A GIS Analysis to Evaluate Areas Suitable for Crushed1

8/19/2019 A GIS Analysis to Evaluate Areas Suitable for Crushed1

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144 Robinson, Kapo, and Raines

and population parameters. The results of this regional analysis can identify areas for moredetailed evaluation. As transportation or population features change, the model can be revisedeasily to reflect these changes.

KEY WORDS: Aggregate resources, Weights of Evidence (WofE), Weighted Logistic Regression

(WLR).

INTRODUCTION

Natural aggregate consists of stone crushed froma variety of hard-rock deposit types and sand andgravel mined from alluvial deposits. Aggregate isused extensively in asphalt pavement, cement con-crete, and structural fill in construction activities todevelop, maintain, modify, and improve roads, build-ings, and other infrastructure in urban and devel-oping areas (Tepordei, 2001a; Langer, 1988; Langerand Glanzman, 1993). Aggregate must meet a va-

riety of engineering and quality specifications thatare defined by standardized tests (ASTM, 2003a,2003b; Barksdale, 2000), and the high-quality sourcerocks and gravels that meet these specifications, al-though generally abundant regionally, are limited orrestricted in many local areas (Langer and Knepper,1995).

Aggregate is a high-bulk, low unit-value, highplace-value (Bates, 1969) mineral commodity whosecosttotheenduserisinfluencedstronglyby thecost of transporting processed aggregate from the mine siteto the construction site (Poulin, Pakalnis, and Sinding,1994). Lower quality aggregate production sites that

are closer to the consumer can be more competitivethan higher quality aggregate produced from moredistant sites (Poulin, Pakalnis, and Sinding, 1994).Groups of aggregate producers and end users linkedby transportation corridors generally develop exclu-sive market areas for aggregate within a geographicregion (Poulin and Bildeau, 1993; Joseph and others,1987; Fakundiny, 1980). The dispersed locations of ag-gregate production sites are a complex function of ge-ologic, marketplace, and land use factors. Importantvariables include:

(1) Variation in the distribution and quality at-

tributes of the geologic source materials thatcan be used for crushed stone aggregate,

(2) Variation in location of demand for aggre-gate,

(3) Variation in the availability of land with high-quality aggregate source materials because of preemptive land development and restrictivezoning (Kuff, 1984),

(4) Variation in transportation methods, costs,and aggregate haul distances (Poulin,Pakalnis, and Sinding, 1994), and

(5) The relative success rate in developing newproduction sites and re-permitting existingproduction sitesfor aggregate (Weaver,1995;Stanley, Marlow, and Harris, 2000; Langer,2002).

The increasing demand for aggregate, growingat an average rate of more than 2% per year in theUnited States (Tepordei, 2001a), and the dif ficulty

of developing and permitting new sites of aggregateproduction (Stanley, Marlow, and Harris, 2000) indi-cates that aggregate will need to be supplied fromsources yet to be developed or delineated in manyareas (Tepordei, 2001a). The delineation of prospec-tive source areas for aggregate involves evaluation ofboth geologic factors that relate to aggregate qualityand transportation and socioeconomic factors that re-late to the economic viability of the industry (Stanley,Marlow, and Harris, 2000).

This paper explores the use of a quantitative andreproducible data-driven Geographic-Information-

System (GIS) technique to measure spatial relationsbetween existing crushed stone aggregate quarry sites,geology, transportation networks, and population dis-tribution. The GIS technique quickly develops a pre-dictive model based on these regional spatial rela-tions. The model defines the general areas most likelyto be of interest to the aggregate industry as sites forcrushed stone production, and to the land manage-ment community for planning and zoning evaluationrelated to new permit applications and existing per-mit renewals for aggregate production. The derivedprobabilities can best be considered a relative rankingof the degree of suitability for production of crushed

stone aggregate in the area studied. The suitable areasidentified by theregional model then can be evaluatedfurther on a site-specific basis using more detailed in-formation on rock properties and economic analysis(Marlow, Stanley, and Hudman, 2001). As transporta-tion and population features change because of plan-ning or actual development, the model can be revisedeasily to reflect these changes.


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Areas Suitable for Aggregate Quarries in New England 145

Two data-driven methods, weights of evidence(WofE; Bonham-Carter, 1994) and weighted logisticregression (WLR; Agterberg and others, 1993), areused to model the distribution of crushed stone aggre-gate quarry sites relative to geologic units, transporta-tion networks, and population density. The Arc-SDM

extension for Arcview (Kemp and others, 2001) im-plemented these methods used in this study.

