REEVALUATING POVERTY CONCENTRATION WITH SPATIAL ANALYSIS: DETROIT IN THE 1990S1

Joe Grengs2

Urban and Regional PlanningUniversity of Michigan

Abstract: Standard measures of poverty concentration based on census tracts may not accu-rately reflect neighborhood conditions because they offer a weak link to the underlying geogra-phy of a neighborhood. Changes in the spatial configuration of land use within a census tract canhave the effect of increasing or decreasing the density of poverty. This study uses a dasymetricmapping technique in a raster GIS environment to intersect population data in a block grouplayer with land use categories from a land use layer. I produce poverty counts and rates at a muchfiner spatial resolution than a block group, with an explicit spatial relationship between popula-tion and surrounding neighborhood characteristics. I illustrate the technique for the City ofDetroit by measuring poverty concentration change between 1990 and 2000. I find that povertybecame more concentrated in space during the 1990s, counter to reports of diminishing povertyconcentration that are based on the share of poor people in high-poverty tracts. [Key words:concentrated poverty, geographic information systems, spatial analysis, neighborhood effects,Detroit.]

INTRODUCTION

Concentrated poverty improved dramatically in the United States during the 1990s bymost accounts (Jargowsky, 2003; Kingsley and Pettit, 2003). The standard measure ofconcentrated poverty is the share of poor people in an area who live in a census tract thatexceeds some poverty rate threshold, typically 40% (Bishaw, 2005). For large-scale,cross-sectional studies nationwide, such census tract rates are effective estimates formaking comparisons between cities or metropolitan regions. But the standard methods ofmeasuring concentrated poverty may be misleading because they lack a meaningfulspatial relationship to their immediate surroundings.

Neighborhood conditions influence poverty when they serve to isolate poor peoplefrom nonpoor people (Jencks and Mayer, 1990; Brooks-Gunn et al., 1997b), and isolationis partly determined by the spatial arrangement of neighborhood resources and opportu-nities; the density of supportive institutions, the mix of people and role models, and theproximity of jobs, schools, and services are a few examples. In other words, space mattersin how we interpret poverty, and to rely heavily on a method that discounts space is to

1I am grateful to the three referees and co-editor Elvin Wyly for extensive suggestions that greatly improvedthis work.2Correspondence concerning this article should be addressed to Joe Grengs, Urban and Regional Planning,University of Michigan, 2000 Bonisteel Boulevard, Ann Arbor MI 48109-2069; telephone: 734-763-1114; fax:734-763-2223; e-mail: grengs@umich.edu

340Urban Geography, 2007, 28, 4, pp. 340–360.Copyright © 2007 by V. H. Winston & Son, Inc. All rights reserved.

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miss an opportunity for a more theoretically satisfying link to the urgent questions stem-ming from the concentration effects first proposed by Wilson (1987, 1996).

The main purpose of this paper is to explain a technique for estimating populationdistributions at a fine-grained spatial resolution, and to describe why such a technique hasimportant implications for researchers who study poverty. I illustrate a method using geo-graphic information systems (GIS) to calculate poverty rates that account for surroundingspatial change, using land use as a proxy for neighborhood conditions. Land-use changeis important because the cities that show the most dramatic improvement in census tractpoverty rates simultaneously faced substantial and potentially damaging shifts in landuse. Housing abandonment, conversion from residential to nonresidential property, andgentrification are examples of land-use change with decisive effects on the density ofpoverty within a census tract. I illustrate the technique with a case study of concentratedpoverty in the City of Detroit between 1990 and 2000. The new method proposed hereproduces several results that are different from those revealed by analysis of census tractsalone: maps show significantly different geographic patterns; the places where the num-ber of people in poverty increased are concentrated in very small spaces; and a greatershare of people in poverty lived at higher densities—poor people per given area—in 2000than in 1990, a finding that runs counter to recent reports of diminishing concentration ofpoverty.

A second purpose is to illustrate how the technique can be applied to research ques-tions on concentrated poverty, and in particular on evaluating spatial changes in theneighborhood characteristics that immediately surround people who live in poverty.Geographers and urban planners have recently developed highly detailed techniques forevaluating surrounding neighborhood conditions for the study of environmental justice(McMaster et al., 1997; Liu, 2001) and physical activity (Forsyth, 2005), and methodsthat produce data at a fine spatial resolution open up new approaches to poverty research.

CONCENTRATION EFFECTS AND SOCIAL ISOLATION

The sudden interest among social scientists in measuring the spatial concentration ofpoverty that emerged in the late 1980s relied on a conceptual framework formulated byWilliam Julius Wilson (1987). He observed that severe economic deindustrializationpushed a growing share of inner-city Blacks out of the labor force at the same time thatother members of the Black community joined the ranks of the middle class and leftpoverty-stricken neighborhoods. He coined the term concentration effects for an array oftroubling social outcomes—such as chronic joblessness, high rates of crime, and out-of-wedlock childbirth—that result from large numbers of poor people living in close prox-imity to each other. Highly concentrated poverty brings disadvantage to some neighbor-hoods because, Wilson (1987, p. 60) argued, the social environment is characterized byextreme social isolation, a “lack of contact or of sustained interaction with individualsand institutions that represent mainstream society.” Poor Blacks, according to Wilson,lost contact with middle class Blacks who had previously served as role models ofeconomic success and had maintained the local community institutions that providedconnections to the larger mainstream society beyond the immediate neighborhood.Wilson hypothesized that people living in a community of highly concentrated povertywould face problems greater than others who are similarly situated but living in a

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community with people of a more diverse range of social status. The conceptual frame-work thus provides a link between individuals and their local surroundings, with empha-sis on social isolation as a community-level characteristic that derives from high rates ofspatially concentrated poverty.

