Columbia University assisted with Bronx and Queens dataacquisition.

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Prioritization of coastal properties for conservation in New York State