The WofE analysis approach is a quantitativemethod using evidence to test a hypothesis. The re-sults of this analysis can be used to describe and ex-plore relations in spatial data from diverse sources,make predictive models, andprovide support fordeci-sion makers. The WofE analysis method was adaptedto GIS for mineral-potential mapping by Bonham-Carter, Agterberg, and Wright (1988) and Agterberg,Bonham-Carter, and Wright (1990), and the methodis summarized in Raines, Bonham-Carter, and Kemp(2000). The method tests the hypothesis that an area

is suitable for the occurrence of a mineral depositsite, defined by a set of response variable point lo-cations (termed training sites), relative to a set of pre-dictor variables (termed evidence). In the mineral-resource-potential mapping example presented here,the training points are the set of locations of activecrushed stone quarries and the predictive evidenceconsists of geologic, transportation network, andpopulation distribution spatial data. The evidentialthemes may have categorical values (e.g., the classesof geologic units or rock types from maps), or or-dered values (e.g., distance to linear and other spatial

objects).For each binary evidential theme, a pair of weights is calculated relative to the training sites, onefor presence of the evidence criterion (w+) and onefor absence of the evidence criterion (w−). Multi-class evidential themes are associated with multipleweights, with one weight for each class. The magni-tude of the weights depends on the measured spa-tial association between the evidence criteria and thetraining sites (crushed stone quarries)in thearea stud-ied. The arithmetic difference between the binaryweights, termed contrast , is a measure of this asso-ciation. Uncertainties in the weights can be used to

measure the certainty that the contrast is not zero(Bonham-Carter, 1994). This measure is termed confidence. The weights then are used to estimate theprobability that an area contains a mineral deposit,based on the presence or absence of evidence crite-ria. The response theme is the posterior probabilitythat a unit area contains a training point. Uncertain-ties resulting from variances of weights and missing

data allow the relative uncertainty in posterior prob-ability to be estimated and mapped.

WofE analysis was used to analyze spatial asso-ciations among the training sites relative to the multi-ple evidence categories and to reclassify the evidencecategories into binary or multiclass groups for opti-

mal prediction. Weighted logistic regression (WLR)is used to combine mathematically the optimized evi-dence map patterns (Agterberg, 1989; Agterberg andothers, 1993; Agterberg, Bonham-Carter, and Wright,1990) to predict the distribution of quarry sites. TheWLR method avoids bias that may be present in theWofEmethod caused by combining evidence datasetsthat are spatially related (conditional dependence;Agterberg and others, 1993).

The model posterior probabilities derived fromWLR can best be considered a relative ranking of the degree of suitability for crushed stone aggregatequarry development in the area studied. The resulting

“suitability theme” is based on the posterior probabil-ity that a unit area contains a training site quarry. Thehigh ranking areas delineated by the regional modelsare considered suitable forsite occurrence,and can beevaluated further on a site specific basis using moredetailed information on rock properties and marketconditions.

SOURCES OF DATA

Four types of spatial data are used in the GIS-

based model approach to define the areas that aremost likely to produce crushed stone aggregate fromboth newly developed and re-permitted quarries. Thesites of knownactive crushed stone quarries were usedas training sites to develop the model.The locationsof current crushed stone quarries were identified usinginformation in Tepordei (2001b) and Mine Safety andHealth Administration (MSHA) permit records forNew England. The sites were characterized further byMSHA permit history into three categories: (1) newquarries with permits issued after 1990, (2) existingquarries with permits renewed and new permit num-bers issued after 1990, and (3) existing sites with per-

mit numbers issued before 1990. All sites were usedto calibrate the model. Data on new and permit re-newal sites were used to evaluate the model results bypermit status category and modify the model to betterapply to the development of new quarry sites.

Three evidential theme layers were used: (1)bedrock map units with appropriate quality attributes(Langer and Knepper, 1995) to be used for aggregate


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Figure 1. Crushed stone aggregate quarry sites in relation to bedrock geology units generally suitable for aggregate in New England.

(Fig. 1), (2) proximity to principal highways and rail-road lines (Fig. 2), and (3) categorical groups definedby census tract population density (Fig. 3). The lo-cations of current and past crushed stone quarries(McFaul and others,2000; Tepordei, 2001b) were usedto identify the geologic map units that are most suit-able for use as aggregate (Fig. 1).

A compilation of 1:500,000 and 1:250,000 scalestate bedrock geologic maps (Doll and others, 1961;Hermes, Gromet, and Murray, 1994; Lyons and oth-

ers, 1997; Osberg, Hussey, and Boone, 1985; Rodgers,1985; Zen and others, 1983) were used for predictiveevidence for New England geology at the regionallevel. These maps are similar in type and scale to theregional geologic map information available for otherareas. More detailed geologic map information, whichis not available currently in digital format, is prefer-able because some geologic units that are suitable for

aggregate are not delineated at the scale of the statebedrock map. The 1:500,000 and 1:250,000-scale geo-logic maps used in this analysis were compiled on basemaps with an estimated 250- and 125-m spatial reso-lution (Longley and others, 2001). The geology, whichwas compiled by inspection, generally has a spatial ac-curacy as good as 1 km, although the mismatch of unitcontacts across state boundaries indicates that spatialuncertainty in some areas may exceed 2 km.