Early efforts to test Wilson’s conceptual framework operationalized concentrated pov-erty by identifying census tracts that surpassed some threshold share of the populationliving below the federally defined poverty level. Danziger and Gottschalk (1987), study-ing the period between the decennial censuses of 1970 and 1980, defined concentratedpoverty as census tracts in which at least 40% of residents earned incomes below thefederally defined poverty level, and confirmed Wilson’s hypothesis that the number ofpoor Blacks living in concentrated poverty increased in absolute numbers and as a shareof all poor people. Jargowsky and Bane (1991) attempted to validate the 40% thresholdby conducting field observations in several cities. And in an comprehensive study ofneighborhood poverty nationwide, Jargowsky (1997) used census tracts and a 40%poverty rate cutoff to show that the number of high-poverty census tracts more thandoubled nationwide from 1,177 in 1970 to 2,726 in 1990, and that the number of peopleliving in them increased from 4 million to 8 million.

Wilson’s conceptual framework of concentration effects suggests that deconcentratingpoor people will leave them better off. Yet in practice, recent public policy actions aimedat deconcentrating poverty have resulted in questionable benefits for poor people, bothfor those who leave high-poverty neighborhoods and for the neighborhoods they leavebehind. Following the widely acknowledged failure of high-rise public housing develop-ments and the landmark Gautreaux case that stemmed from that failure, current federalpolicy now seeks to deconcentrate poverty through a wide range of initiatives, includingproviding housing subsidies through mixed-income developments, building scattered-site public housing units, and sponsoring mobility programs that require tenants torelocate to neighborhoods with low rates of poverty (Goetz, 2003).

Although evidence suggests that many poor residents are largely better off after relo-cating away from high-poverty neighborhoods, some scholars have questioned the effectson poor residents in the long run (Popkin et al., 2000). For example, mobility programsmust remain small in order to avoid political backlash from receiving communities(Goetz, 2003), and the small numbers make poor people in such communities politicallyvulnerable, with little chance of becoming full-fledged participants in the larger commu-nity. Many poor people discover that they miss the informal social support of their oldneighborhoods—despite the troubling poverty—such as the ability to rely on friends andfamily for child care (Goetz, 2003). Some regret their move because they “experiencediscrimination from neighbors and others in the community, difficulty with transporta-tion, loss of support and sense of community, and in some instances, still poor housingand neighborhood conditions” (Popkin et al., 2000, p. viii). That many poor people do notwant to move from concentrated poverty is not surprising because, despite the range ofpainful difficulties such places impose on residents, these are the neighborhoods wherepeople have learned to adapt to their conditions with creative institutions of their ownmaking (Wacquant, 1997). Perhaps most troubling from the perspective of Wilson’s ideasis the finding that movers commonly keep to themselves and do not interact with theirneighbors in their new communities (Popkin et al., 2000).

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Aside from the effects on the people who move, efforts to deconcentrate poverty havebeen criticized for harming poor people who are left behind. First, dispersal policies tendto select the most advantaged of the poor for moving out of high-poverty neighborhoods,including those residents who are “more motivated, more functional, and have broaderlife experiences and abilities” (Goetz, 2003, p. 242). Second, some charge that deconcen-trating the poor is an effort to dilute minority political strength by drawing down numbersof the Black community (Goetz, 2003). Third, initiatives such as HOPE VI to redevelopinner-city neighborhoods by achieving a wider mix of incomes have served to furtherisolate and alienate poor people in their own neighborhoods, and as rents rise many poorpeople are forced to flee their established social networks (Keating, 2000). And, finally,Venkatesh (2003) questions whether spatially concentrated poverty actually leads to theextreme social isolation that Wilson hypothesizes. Detailed ethnographic study of socialinteractions, according to Venkatesh, reveals a far more complex relationship betweenhigh-poverty neighborhoods and mainstream society, and that the geographic isolation ofpoor people may be less influential in their social isolation than are government actionssuch as improper policing practices and inequitable distribution of resources.

Despite these critiques of how Wilson’s conceptual framework is put into practice,social scientists need an accurate assessment of poverty concentration to continue thesearch for the causes and consequences of poverty. I demonstrate with this study how anexplicit accounting of space can yield surprisingly different results than standard mea-sures of poverty concentration.

THE ROLE OF SPACE IN THE ASSESSMENT OF POVERTY

Because census tract percentages have weak spatial dimensions, analysts who rely onthem as the basis for measuring poverty concentration face two main problems. The firstproblem is well understood and stems from the fact that census tract poverty rates ignorethe spatial organization of the tracts themselves. Known as the checkerboard problem, ameasure of a city’s poverty concentration fails to account for whether census tracts arescattered throughout the city or clustered tightly together (White, 1983). Fortunately,many promising new approaches are emerging to address the spatial relationships amongcensus tracts (Greene, 1991; Wong, 1993; Dawkins, 2004; Jargowsky and Kim, 2005).

