• J Quant Criminol-Not ‘Islands, Entire of Themselves’: Exploring the Spatial Context of City-level Robbery Rates


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j quant criminol (2008) 24:363–380
doi 10.1007/s10940-008-9049-3
original paper
not ‘islands, entire of themselves’: exploring
the spatial context of city-level robbery rates
glenn deane steven f. messner thomas d. stucky kelly mcgeever
charis e. kubrin
published online: 1 may 2008
springer science+business media, llc 2008
abstract the current study examines spatial dependence in robbery rates for a sample of
1,056 cities with 25,000 or more residents over the 2000–2003 period. although com-
monly considered in some macro-level research, spatial processes have not been examined
in relation to city-level variation in robbery. the results of our regression analyses suggest
that city robbery rates are not spatially independent. we find that spatial dependence is
better accounted for by spatial error models than by spatial lag models. further exploration
of various spatial weights matrices indicates that robbery rates of cities within the same
state are related to robbery rates of other cities within the same state, regardless of their
proximity. our analyses illustrate how systematic inquiry into spatial processes can alert
researchers to important omitted variable biases and identify intriguing problems for future
research.
keywords city-level robbery rates spatial dependence spatial weights
state-level covariates
this material is based upon work supported by the national science foundation under grant no. ses-
0215551 and sbr-9513040 to the national consortium on violence research (ncovr). an earlier draft of
this paper was presented at seventy-sixth annual meeting of the eastern sociological society, boston, ma,
february 23–26, 2006. the paper evolved from research originally presented at a workshop sponsored by
ncovr. we are grateful to the participants at the workshop for comments on the paper, with special thanks
to robert j. bursik, jr., thomas bernard, and paul nieuwbeerta.
g. deane (&) s. f. messner k. mcgeever
department of sociology, university at albany, state university of new york,
albany, ny, usa
e-mail: [email protected]
t. d. stucky
department of criminal justice, indiana university-purdue university indianapolis,
indianapolis, in, usa
c. e. kubrin
department of sociology, george washington university, washington, dc, usa
123
364 j quant criminol (2008) 24:363–380
introduction
over the past several decades, criminologists have become increasingly appreciative of the
importance of spatial dynamics. geographic space plays a key role in place-based theories
of crime such as routine activities theory (cohen and felson 1979; felson 1998), crime
pattern theory (brantingham and brantingham 1993; see also anselin et al. 2000), and
‘‘hot spots’’ theories (roncek and meier 1991; sherman et al. 1989; sherman and weis-
burd 1995). in addition, explicit spatial modeling has been incorporated in research on
violent crime for areal units at both the relatively small level of aggregation of census
tracts or neighborhoods (cohen and tita 1999; kubrin and weitzer 2003; morenoff and
sampson 1997; morenoff et al. 2001; rosenfeld et al. 1999) and the larger level of
aggregation of counties (baller et al. 2001; messner et al. 1999). however, despite the
growing interest in spatial analyses in criminology in general, there has been a curious
neglect of the possibility of statistically and substantively important spatial dependence in
the variation of violent crime rates across cities, perhaps because cities are usually geo-
graphically separate from, rather than adjacent to, other cities.
the purpose of this study is to explore the spatial context of variation in violent crime
rates for an offense of particular concern in urban areas—robbery—for a large sample of
u.s. cities. we begin by estimating a baseline model of the structural covariates of city-
level robbery rates and test for spatial autocorrelation, using predictors included in most
prior city-level studies of violence such as robbery and homicide. at a minimum, the
spatial dependence revealed in our analyses implies that variance estimates are biased,
which may in turn lead to faulty inference about the baseline covariates. in addition, if
spatial dependence violates the regression assumption that the error of prediction for an
observation cannot be predicted by its value on an independent variable, sample estimates
of the population regression coefficients will be biased. we then apply spatial econometric
strategies to track the source of residual correlation. our analyses provide examples of the
deleterious consequences of these assumption violations in commonly used regression
models based on city-level data. more broadly, our analyses illustrate how systematic
inquiry into spatial dependence can generate new insights about macro-level variation in
violent c


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