The proximity to the transportation network was

categorized into groups at distance intervals of 1.6 km(1 mile), which is greater than the estimated spa-tial resolution of the evidential data layers used inthe analysis. The spatial resolution of the NationalHighway Planning Network database and the U.S.Census Bureau census tract database are estimatedas 100 and 500 m, respectively (Longley and others,2001).


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Figure 2. Surface transportation features (major highways and railroad networks) and crushed stone aggregate quarry sites inNew England.

Spatial population density information (peopleper square mile) by U.S. census tract, used for pre-dictive evidence, was obtained from the U.S. Cen-sus Bureau (2000), with a scale of approximately1:500,000 (Fig. 2). The National Highway PlanningNetwork database provided spatial information forthe interstates and major highway features of the

study area at a scale of 1:100,000 (Fig. 3) that wereused for predictive evidence. The highway network iscomposed of rural arterials, urban principal arterials,and all National Highway Systemroutes (U.S. Depart-ment of Transportation, 2002). The railroad networkdata is composed of the national railway system re-ported by the Federal Railroad Administration (U.S.Department of Transportation, 2002).

All evidential theme layers were prepared ingrid format using Arcview 3.2 and Arc-SDM exten-sion (Kemp and others, 2001). Each grid has a cellsize of 100 m, which is less than the minimum spa-tial uncertainty of the evidential theme source data.The small cell size was selected to minimize clas-sification error caused during the grid generation

process.

ANALYSIS OF EVIDENTIAL THEME LAYERS

WofE analysis was used to evaluate the spatialassociations among the training sites and predictive


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Figure 3. Census-tract level population density and crushed stone aggregate quarry sites in New England.

evidential theme categories, and to reclassify theevidential theme layers for optimal prediction(Bonham-Carter, 1994; Raines, Bonham-Carter, andKemp, 2000). The base model assumption in theWofE analysis is that current aggregate market-place conditions defining proximity to construction

markets, proximity to transportation corridors toef ficiently transport aggregate, and lithologic rocktypes suitable for aggregate production will con-tinue to be relevant in the future. This assumptionwas evaluated using permit history data iden-tifying new quarries and existing quarries withsite permits renewed after 1990 based on MSHArecords.

Bedrock Lithology

The bedrock geology map units with generallithology characteristics highly suitable for aggre-gate (Langer and Knepper, 1995) and evidence ofcurrent (Tepordei, 2001b) and past (McFaul and

others, 2000) production of crushed stone aggregateare listed in Table 1. Some of the units in this table arelisted because they contain localized subunits, such ascarbonate rock or marble layers, which are not por-trayed at the state map scale but which are suitablefor aggregate and have been utilized for aggregateproduction in the past. Many other metamorphic andgranitic rock types that occur in mapunits that are not


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Table 1. Rock Units on State Bedrock Geologic Maps That Are

Suitable for Aggregate and Have Been Utilized to ProduceCrushed Stone Aggregate in New England, by Lithology groupa

State Bedrock map unit symbols Lithology group

CT C-sb, OC-s Carbonate rocks

C-wb, Og, Pp, Pw, Ygr, Zsgg,

Zsph, Zwr

Granitic rocks

Ob, Ohb, Om, Omo, Oq Other Mafic rocksJb, Jha, Jho Jurassic basalt

De, Otfg, Ow, SOh Other Rock typesMA C-sc, Osg Carbonate rocks

Dcgr, SOcgr, Zdgr, Zdngr,Zfgr, Zhg, Zpgr, Zsg

Granitic rocks

DSdi, DZl, Oa, Ohg, OZm,Ssqd, Zb, Zdi, Zdigb, Zv

Other Mafic rocks

Jd, Jdb, Jhb Jurassic basaltC-Zds, De, Opf, Ow, Pp, Pr, Pw,

Pwv, PzZc, SOvh, St, Ygg

Metamorphic rocks

ME Dsdl, Ozsk, Ssal, Ssl, Swl, Zil Carbonate rocks

C1, C1b(m), D1, K1a Granitic rocksOw, Ozc, OZev Other Mafic rocks

OC-s, Om, Oq, OZm, SOv Metamorphic rocksNH D1m, Db2b, Dc1m, OZrb,

PM1m

Granitic rocks

De9, Oal, Oalx Other Mafic rocks

Oq Metamorphic rocksRI Dsg, Zeg, Zseg, Zsgg, Zwr Granitic rocks

DZgd, Zbu Other Mafic rocksPnbpu, Zbg Metamorphic rocks

VT C-cs, C-d, C-w, Ob, Ocw, Ohg,Omic, Os

Carbonate rocks

nhu Granitic rocksOa, OC-sg Other Mafic rocks

Oal, pC Metamorphic rocks

a Bedrock map unit symbols are from the published state bedrock

geologic maps (Doll and others, 1961; Hermes, Gromet, andMurray, 1994;Lyonsand others, 1997;Osberg, Hussey, and Boone,