The second problem deals not with the relationships among census tracts, but ratherwith the changes within a census tract, and is addressed with the method proposed here.One persuasive explanation for the improvement in poverty concentration during the1990s is that residents experienced a growth in income due to the unusual nationaleconomic boom of the late 1990s that peaked around 2000 (Jargowsky, 2003). Anotherpossible explanation for changes in poverty concentration is that the mix of peoplechanged in high-poverty census tracts (Danziger and Gottschalk, 1987). If a dispropor-tionate share of nonpoor people move in, or if poor people move out, a high-poverty tractwill show a declining rate of poverty. Thus, census data can tell us that poverty concen-tration has changed, but they cannot tell us whether the reason for the change was higherincomes or selective migration.

But there is yet another influence on poverty concentration, one that cannot bedetected by census data alone. Changes in the spatial configuration within a census tractcan have the effect of increasing or decreasing the net density of poverty. Density is more

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theoretically relevant to Wilson’s (1987) concentration effects than is a census tractpoverty rate. The poverty rate (the count of people per enumeration zone) serves as aproxy for density (the count of people per given area of land). But notice how a povertyrate is distinct from density. Consider two tracts with a poverty rate of 40%. One has fourpoor people out of a total of ten living in a large and sparsely settled tract; the other has2,000 poor people among 5,000 in a small and crowded tract. Both have equal povertyrates but the second tract has a much higher poverty density. And a higher poverty densityis likely to contribute to substantially different social isolation under theories of neighbor-hood effects (Jencks and Mayer, 1990; Brooks-Gunn et al., 1997a).

Furthermore, gross density (based on total land area) is likely to have different conse-quences for social isolation than does net density (based on occupied land, a subset oftotal land). Two tracts may have equal populations but considerably different net densi-ties. Consider two tracts of equal area, both with a poverty rate of 40% made up of 2,000poor people among a total of 5,000. In one of the tracts, people are spread uniformlyacross the entire tract. But in the second tract, an industrial wasteland occupies three-quarters of the tract’s area, leaving only a quarter of the space for housing. The secondtract would have a net density four times higher than the first, with different social out-comes predicted by theories of concentration effects.

Why is density the more relevant measure for evaluating concentration effects? Theidea that high levels of poverty concentration are related to harmful degrees of socialisolation is reasonable to the extent that “a spatially concentrated or segregated groupwould be expected to have less contact with other groups than would one whose memberswere evenly distributed throughout a geographic area” (Greene, 1991, p. 242). Contactand interaction are mediated by space, where the intensity of people and institutionswithin a given geographic space—density—determine access to opportunity.

Most studies that examine the concentration effects of poverty are limited by theirinability to properly capture complex spatial patterns and, furthermore, offer little theo-retical link between space and social conditions. The most widely used data source—thedecennial census—“does not provide measures of neighborhood characteristics thatmatch the theoretical concepts” (Duncan and Aber, 1997, p. 65). A common approach isto assume that a census tract represents a neighborhood, and then to select the few rele-vant census variables that describe the kinds of people who live in the neighborhood: “thedata sources traditionally relied upon by neighborhood researchers … typically provideinformation on the sociodemographic composition of statistical areas (e.g., poverty rateor racial makeup of census tracts) rather than the dynamic processes hypothesized toshape child and adolescent well-being” (Sampson et al., 2002, p. 443). Relying exclu-sively on census sociodemographic data can tell us about important dimensions of neigh-borhood poverty—such as female-headed households with children, unemploymentrates, and the share of recipients of public assistance. But to capture the effects of othercritical dimensions that influence poverty—such as crime, drugs, churches, communitycenters, and social ties stemming from daily activity patterns—we must turn to multipledata sources.

Enumeration zones (such as counties, census tracts, or block groupings) therefore con-strain research on poverty and neighborhood effects in several ways: analysis is limitedto the data attributes aggregated to the zone boundary; boundaries are approximations ofneighborhoods, with some large and some small; and boundaries are not stable, making

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assessments of change over time difficult. In addition to these problems, enumerationzones also present an important but overlooked obstacle to understanding poverty: ourinability as analysts to accurately visualize the spatial patterns (Martin, 1996). Thisinability stems from problems inherent in commonly used choropleth mapping tech-niques. Choropleth mapping—using gradations of shading—is the most common meansof displaying areal census data, providing an easy way to visualize how a socioeconomicattribute varies across geographic space. But choropleth maps have limited power fordetailed analysis of social conditions at the scale of a neighborhood.

Researchers who perform highly localized neighborhood analysis therefore faceseveral problems of misleading inaccuracies when using choropleth maps based onenumeration zones. First, the easy visualization comes at the price of much lost informa-tion: choropleth mapping achieves a smoothing out of data by suppressing the variationin an attribute through reclassifying values into just a few categories (Haining, 2003). Thelevel of measurement, in effect, is reduced from ratio to ordinal. Second, the patterns of achoropleth map depend substantially on the analyst’s choice of both classification methodand the number of data classes. Third, because census data are aggregated to enumerationzones that are for the most part arbitrarily drawn,3 choropleth maps “give the impressionthat population is distributed homogeneously throughout each areal unit, even whenportions of the region are, in actuality, uninhabited” (Mennis, 2003, p. 31). Fourth, thesearbitrary zones produce false and abrupt spatial discontinuities when presented inchoropleth maps (Langford and Unwin, 1994). Thus, continuous data—such as popula-tion density—appear to exhibit distinct changes from one enumeration zone to the next.Finally, these arbitrary zones contribute to the dangers of the modifiable area unitproblem (where results change depending on the size and configuration of the zones) andof the ecological fallacy, which stems from the aggregate nature of zonal data (Open-shaw, 1984; Sheppard, 1995). It takes a careful analyst to avoid the mistake of drawinginferences about particular kinds of people from data aggregated to enumeration zones(Pickles, 1995), and the fallacy is especially risky with the case of Wilson’s theory ofconcentration effects because he claims that individual behaviors are influenced byaggregate neighborhood conditions.

