Chapter 5: 

Spatial Autocorrelation Statistics 

Ned Levine1 

Ned Levine & Associates

 Houston, TX 

The author would like to thank Dr. David Wong for help with the Getis-Ord >G= and local Getis-Ord 

statistics. 

1 
 

 

 

 

 

 

 

 

 

 

 

 

Table of Contents 

Spatial Autocorrelation 5.1 

Spatial Autocorrelation Statistics for Zonal Data 5.3 

Indices of Spatial Autocorrelation 5.3 

Assigning Point Data to Zones 5.3 

Spatial Autocorrelation Statistics for Attribute Data 5.5 

Moran=s “I” Statistic 5.5

 Adjust for Small Distances 5.6 

 Testing the Significance of Moran’s “I” 5.7 

Example: Testing Houston Burglaries with Moran’s “I” 5.8 

 Comparing Moran’s “I” for Two Distributions 5.10 

Geary=s “C” Statistic 5.10

 Adjusted “C” 5.13 

 Adjust for Small Distances 5.13 

 Testing the Significance of Geary’s “C” 5.14 

Example: Testing Houston Burglaries with Geary’s “C” 5.14 

Getis-Ord “G” Statistic 5.16

 Testing the Significance of “G” 5.18 

 Simulating Confidence Intervals for “G” 5.20 

Example: Testing Simulated Data with the Getis-Ord AG@ 5.20 

Example: Testing Houston Burglaries with the Getis-Ord AG@ 5.25 

Use and Limitations of the Getis-Ord AG@ 5.25 

Moran Correlogram 5.26 

 Adjust for Small Distances 5.27 

 Simulation of Confidence Intervals 5.27 

Example: Moran Correlogram of Baltimore County 

Vehicle Theft and Population 5.27 

Uses and Limitations of the Moran Correlogram 5.30 

Geary Correlogram 5.34 

 Adjust for Small Distances 5.34 

Geary Correlogram Simulation of Confidence Intervals 5.34 

Example: Geary Correlogram of Baltimore County Vehicle Thefts 5.34 

Uses and Limitations of the Geary Correlogram 5.35 
 

 

 

 

 

 

 

Table of Contents (continued) 

Getis-Ord Correlogram 5.35 

 Getis-Ord Simulation of Confidence Intervals 5.37 

Example: Getis-Ord Correlogram of Baltimore County Vehicle Thefts 5.37 

Uses and Limitations of the Getis-Ord Correlogram 5.39 

Running the Spatial Autocorrelation Routines 5.39 

Guidelines for Examining Spatial Autocorrelation 5.39 

References 5.42 

Attachments 5.44 

A. Global Moran’s “I” and Small Distance Adjustment: Spatial Pattern of 

Crime in Tokyo 

By Takahito Shimada 5.45 

B. Preliminary Statistical Tests for Hotspots: 

Examples from London, England 

By Spencer Chainey 5.46 
 

Chapter 5: 

Spatial Autocorrelation Statistics 

This chapter discusses statistics for describing spatial autocorrelation that are applicable 

to zonal data. A good grasp of basic statistics is a requirement for reading this chapter. Figure 

5.1 shows the Spatial Autocorrelation page within the Spatial Description section. This includes 

global tests of spatial autocorrelation for zone data or point data in which an attribute can be 

associated with the coordinates. The section includes six tests for global spatial autocorrelation: 

1. Moran=s AI@ statistic 

2. Geary=s AC@ statistic 

3. Getis-Ord AG@ statistic 

4. Moran Correlogram 

5. Geary Correlogram 

6. Getis-Ord Correlogram 

These indices would typically be applied to zonal data where an attribute value can be 

assigned to each zone. Six spatial autocorrelation indices are calculated. All require an intensity 

variable in the Primary File. 

The discussion in the chapter will concentrate on defining the indices and demonstrating 

how they can be used. Specific instructions for running the routines are given at the end of the 

chapter while detailed information is provided in Chapter 2. 

Spatial Autocorrelation 

The concept of spatial autocorrelation is one of the most important in spatial statistics in 

that it implies a lack of spatial independence. Classical statistics assumes that observations are 

independently chosen and are spatially unrelated to each other. The intuitive concept is that the 

location of an incident (e.g., a street robbery, a burglary) is unrelated to the location of any other 

incident. The opposite condition - spatial autocorrelation, is a spatial arrangement of incidents 

such that the locations where incidents occur are related to each other; that is, they are not 

statistically independent of one another. In other words, spatial autocorrelation is a spatial 

arrangement where spatial independence has been violated. 

When events or people or facilities are clustered together, we refer to this arrangement as 

positive spatial autocorrelation. Conversely, an arrangement where people, events or facilities 

5.1 
Figure 5.1:

Spatial Autocorrelation Statistics 
 

are extremely dispersed is referred to as negative spatial autocorrelation; it is a rarer 

arrangement, but does exist (Levine, 1999). 

However, many, if not most, social phenomena are spatially autocorrelated. In any large 

metropolitan area, most social characteristics and indicators, such as the number of persons, 

income levels, ethnicity, education, employment, and the location of facilities are not spatially 

independent, but tend to be concentrated. 