1985; Rodgers, 1985; Zen and others, 1983).

listed in Table 1 can be crushed to produce aggregate.Some have been used to produce aggregate in limitedamounts. However, the general rock characteristics of these mapunitsare less suitable foraggregatethan themap units listed in Table 1. To develop the preliminaryset of evidential theme layers for WofE analysis andoptimization, the bedrock map units were grouped

into lithology categories (Table 1) The source lithol-ogy types for active quarries identified by the quarryoperatorsarecategorizedinTable2.Thesourcelithol-ogy types for active quarries indicated by geologicmap information are summarized in Table 3. Thedistribution of these bedrock map units is shown inFigure 1, with the lithology types grouped into threecategories.

Table 2. Comparison of Crushed Stone Lithology Type Reported

by Aggregate Producer (Tepordei, 2001b) andby Location of Production Site on Geolgic Map Data

Number of sites

Lithology group Producer reported Bedrock map

Carbonate rock 27 25Granitic rock 30 24Trap rock 49 57

Jurassic basalt 11 11Other Mafic rocks 8 14

Metamorphic rocks 15 32

Total 106 106

One hundred six production sites for crushedstone aggregate, that were active in 2001, havebeen identified (MSHA permit data) and located

(Tepordei, 2001b) in New England. Table 2 lists andcompares the aggregate source rock types as classifiedby the aggregate producer (Tepordei, 2001b) and bybedrock geologic map unit. Although there is a dis-crepancy in numbers between the producer and ge-ologic map classifications, the important source rockcategories are consistent.

Carbonate rock, granitic rock, and trap rock arethe most important source materials used for aggre-gate at the quarry sites (Table 2). Trap rock is an in-dustry trade term that refers to a dark fine-grainedrock that generally would be classified using geologic

criteria as either mafic or metamorphic rock types.The trap rock category has been subdivided into ametamorphic rocks subgroup and two mafic rock sub-groups [(1) Jurassic basalt and (2) other mafic rocks].The metamorphic rocks subgroup generally repre-sents fine-grained, poorly foliated metamorphic rocktypes, including silicified metamorphic rocks occur-ring along fault zones. The most important sourcerocks for crushed stone aggregate reported by the ag-gregate producers are carbonate rock, granitic rock,and mafic rock, which comprise 25, 28, and 18%of the producing sites, respectively. Basalt and dia-base of Jurassic age is the most important individual

source rock in the mafic rock subgroup (11 sites). Theactive crushed stone aggregate quarry sites catego-rized by bedrock geologic map unit lithology are pre-dominately located in carbonate rock (25), graniticrock (24), mafic rocks (25—mafic rocks and Jurassicbasalt/diabase categories in Table 3), and other meta-morphic rocks (32—metamorphic rock and less suit-able lithologies in Table 3).


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Table 3. WofE Analysis Results for Evidence Classes, Showing Optimized Reclassificationa

Reclassification

Evidence Sites Area W + W − C s(C ) C / s(C ) Binary Multi

Lithology categoriesLess suitable lithologies 16 80.9% −1.680 1.497 −3.177 0.271 −11.707 0 0

Metamorphic rocks 16 6.1% 0.914 −0.101 1.015 0.272 3.737 1 1Other Mafic rocks 14 2.7% 1.604 −0.115 1.719 0.287 5.984 1 1

Granitic rock 24 7.8% 1.069 −0.176 1.245 0.232 5.360 1 1Carbonate rocks 25 2.3% 2.317 −0.246 2.563 0.229 11.173 1 2

Jurassic basalt & diabase 11 0.2% 3.981 −0.108 4.089 0.323 12.649 1 3Model contrast = 5.661

Model confidence = 14.245Proximity to pricipal roads

>3.2 km from roads 8 52.7% −1.944 0.671 −2.616 0.368 −7.1131.6−3.2 km from roads 20 18.7% 0.011 −0.003 0.014 0.248 0.0543.2 km from roads 29 74.0% −0.996 1.029 −2.025 0.218 −9.289

1.6−3.2 km from roads 27 12.0% 0.756 −0.167 0.922 0.223 4.1353.2 km 4 47.1%. −2.525 0.599 −3.123 0.510 −6.127 0 0One category 1.6−3.2 km 12 18.9% −0.514 0.090 −0.604 0.307 −1.970 0 1