Improving Poverty Study with Dasymetric Mapping

One approach to getting around these problems—the inaccuracies of choropleth map-ping, representing concentration as a rate, and the absence of spatial relationshipsbetween people and their surroundings—is to detach the analysis from the arbitrarilydrawn enumeration boundaries. Dasymetric mapping is a technique of areal interpolationthat converts enumeration zones to smaller, more spatially relevant boundaries by addingsupplementary information, thereby transforming data from one set of geographic bound-aries to another (DeMers, 2005). The aim is to exploit the power of a GIS to integrate

3Boundary locations have little spatial meaning relative to underlying social conditions. Census tracts aredesigned to be relatively homogeneous with respect to population characteristics, economic status, and livingconditions. But in practice, the boundaries do not change in accordance to shifting conditions, and small tractsat the urban core capture space differently than large tracts at the suburban periphery.

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dissimilar data sources by intersecting two or more data sets to produce a more preciseestimate of a spatial distribution than would be possible with only one data set alone(Longley et al., 2005). Figure 1 illustrates how dasymetric mapping works, and showstwo separate layers of data. The first is the original layer, shown as dark lines, consistingof a block group enumeration zone associated with attributes from the Census of Populationand Housing, such as the number of persons below the federally defined poverty line. Thesecond layer is the ancillary layer, data that can be used to infer spatial distributionswithin any single block group of the original layer. I use land use polygons in this studyfor the ancillary layer.

By using land use polygons, the ancillary layer serves two purposes for redistributingpopulation within a block group. First, it identifies locations uninhabited by people.People do not live in cemeteries or water, for example. If I can locate all uninhabitedspaces like cemeteries and water, then I can allocate population away from such areas. InFigure 1, the shaded areas are residential territory, and all population in a block group isallocated to these sub-areas within a block group. Population is never reallocated acrossa block group boundary, a constraint that preserves the original attribute data (Tobler,1979). Note that the population density—on a people per area basis—would changesubstantially in Figure 1 after redistributing population with ancillary data. To calculate apopulation density for the original layer alone, in the absence of other information, wouldrequire assuming that population is evenly distributed throughout a block group, so thatthe population density in Block Group 2 in Figure 1 would be based on the full area of theblock group. But after redistributing the population with ancillary data, the populationdensity of Block Group 2 would be based on the area of polygon c, an area substantiallysmaller than the full block group.

Not only do the ancillary data allow for redistributing population by area of residentialterritory, but they also provide enough information to redistribute population within theresidential territories themselves. The second main purpose served by the land use cate-gories in the ancillary layer is to differentiate among residential areas by density classes—

Fig. 1. Schematic Illustration of the dasymetric mapping method.

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categories based on degree of population density. For example, people living in multiple-family housing in high-rise buildings occupy space at a higher density than people livingin single-family housing. Estimating the relative population densities among varioushousing types allows for allocating population within residential areas accordingly. Toillustrate, Block Group 1 in Figure 1 contains two different density classes. Polygon a, thedarker-shaded residential area, represents a higher population density (e.g., high-risemultiple-family housing) than polygon b (e.g., single-family housing). Because polygona is at a higher population density than polygon b, and because a and b occupy an equalshare of block group area, I can assign more of the block group population to a than to b.One of the main tasks in the steps that follow is to determine how much more of the blockgroup population should be assigned to a than to b.

This method of dasymetric mapping has been shown to produce accurate representa-tions of population density that offer more precision than the underlying original datalayer provides (Langford and Unwin, 1994; Eicher and Brewer, 2001). The method isconceptually straightforward, but somewhat cumbersome to carry out in a GIS, whichperhaps explains why it is not widely used in social science scholarship.

In an early demonstration of the technique, Wright (1936) cautioned against the use ofchoropleth maps for studying socioeconomic data, and offered the method of dasymetricmapping as a more accurate depiction of the variation in population across space. In astudy of Cape Cod in Massachusetts, he redistributed township population in two steps.He first identified uninhabited lands using topographic maps from the U.S. GeologicalSurvey and his own knowledge of the area. He next divided the inhabited lands into sub-jectively determined classes of population density, a step that he admitted was “basedlargely on guesswork” (Wright, 1936, p. 104).

Later studies followed Wright’s example by first isolating inhabited lands and thenestimating population densities. Langford and Unwin (1994), using census data in thearea around Leicester in the British Midlands, employed detailed remote sensing imagesin a raster data structure as ancillary data, and they collapsed all land cover categories intoeither “occupied” or “unoccupied” classes. Their approach does not differentiate betweenpopulation densities within the occupied class, assuming that housing types are uniformlyspread across the region. Holloway et al. (1999), estimating 1990 population densityaround Missoula, Montana, used multiple ancillary layers in identifying uninhabitedlands, including land ownership, topography, and land cover. Like Wright, they subjec-tively assigned predetermined population densities to land cover categories, allocating80% to urban areas, 10% to open lands, and 5% to each of agricultural and wooded lands.Eicher and Brewer (2001), in a study that evaluates several dasymetric techniques usingcounty-level population data for a region of 159 counties in the eastern United States,similarly assigned predetermined percentages to three land use classes within a county:70% of county’s population was assigned to urban lands, 20% to agricultural/woodland,and 10% to forested lands. They pointed out that a major weakness in their three-classapproach is that they did not account for the area of each land use class in a county.