One category


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producer and geology map unit classifications occurfor small bodies of granitic rock and thin carbonaterock units that are not delinated as individual mapunitsonthestatebedrockgeologicmaps(forexample,local bodies of carbonate rock occur as undesignatedmapunits in unit“SOcm” in Maine). This discrepancy

is dependenton mapscale, with theresult that produc-tion sites in granitic and carbonate rocks are slightlyunderrepresented in the analysis and sites in other-wise less suitable lithologies receive a higher weightthan their general lithology would otherwise merit.This weighting of less suitable lithologies representsthe degree of occurrence of small areas of suitablelithologies (that are not designated at map scale) inthe general less-suitable lithology rock groups. Thediscrepancy between the trap rock subgroups of theproducer versus the mafic and metamorphic rock sub-groups defined by the bedrock geologic maps reflectsboth spatial uncertainty in the bedrock map and, to

an unknown degree, misclassification of source rockby the producer. In all of these situations, the WofEanalysis assigns an occurrence weight to the sourcerock category defined by the bedrock geologic mapunits.

Transportation Network

A visual correlation between crushed stoneaggregate production site locations and proximityto principal transportation corridors is evident inFigure 2. Thirty-six percent of the crushed stone

quarries are sited within 1.6 km (1 mile) of bothprincipal highways and rail lines. This distance fromtransportation corridors defines an area of only 8.7%of the region (Table 3). Most of the crushed stonequarries (85%) are sited within 1.6 km of either aprincipal highway or a rail line in the region (34%of the regional area). These relations illustrate theimportance of proximity to transportation corridorsto the industry (Table 3).

Population Density

Population density at the census tract scale (ar-eas on the order of a few tens of square kilometers)is thought to provide an indicator of local proxim-ity to construction markets and is an indicator of the local setting of quarry operations. The popula-tion density distribution was categorized into ten in-tervals with similar ranges on a log-transformed scale.Using WofE to determine optimal breakpoints, these

intervals were subsequently grouped into four pop-ulation density classes (Fig. 3). A visual correlationof aggregate quarry site locations by increasing pop-ulation density categories is evident in Figure 3, andthe information in Table 3 quantifies the degree of correlation.

In New England, 78% of crushed stone quarriesoccur in census tracts with population densities ex-ceeding 100 people/mile2 (28% of the regional area),illustrating the importance of proximity to the urbanand developing urban fringe communities where ag-gregate is predominately used (Table 3). Only onecrushed stone quarry is located in a census tractwith a population density less than 15 people/mile2,reflecting the lack of a suf ficient market demandin many rural areas to justify the required invest-ment in the equipment needed to process and pro-duce crushed stone relative to other sand and gravelsources.

Most crushed stone quarries in New Englandhave been in operation for at least 20 years andall of the crushed stone quarries currently active inhigh population density areas were developed initiallywhen the areas were less populated (McFaul and oth-ers, 2000). However, existing crushed stone quarriesmust renew their operating permits on a recurringbasis, ranging from every few years to more than tenyears for states in the New England region. Many of these permit renewal applications include changes inaggregate production rate and changes in the area of the quarry operation.

MODEL INTEGRATION

WofE analysis was used to analyze the spatialassociations among the training sites and multiclassevidential theme layers, and to reclassify the layersfor optimal prediction (Bonham-Carter, 1994; Raines,Bonham-Carter, and Kemp, 2000). WLR modelingwas used to combine the optmized evidential themelayers to calculate the posterior probability for oc-currence of crushed stone aggregate production sites(Agterberg and others, 1993; Agterberg, Bonham-

Carter, and Wright, 1990; Bonham-Carter, 1994). Twomodels were developed and compared: (1) A simplemodel (Model 1) using binary class groups of geol-ogy, transportation, and population density evidentialtheme layers, and (2) a complex model (Model 2) us-ing multiclass geology, transportion, and populationdensity evidential theme layers with four data cate-gories in each evidence.


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The optimized reclassification of the geologyand population density evidential theme maps inthe binary and multiclass models used WofE anal-ysis weight and confidence criteria as documentedin Bonham-Carter, Agterberg, and Wright (1988),Wright (1996), Wright and Bonham-Carter (1996),

Mihalasky (1999), Raines (1999), and Kemp and oth-ers(2001). The binary andmulticlass reclassification issummarized in Table 3 along with the resulting WofEweights.

Contrast, constrast standard deviation, confi-dence, and contrast differences for the preliminaryevidence categories in Table 3 were used to reclassifyevidence in the binary and multiclass models. Con-fidence [C/s(C) in Table 3] is used as an informalsignificance test of each evidence category. A C/s(C)absolute value of 1.96 is approximately equal to 95%confidence that the reported contrast is not zero(Bonham-Carter, 1994; Bonham-Carter, Agterberg,

and Wright, 1988), which is considered significant.For the simple binary model (Model 1), for each

evidential theme layer, all preliminary evidence cat-egories with weights significantly greater than zerowere combined into class 1, indicating those cate-gories are associated positively with crushed stonequarries. Allcategories with weights less than or equalto zero were combined into class 0, indicating thatquarries are not associated with these areas.

The differences in contrast and their standard de-viations are used to evaluate whether the preliminaryevidence categories for each evidential theme layer in

Table 3 were suf ficiently similar such that they shouldbe combined in the multiclass model. The Student’st -test, similar to the confidence test above, is used asa general measure of the certainty that the measuredcontrasts in two preliminary evidence groups are notidentical.The studentized value of contrast differenceis calculated as:

Z = |(C 1 − C 2)|/( s(C 1)2 + s(C 2)

2)1/2 (1)

Table 4. Criteria, Weights (w−, w+), and Contrast (C ) with Confidence (C / s(C )) for Evidential Theme Categories Used to Develop the

Binary Class Model (Model 1), With WofE Model Conditional Independence Value

Evidence w− w+ C C / s(C ) Criteria

Bedrock geology −1.6202 1.4891 3.1094 11.7440 Bedrock unit suitable for aggregate (Table 1)

Population density −1.2065 1.0468 2.2533 9.5601 Within census tracts with >100 people/sq miPrincipal road + rail proximity −1.4153 0.9051 2.3203 8.7647 Within 1.6 km of principal road or rail line

Trainig sites: 106 Prior probability = 0.0006 sites/km2

Conditional Independence (CI of Agterberg and Cheng, 2002): 2.88

CI values greater than 2.33 indicate some conditional dependence at alpha = 0.01

where Z is the studentized value and C and s are thecontrast and its standard deviation for each grouppair. A value of Z greater than 1.96 is used in thisstudy as an informal significance test; this value is in-terpreted as approximately 95% confidence that thecontrast values are not identical. Using this criterion

for the multiclass model (Model 2), geology was cate-gorized into four lithology classes (three positively as-sociated with crushed stone quarries), transportationproximity into four classes (two positively associatedwith aggregate production sites) and population den-sity into four classes (two positively associated withcrushed stone quarries) (Table 3). To minimize condi-tional dependence between transportation evidence,the proximity to principal roads and rail line evidencewascombinedinto a composite evidential theme layer(Road & Rail, Table 3).

MODEL RESULTS

The binary evidence class model (Model 1) de-fines only eight posterior probability categories andis the easiest model to compare weight, contrast,and confidence measures. As indicated by the con-trast in WofE results for the binary evidence classmodel (Table 4), bedrock geology criteria provide thestrongest evidence for location of crushed stone ag-gregate quarries, followed by the transportation andpopulation density evidence that are nearly equiva-lent in contrast.

The geology evidence and population density ev-idence have similar values for w− and w+. Thesebalanced weights indicate that the favorable criteriain these evidence categories target where aggregateproduction occurs. Such evidence can be thought ofas inclusive evidence (Raines and Mihalasky, 2002).The weights defined for transportation network prox-imity are characterized by favorable areas with w+magnitudes that are smaller than the absolute value


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Table 5. Criteria, Weights (w+), and Contrast (C ) With Confidence (C / s(C )) for Evidential Theme CategoriesUsed to Develop the Multiclass Model (Model 2), With WofE Model Conditional Independence Value

Evidence Geology Population Transportation

(reclassification) (lithology groups) (population density) (proximity to road/rail)

0 (weight) −1.6801 −3.6369 −2.5245Critera Unfavorable rocks 3.2 km

1 (weight) 1.1284 −1.3562 −0.5142Criteria Granite + other rocks 16–45 peo/sq mi one 1.6−3.2 km

2 (weight) 2.3171 0.0250 0.6614Criteria Carbonate rocks 46–100 peo/sq mi one 100 peo/sq mi both


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Figure 4. Binary Class Model results (Model 1). Quarry sites are categorized by permit status. Classification of quarry site suitability is based

posterior probability model results. Posterior probability values lower than 0.0004 sites/km2 are designated as generally unsuitable areas forcrushed stone aggregate quarries; map areas with posterior probability exceeding this value are considered generally suitable for aggregate

quarries. Based on natural breaks in posterior probability results of models, suitable category was subdivided into low, moderate, and highsuitability categories at posterior probability values of 0.001 and 0.002 sites/km2.

the model poorly because the suitable geologic unitsthat the sites exploit for aggregate occur in limited

areas that are not delineated at the scale of the statebedrock map. For a few sites that are located nearbedrock unit contacts, the misclassification is causedby spatial uncertainty in the location of the site, spa-tial uncertainty in the location of the bedrock uniton maps compiled at regional scale, and inaccuracyin grid classification caused by the 100-m grid cellsize.