These studies reveal two major problems with previous dasymetric mapping efforts.Population is assigned to inhabited lands using subjectively determined percentages, andpopulation is assigned to ancillary classes without accounting for differences in areaamong the classes. For instance, in the Eicher and Brewer (2001) study, 70% of a

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county’s population is allocated to urban land regardless of the actual share of thecounty’s total area occupied by urban land.

Mennis (2003) developed a technique that addresses both shortcomings. To improveon the subjectively defined percentages, he empirically sampled population density toarrive at percentage assignments that are rooted in observed measures. Then, to addressdifferences in area among ancillary classes, he used a weighting technique based on arealinterpolation to modify the percentages assigned to ancillary classes.

CASE STUDY: APPLYING DASYMETRIC MAPPING TO POVERTY IN DETROIT

To demonstrate the technique of dasymetric mapping, I use Mennis’s (2003)approach, but apply it to poverty populations in Detroit for both 1990 and 2000. My goalis to intersect population data in a block group layer with land use categories from a landuse layer, and by converting from polygon boundaries to raster cells, to produce povertycounts and rates at a much finer spatial resolution than a block group. The result will bea new data set that offers an explicit spatial relationship between population and highlylocalized surrounding land uses that is directly comparable over time. The new data setcan then be used to answer a series of questions about the change in poverty concentrationbetween 1990 and 2000 that could not be addressed with census tract or block group dataalone.

Detroit has been one of the nation’s most troubled central cities for decades, withsevere rates of crime, unemployment, neighborhood abandonment, and poverty (Furdellet al., 2005). It was the most impoverished large city in the nation in 2003, with more thanone in three residents living below the federal poverty line (U.S. Bureau of the Census,2005, Table R1701). Even though poverty remains troublesome in Detroit, measures ofpoverty concentration based on census tract rates showed substantial improvement duringthe 1990s. For example, Detroit’s decline in the number of people living in high-povertyneighborhoods in the 1990s was higher than in any other metropolitan region, with a dropof 74% (Jargowsky, 2003). And a far larger share of Detroit’s poor population lived inplaces of concentrated poverty in 1990 than in 2000: whereas 36% of the city’s poorpeople lived in “extreme-poverty tracts” (where 40% or more of residents are below thepoverty line) in 1990, just 10% of poor people lived in such tracts by 2000 (Kingsley andPettit, 2003). Jargowsky and Yang (2006, p. 67) evaluated improvements in social condi-tions nationwide during the 1990s based on four common indicators—male unemploy-ment, high school dropouts, female-headed households with children, and households onpublic assistance—and concluded that “the changes experienced by inner-city neighbor-hoods are nothing short of profound.” They used the well-known criteria for defining“underclass neighborhoods” devised by Ricketts and Sawhill (1988), and then comparedthe count of census tracts between 1990 and 2000 that simultaneously met the criteria.Detroit was among the metropolitan areas showing the greatest improvement in thereduction of “underclass neighborhoods,” and the authors tentatively suggested that theimprovement in social problems may result from reductions in poverty concentration.

All of these studies reporting improvements in poverty concentration were based onpoverty rates of census tracts. But even while census tract poverty rates improved inDetroit, however, the city simultaneously lost over 6,000 acres of residential land to otheruses, representing 15% of the total residential space (Southeast Michigan Council of

POVERTY CONCENTRATION AND SPATIAL ANALYSIS 349

Governments, 2004). What is the effect of such dramatic land use change on the geo-graphic concentration of poverty?

For the study area, I restrict my analysis to the municipal boundaries of Detroit,mapped in Figure 2. Note that two independent municipalities—Highland Park andHamtramck—lie within the boundaries of Detroit. A more complete analysis wouldinclude these other municipalities, along with suburban jurisdictions that lie beyond themunicipal boundary, to detect patterns in poverty throughout the region. Because the dataprocessing is extensive, however, I chose to focus exclusively on the central city for thepurpose of demonstrating the technique.

Data

For the original data boundary I use block groups in 1990 and 2000. Block groups arethe smallest geographic unit at which census data are provided from the long-form ques-tionnaire. For the ancillary layer, I use Land Use/Land Cover data (LULC) provided bythe Southeast Michigan Council of Governments. The LULC data were derived fromaerial photography, gathered in 1990 and 2000, and consist of polygons of land classifiedby types of urban development, following a standard hierarchical system proposedby Anderson et al. (1976). The highest level categories include, for example, Urban,Agricultural Lands, Forest, and Water. Within these broad categories are two levels of

Fig. 2. Overview of the City of Detroit and three-county region, 2000. Source: Environmental SystemsResearch Institute, Inc. (2003).

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highly detailed subcategories. At the second level within the Urban classification are suchsubcategories as Residential, Commercial, and Industrial. These are further divided intoa third level of detail, so that under Residential constitutes 13 separate subcategories,allowing for isolating areas based on population density. For example, I can distinguishbetween multiple-family housing in high rises from multiple-family low-rise develop-ment from single-family housing. The dasymetric mapping technique uses these residen-tial subcategories to define density classes for the purpose of redistributing populationwithin a block group.

The LULC data consist of vector polygons. I converted the LULC data to a raster gridwith a 250-foot resolution, a cell size small enough to capture the smallest block group.All subsequent raster grids created in the analysis conform to this 250-foot resolution.