In the binary class model (Model 1, Fig. 4) allposterior probability areas classified as suitable oc-

cur in category 1 lithology evidence (Table 3). In themulticlass model, most of the posterior probabilityarea classified as suitable have category 1 or higherlithology evidence; all of the suitable areas have atleast two category 1 evidence categories and at leastone category 2 or higher evidence (Table 6). In thebinary class model, the high suitability areas occuronly where all evidence has category 1 status. In the


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Figure 5. Multiclass Model results (Model 2). Quarry sites are categorized by permit status. Classification of quarry site suitability is based

posterior probability model results. Posterior probability values lower than 0.0004 sites/km 2 are designated as generally unsuitable areas forcrushed stone aggregate quarries; map areas with posterior probability exceeding this value are considered generally suitable for aggregate

quarries. Based on natural breaks in posterior probability results of models, suitable category was subdivided into low, moderate, and highsuitability categories at posterior probability values of 0.001 and 0.002 sites/km2.

multiclass model, the high suitability areas occur onlywhere geology evidence is at least category 1 statusand where at least one other evidence has category 3

status.Although aggregate production data were not

considered in the WofE model, it is used as an indirectvalidation test of the model results. Production datafrom 2001 for the training site crushed stone quar-ries (V.V. Tepordei, proprietary data) was grouped bythe suitability categories defined by the model results.A mean site production rate of aggregate, calculated

for each suitability category, is tabulated in Table 6.The mean site production of aggregate increases anddiffers systematically with increasing posterior prob-

ability for the binary model results (adjusted R2

is0.95 for least-squares regression between mean siteproduction and log mean site posterior probabilityfor the binary model suitability categories in Table 6).The trend for the multiclass model is similar and theresults for both models overlap.

The model results were evaluated relative to in-formation on the permit history of the quarry sites.


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Table 6. Model Results in Relation to Training Site Permit Status and Average Rate of Aggregate Production Per Site in Model Categorya

Suitability category of Model Area All sites Repermit sites New sites Avg. site production Comments

Binary Model Fraction in category 106 mTons/YrGenerally unsuitable 0.69 0.06 0.06 0.00 0.25 Category 0 lithology

Low suitability 0.20 0.13 0.05 0.20 0.30 Category 1 lithologyor 2 other category 1

Moderate suitability 0.06 0.25 0.22 0.40 0.38 Category 1 lithology +1 other category 1

High suitability 0.05 0.56 0.67 0.40 0.46 3 category 1 evidence

Multiclass Model

Generally unsuitable 0.77 0.08 0.06 0.20 0.22 ≤2 category 1Low suitability 0.14 0.13 0.11 0.00 0.32 >2 category 1 with >1

category 2 evidenceModerate suitability 0.01 0.01 0.00 0.00 N/A Category 3 population

+>1 category 1High suitability 0.08 0.78 0.83 0.80 0.44 > category 1 lithology

+>1 category 3

Site count 106 18 5

a Training sites are categorized by MSHA permit history into three categories: (1) all active quarry sites (all sites), (2) quarry sites withoperating permits renewed and new permit numbers issued after 1990 (repermit sites), and (3) new quarry sites with initial permit numbers

issued after 1990 (new sites). Values in the area and sites categories are the fraction of the suitability category area or training sites thatoccur in that category relative to the total area or number of sites. Average site production is calculated using unpublished site production

data for 2001 (V.V. Tepordei, proprietary data) averaged for all sites with available data in each suitability category. Production data arenot available for the one site in the moderate suitability category of the multiclass model. Comments identify the evidential theme classes

that occur in the suitability category groups for each model. The theme classes in the binary model are either category 0 or 1. The themeclasses in the multiclass model range from category 0 to 3.

The quarry sites were characterized by MSHA permithistory into three categories: (1) new quarries withpermits issued after 1990, (2) existing quarries withpermits renewed and new permit numbers issued af-ter 1990, and (3) existing sites with permit numbersissued before 1990. All sites were used to calibrate themodel. Data on new and permit renewal sites wereused to evaluate the model. The frequency of occur-rence rates for the training sites in each category aretabulated in Table 6. The site occurrence rates aresimilar for each of the permit status categories, con-sidering the small sample size of the new and permitrenewal categories.