Finally, for the numbers of people living in poverty, I use data collected by the CensusBureau for the decennial censuses of 1990 and 2000 (which are based on incomes from1989 and 1999). The Census Bureau uses the federal government’s official poverty defi-nition, which was originally developed in 1964 (U.S. Bureau of the Census, 2002b).Many scholars and public officials have raised questions about the accuracy of this offi-cial measure of poverty (Citro and Michael, 1995). Because the official measure fails toadapt to rapidly changing social conditions, the results of any study that makes compari-sons over time (such as the one presented here) should be interpreted cautiously.4

Steps in the Method

The goal is to allocate block group poverty populations to raster grid cells within theblock group. The population is distributed to a grid cell according to a formula thataccounts for two factors: the relative share of population density among density classes ina block group; and the relative share of area among density classes in a block group. Iillustrate the technique in four steps: (1) calculating the density factor, (2) calculating thearea factor, (3) combining the results from the first two steps to calculate grid cell popu-lations in a table, and (4) constructing raster grids from the table results.

Step 1 begins by defining the density classes from land use categories, with the aim ofdifferentiating among housing types. I define five density classes that correspond to fiveland use categories, in order of increasing density: single-family housing where 75% ormore of housing units are vacant; single-family housing where up to 75% of units arevacant; single-family housing; low-rise multiple-family housing; and high-rise multiple-family housing. Defining the density classes is carried out through empirical sampling, asproposed by Mennis (2003), as a way to avoid the pitfall of previous studies that relied on

4The official measure of poverty is an important social indicator that affects public policies, governmentprograms, and the public’s perception of the problem. But it is widely considered flawed by the social scientistswho nonetheless use it regularly in their work. The current measure has remained largely unchanged overdecades and may no longer provide an accurate picture of material deprivation in the United States todaybecause of several weaknesses: it does not distinguish between the differing needs of workers and nonworkers;it does not account for differences among population groups; it fails to account for price differences amongregions across the nation; and, most important for this study, it fails to adjust to far-reaching changes in theeconomy and in public policies so that comparisons over time are highly questionable (Citro and Michael,1995).

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subjectively allocating population among various land uses. I assume that the relativeshare of population densities within any single block group occurs in the same relativeshare of densities throughout the study area (City of Detroit). Table 1 shows the densityclasses, their associated land use category, and in column 1 the aggregate populationdensity.

The population density fraction is the share of a block group’s population that will beallocated to a particular density class within the block group. It consists of a relative pro-portion of the aggregate population densities and is calculated according to the followingformula:

(1)

where: dc is the density fraction for density class c; pc is the population density (people/square mile) for density class c; for a set of N density classes, c = 1, 2, …, N.

The results are listed in column 2 of Table 1 for the year 2000. Note that the densityfraction for a particular density class is calculated for the entire study area—in this case,for the City of Detroit. So the density fraction for a particular density class is applied toevery block group in the city.

Step 2 calculates the area factor by applying a weight based on the share of a blockgroup’s area occupied by a density class, expressed as:

(2)

where acb is the area ratio of density class c in block group b; ncb is the number of gridcells (i.e., area) of density class c in block group b; nb is the number of grid cells (i.e.,

TABLE 1. POPULATION DENSITY FRACTION, BY DENSITY CLASS, CITY OF DETROIT, 2000

Density class Land use category

(1)Population density(people per acre)

(2)Density

fraction (d)

1 Single-family housing 84 0.100

2 75% or more vacant 21 0.025

3 Up to 75% vacant 42 0.050

4 Multifamily, low-rise 180 0.214

5 Multifamily, high-rise 514 0.610

Total 843 1.000

Source: U.S. Bureau of the Census (2002a); Southeast Michigan Council of Governments (2004).

dc

pc

pc

c 1=

N

∑-------------------=

acb

ncb

nb--------

1 N⁄-------------=

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area) in block group b; N is the number of density classes considered in the region; for aset of N density classes, c = 1, 2, …, N.

Step 3 combines the results from above into a single expression, the total fraction,which jointly accounts for the contribution of both the relative density and area of a den-sity class within a block group. The total fraction is expressed as:

(3)

where: fcb is the total fraction of density class c in block group b; all others as definedabove.

Step 4 converts the total fraction from the previous step to grid cells in a raster layer,as follows:

(4)

where: popcb is the estimated population in a grid cell of density class c in block group b(population can be any count variable, such as the number of people below the povertyline, or the number of people above the poverty line); popb is the population of blockgroup b; fcb and ncb are defined above.

The final result is a raster layer consisting of cells filled with the number of peoplebelow the poverty line, with one layer for each of two years.

VISUALLY COMPARING MAPPING METHODS:CENSUS TRACTS VS. DASYMETRIC MAPPING

Figure 3 compares two maps of the poverty rate, with one showing a choropleth mapof data aggregated to census tracts, and the other displaying a continuous raster surfacederived from the dasymetric mapping technique. The most striking difference betweenthe maps is the amount of nonresidential land revealed by the raster version. Map A,which shows census tracts, gives a false impression of continuity across space. By con-trast, the raster of Map B indicates a choppy, broken-up landscape. Map B reveals that themany places of high poverty are surrounded by swaths of nonresidential space and effec-tively cut off from nearby neighbors, and furthermore that the degree of spatial isolationis not evenly distributed across the city.

By zooming in on a small part of the city, Figure 4 illustrates the degree of detailprovided by the dasymetric mapping method. It compares the same data at three spatialresolutions: a single census tract (panel A); the three block groups that make up the cen-sus tract (panel B); and the raster grid cells derived from dasymetric mapping (panel C).The white space of panel C represents nonresidential development which misleadinglyappears to be absent from the census tracts of panel A. Notice that the raster of panel C

fcb

dcacb

dcacb

c 1=

N

∑--------------------------=

popcb

fcbpopb

ncb-------------------=

POVERTY CONCENTRATION AND SPATIAL ANALYSIS 353

Fig. 3. Comparing patterns of poverty rates by (A) census tracts and (B) raster grid cells, City of Detroit,2000. Source: U.S. Bureau of the Census (1993, 2002a); Southeast Michigan Council of Governments (2004).

Fig. 4. Comparing poverty concentration in three views: (A) census tract, (B) block groups, (C) raster gridcells, City of Detroit, 2000. Source: U.S. Bureau of the Census (1993, 2002a); Southeast Michigan Council ofGovernments (2004).

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shows substantial stretches of nonresidential space surrounding isolated pockets of highpoverty.

MEASURING CHANGE IN POVERTY CONCENTRATION

The dasymetric mapping technique offers four important advantages over censustracts in assessing change in poverty. The first is the ability to define concentration basedon space, as a density rather than a poverty rate at an arbitrarily sized census tract. Table2 compares the residential density of people below the poverty line in 1990 and 2000, andconfirms what the visual inspection suggests: a greater share of people in poverty lived athigher densities in 2000 than in 1990. For example, 20% of poor people lived at a densitygreater than 20 poor people per acre in 2000, compared to just 5% of poor people in 1990.Furthermore, the places where poverty increased contained a much larger share of thecity’s poor people in 2000 than they did in 1990. For example, by isolating the zoneswhere the number of poor people increased by at least 100%, I found that the sum of thearea of these zones—which are highly scattered throughout the city—constitute about10% of the city’s residential land (based on 2000). These zones contained just 5% of thecity’s poor population in 1990, but contained 22% of the city’s poor population by 2000.This finding that poor people are living in closer proximity to other poor people in 2000compared to 1990 is surprising in light of the nationwide studies that reported dramaticimprovements in poverty concentration (Jargowsky, 2003; Kingsley and Pettit, 2003).

A second advantage is that a raster grid allows for direct comparisons of territory overtime, so that the data can be visualized with maps that compare the same locations fromone period to the next. Because census tract boundaries change from one decennial cen-sus to the next, such maps are difficult to construct using census tracts alone. To illustrate,the map in Figure 5 shows the absolute change in the number of people living in povertyduring the 1990s. Because the aggregate number of the poorest of people in the citydropped considerably by official estimates during the 1990s—from 328,500 in 1989 to243,000 in 1999—it is not surprising that the vast majority of the city’s territory in themap shows a decline in the number of people in poverty. But Figure 5 also makes clearthat change in poverty was not at all uniform in space. Indeed, the places where the

TABLE 2. CHANGE IN DENSITY OF POVERTY POPULATION, CITY OF DETROIT, 1990–2000

Density of poverty population(people in poverty per acre)

Share of poverty population (%)

1990 2000

1 0–9.9 61.2 64.7

2 10–19.9 33.8 15.6

3 20–29.9 3.7 8.1

4 30 and over 1.3 11.6

Total 100.0 100.0

Source: U.S. Bureau of the Census (1993, 2002a); Southeast Michigan Council of Governments (2004).

POVERTY CONCENTRATION AND SPATIAL ANALYSIS 355

number of people in poverty increased are concentrated in very small spaces. The spatialconcentration of worsening poverty is represented in Figure 5 by the dark-shaded areas,with the places that experienced the highest increase in poverty appearing so small on themap as to be difficult to detect. With close inspection, the map helps us see that povertyincreased in very small pockets, that these pockets are scattered throughout the city, andthat they are often surrounded by large areas where poverty diminished substantially.

A third advantage in the assessment of poverty change is the ability to isolate territo-ries within the city that experienced either worsening or improving conditions. Table 3provides a matrix of poverty population densities to show how places changed in theirpoverty rates during the 1990s. The rows represent poverty categories in 1990 and the

Fig. 5. Change in the number of people in poverty by raster grid cell, City of Detroit, 1989–1999. Source:U.S. Bureau of the Census (1993, 2002a); Southeast Michigan Council of Governments (2004). Notes: (1)Figures represent the change in the number of poor people per grid cell (250 ft. × 250 ft.). (2) White spacecontains both nonresidential space and places where the number of poverty residents decreased.

TABLE 3. CHANGE IN DENSITY OF POVERTY POPULATION (PEOPLE IN POVERTY PER ACRE) BY POVERTY CATEGORY, CITY OF DETROIT, 1990–2000a

Povertycategory (%) 1 2 3 4 5

0–9.9 1 (0.0) 1.2 2.9 3.9 4.5

10–19.9 2 (1.5) (0.1) 1.6 3.4 5.1

20–29.9 3 (3.1) (2.2) (0.5) 1.5 2.7

30–39.9 4 (4.5) (4.0) (3.2) (0.6) 1.1

40 and over 5 (6.3) (7.4) (5.6) (4.7) (3.3)

aRows are poverty categories for 1990, columns are poverty categories for 2000. Italicized cells representterritories where poverty rates increased between 1990 and 2000. Negative numbers, representing adecrease in density, are denoted with parentheses.Source: U.S. Bureau of the Census (1993, 2002a); Southeast Michigan Council of Governments (2004).