Information on the population density setting of the 18 permit renewal sites and 5 new quarry siteswas evaluated in greater detail to test and modify fur-ther the model. The permit renewal sites occurred in

all of the population density categories tabulated inTable 3 at rates similar to the entire training site dataset. The general model forecasts seem valid for sitepermit renewal rates. Based on the MSHA permitdata, 4 of the 5 new quarry sites are limited to pop-ulation density settings of less than 200 people/mile 2.The one site in Rhode Island that exceeds this rangeis sited within 1 km of a census tract with a popu-

lation density less than 200 people/mile2. The mul-ticlass model was modified to take into account thedif ficulty of permitting new quarry sites in highly pop-ulated areas by applying a population density filter ata threshold of 200 people/mile2 to the suitability mapin Figure 5. Census-tract areas with population den-sities more than 200 people/mile2 were grouped intothe “permit unlikely” category by default. Increasingpopulation density is interpreted as a negative influ-ence on the relative success rate for permitting newquarry sites. The new quarry model results are shownin Figure 6 relative to the five new quarry sites iden-tified by MSHA records. Four of the five new quarrysites occur in high suitability areas identified by thenew quarry model (Table 6, Fig. 6). One new quarrysite in Maine occurs in the generally unsuitable cate-gory based on population density evidence; this site is

within 1 km of higher population density areas. In thebinary and multiclass models (Figs. 4 and 5), the oc-currence rate of crushed stone quarries in the higherpopulation density areas reflects the relative successrate of the permit renewal process, whereas the oc-currence rate in the lower population density areasreflects both the rate of new quarry development andthe permit renewal process.


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Figure 6. New crushed stone quarry sites and multiclass model results with highest population densities (>200 people/sq mi) grouped in

“permit ulikely” category.

CONCLUSIONS

Geology provides the strongest predictive evi-dence for crushed stone quarry locations followedby population density and transportation evidence,

based on the WofE contrasts evaluated for boththe binary and multiclass models (Tables 4 and 5).Crushed stone aggregate is produced predominantlyfrom three hard-rock types that are widely distributedin the region; 24% of the aggregate production sitesproduce aggregate from carbonate rock, 30% pro-duce aggregate from granitic rocks, and 34% pro-duce aggregate from mafic and other metamorphic

rock types that are classified as trap rock by the in-dustry. On an area-weighted basis, carbonate rocksand Jurassic basalt are the two most important sourcerocksproviding crushed stone aggregate,with graniticand other mafic rock sources falling into a third cate-

gory with similar weights (Table 3).Eighty-five percent of the active crushed stone

quarries are sited within 1.6 km (1 mile) of either aprincipal highway or rail line in the region(34% of theregional area), illustrating the importance of proxim-ity to transportation corridors to the industry. In NewEngland, 78% of the quarries occur in census tractswith population densities exceeding 100 people/mile2


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(28% of the regional area), illustrating the impor-tance of proximity to the urban and developing ur-ban fringe communities where aggregate is predomi-nately used. Only one crushed stone quarry is locatedin a census tract with a population density less than15 people/mile2, reflecting the lackof a suf ficient mar-

ket demand in many rural areas to justify the re-quired investment in the equipment needed to pro-cess and produce crushed stone relative to other sandand gravel sources. Based on MSHA permit data,4 of the 5 quarries developed since 1990 have beenlimited to tracts with population densities less than200 people/mile2; the fifth site was within 1 km of a census tract with a population density less than200 people/mile2. This population density thresholdwas used to develop a model for new quarrydevelopment.

This paper demonstrates a technique to definesuitable areas for aggregate production with WofE

and WLR techniques using geologic map, transporta-tion network, and population density spatial data forevidence. The locations of current crushed stone quar-ries are used as training points to model relations be-tween the quarry sites, transportation networks, pop-ulation density, and geology. The training sites werecategorized by permit history and aggregate produc-tion at the site to test and further modify the model toapply to new quarry permit sites. Mean site aggregateproduction is found to differ systematically with themodel results.

These GIS methods provide a useful first gen-

eration reconnaissance that is tied to the data and isreproducible. This approach is data-drivenand depen-dent on the distribution of training sites that are rep-resentative of significant deposits. Data for one areacould be used as a model that, once trained, could beapplied in other areas in a fashion comparable to theconventional use of analogy in mineral exploration.

ACKNOWLEDGMENTS

The authors thank Betsy Halliday and Louise

Santoro (Mine Safety and Health Administration)for providing the MSHA quarry permit informationthat was used in this study. Valentin Tepordei (U.S.Geological Survey) provided unpublished informa-tion on crushed stone aggregate production that wasused in this study. The authors thank M. Milalasky(Richard Stockton College), G. Bonham-Carter (Ge-ological Survey of Canada), and L. Drew, J. Duval,

and S. Nicholson (all at the U.S. Geological Sur-vey) for comments and suggestions that improved themanuscript.

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A GIS Analysis to Evaluate Areas Suitable for Crushed1

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