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columns represent corresponding poverty categories in 2000. For an example of readingthe table, the cell at the intersection of row 1 and column 5 tells us that the povertypopulation density increased by 4.5 people per acre in the places that experienced a wors-ening rate of poverty, from the category of the lowest poverty rate (0–9.9%) in 1990 tothe category of the highest poverty rate (40% and over) in 2000. The matrix diagonalrepresents territory where the poverty category did not change during the decade. Cellsabove the diagonal are the places where poverty rates worsened during the decade, andcells below the diagonal are places where poverty rates improved.

Table 4 illustrates another example of comparing territories over time, as a matrix ofthe change in land area by poverty rate categories. For instance, the cell at the intersectionof row 1 and column 5 tells us that 55 acres of land converted from the category of thelowest poverty rate (0–9.9%) in 1990 to the category of the highest poverty rate (40% andover) in 2000. The table indicates that the places where poverty rates worsened during thedecade were confined to relatively small portions of the city’s territory. Each cell in suchmatrices represents a different kind of change, and the territories of these cells can easilybe isolated for further focused analysis in a GIS.

Finally, the fourth and most significant strength of the dasymetric mapping method isin the range of spatial analysis that can be performed once a raster surface is estimated,allowing for the assessment of conditions in adjacent areas. Unlike census tracts alone,this method explicitly accounts for nonresidential space. And the territory in whichpeople do not live is important for the conditions of distressed neighborhoods. Thepresence of schools, parks, and places of worship contribute to the vitality of a neighbor-hood, and this method allows for assessing how such places are changing. Recall that themap in Figure 4 revealed isolated pockets of high poverty surrounded by substantialstretches of nonresidential space. Do large expanses of nonresidential space contribute tothe isolation that people in poverty experience? Some kinds of nonresidential space aremore likely to strengthen social interaction and promote access to opportunity (community

TABLE 4. CHANGE IN LAND AREA (IN ACRES) BY POVERTY CATEGORY, CITY OF DETROIT, 1990–2000a

Poverty category (%) 1 2 3 4 5 6 Total

0–9.9 1 3,634 3,069 841 174 55 235 8,007

10–19.9 2 1,812 3,887 2,300 633 232 340 9,204

20–29.9 3 610 3,568 2,459 1,014 494 429 8,574

30–39.9 4 284 1,588 3,220 2,608 1,539 802 10,042

40 and over 5 24 1,011 3,729 5,562 5,060 1,821 17,208

Nonresidential 6 367 1,261 1,022 890 1,165 31,154 35,858

88,894

aRows are poverty categories for 1990, columns are poverty categories for 2000. Italicized cells representterritories where poverty rates increased between 1990 and 2000.Source: U.S. Bureau of the Census (1993, 2002a); Southeast Michigan Council of Governments (2004).

POVERTY CONCENTRATION AND SPATIAL ANALYSIS 357

centers, good jobs, a mix of businesses) than others (busy roads, empty industrial sites,utility corridors), and the raster method proposed here provides a basis for assessing thespatial relationship between localized poverty and neighborhood surroundings. More-over, because the method allows for direct comparisons over time, it makes it possible tocompare change in poverty to change in surroundings. To illustrate how such an analysismight proceed, by defining land use developments that are more likely to increase socialinteraction or improve access to meeting one’s daily needs, future research could investi-gate whether poverty improved where neighboring conditions also improved. A majoradvantage of this technique is that multiple other layers of data—such as the location ofjobs, community centers, or places of worship—could readily be added within the sameanalytical framework to interrogate relationships to poverty populations.

CONCLUSION

We know that living conditions for people in poverty are made even more difficultwhen immediate neighbors are also struggling with poverty. And a growing body ofliterature suggests that localized neighborhood conditions have an independent effect ona person’s poverty status. Yet analysts rarely evaluate changes in poverty using measuresthat account for spatial change. This study develops a straightforward technique forestimating the spatial distribution of poverty. It offers a number of advantages with ananalytical framework that detaches poverty analysis from zones such as census tracts orblock groups: it provides an explicit spatial link to surrounding conditions; allows forcalculating poverty rates at uniform and comparable spatial units; permits conductinganalysis of change with consistent spatial units over time; provides the ability to assembledissimilar data sets into a single analytical scheme; and enables the making of maps thatprovide more accurate visual portrayal of geographic patterns.

By illustrating the method with the case study of Detroit, I discovered findings thatwould not be revealed through analysis of census tracts alone. First, neighborhood aban-donment is so severe in some neighborhoods that islands of concentrated poverty areemerging, placing greater distance between poor neighborhoods and nonpoor people andinstitutions. Second, although poverty in the aggregate improved in Detroit during the1990s, the gains in poverty concentration may not be as striking as found in previousstudies. The finding that poverty is becoming more concentrated in space runs counter tothe widely reported findings that are based on census tract poverty rates. Small pockets ofsubstantial increases in poverty that are surrounded by diminishing poverty can result ina drop in the poverty rate at the resolution of a census tract. But without examining rela-tive shifts within a census tract, we run the risk of failing to see compact and isolatedpockets of severe conditions of concentrated poverty. The methodology outlined hereoffers the advantage of detecting patterns at highly detailed spatial resolution for thepolicymakers, urban planners, and community activists who operate at the neighborhoodscale to improve the lives of poor people.

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