There are practical consequences. Police and crime analysts know from experience that 

incidents frequently cluster together in what are called >hot spots=. This non-random arrangement 

can allow police to target certain areas or zones where there are concentrations of crimes as well 

as prioritize areas by the intensity of incidents. Many of the incidents are committed by the same 

individuals. For example, if a particular neighborhood had a concentration of street robberies 

over a time period (e.g., a year), many of these robberies will have been committed by the same 

perpetrators. Statistical dependence between events often has common causes. 

Statistically, however, non-spatial independence indicates that many statistical tools and 

inferences are inappropriate. For example, the use of a correlation coefficient or Ordinary Least 

Squares regression (OLS) model to predict a consequence (e.g., correlates or predictors of 

burglaries) assumes observations are randomly selected. If, however, the observations are 

spatially clustered, the estimates obtained from the correlation coefficient or OLS estimator will 

be biased and overly precise. The coefficients will be biased because areas with a higher 

concentration of events will have a greater impact on the model estimate and precision will be 

overestimated because concentrated events tend to have fewer independent observations than are 

being assumed. The spatial autocorrelation concept underlies almost all of CrimeStat’s spatial 

statistics tools. 

Indices of Spatial Autocorrelation 

Assigning Point Data to Zones 

If a user has information on the location of individual events (e.g., robberies), then it is 

better to utilize that information with the point statistics discussed in Chapter 4 and the hot spot 

tools that will be discussed in Chapters 7 and 8. The individual-level information will contain all 

the uniqueness of the events. 

However, sometimes it is not possible to analyze data at the individual level. The user 

may need to aggregate the individual data points to spatial areas (zones) in order to compare the 

events to data that are only obtained for zones, such as census data, or to model environmental 

correlates of the data points or may find that individual data are not available (e.g., when a police 

5.3 
 

 

 

department releases information by police beats but not individual streets). In this case, the 

individual data points are allocated to zones by, first, spatially assigning them to the zones in 

which they fall and, second, counting the number of points assigned to each zone. A user can do 

this with a GIS program or with the “Assign Primary points to Secondary Points” routine that 

will be discussed in Chapter 6. 

In this case, the zone becomes the unit of analysis instead of the individual data points. 

All the incidents are assigned to a single geographical coordinate, typically the centroid of the 

zone, and the number of incidents in the zone (the count) becomes an attribute of the zone (e.g., 

number of robberies per zone; number of motor vehicle crashes per zone). 

It should be obvious that when individual data points are assigned to zones, information 

is lost. Instead of capturing the unique locations of the individual events, all events that occur 

within a zone are assigned a single location. Thus, the distance between zones is a singular value 

for all the points in those zones whereas there is much greater variability with the distances 

between individual events. 

 Further, zones have attributes which are properties of the zone, not of the individual 

events. The attribute can be a count or a continuous variable for a distributional property of the 

zone (e.g., median household income; percentage of households below poverty level).2 

Analysis then proceeds on the basis of the zonal information. The results will be 

different than for an analysis of the individual event information since the spatial characteristics 

are measured by single points for each zone (e.g., the centroid) and the attribute information is 

measured by a property of the zone, not the individual events (e.g., the count of events in the 

zone; a characteristic of the zone such as income level). 

In other words, the user must realize that an analysis of zonal data is quite different from 

an analysis of individual data and that the conclusions might be different. Aggregating data to 

zones creates properties that may be different than those of individual events and that the 

relationships between variables at the zonal level also might be different than at the individual 

level. This is called an ecological relationship and there is a large literature on ecological 

inference and fallacies (see Freedman, 1999; Langbein & Lichtman, 1979). 

Individual level data can also have attributes. For example, Levine and Lee (2013) 

analyzed journey-to-crime distances for offenders in Manchester, England. In this case, the 

attribute variable was the distance traveled and the statistics discussed in this chapter are 

There is no fundamental difference between a count variable and a continuous interval or ratio variable 

since a real number can be converted into a count by multiplying by a power of 10 (e.g., 1.23 = 123 x 10-2). 

The statistics discussed in this chapter are applicable to either count or continuous data. 

5.4 

2 
	 	 

appropriate for analyzing that attribute data. Other examples of individual level data with 

attributes would be the age of the offender, the number of prior convictions, or the number of 

years of formal education. The key criterion is that the records must have an attribute which is 

either a count or an interval variable. 

Spatial Autocorrelation Statistics for Attribute Data 

There are a number of formal statistics that attempt to measure spatial autocorrelation at 

the zonal level or for individual level data with count or interval attributes. These statistics 

include simple indices, such as the Moran=s I@, Geary=s C or the Getis-Ord “G” statistic, the 

application of these statistics to individual zones or records (discussed in Chapter 9), and 

multivariate indices such as the Markov Chain Monte Carlo spatial regression models (discussed 

in Chapter 19). The simple indices attempt to identify whether spatial autocorrelation exists for 

a single variable while the more complicated indices attempt to estimate variability in spatial 

autocorrelation in a study area of the effect of spatial autocorrelation on a particular attribute 

variable. 

CrimeStat includes three global indices - Moran=s I statistic, Geary=s C statistic, and the 

Getis-Ord “G” statistic. It also includes Correlograms that apply each of these indices to 

different distance intervals. Moran, Geary, and Getis-Ord are global in that they represent a 

summary value for all the data points. In Chapter 9, we will present some local indicators of 

spatial autocorrelation that apply the Moran, Geary and Getis-Ord statistics to individual zones. 

But, for now, we are focused on describing the entire study area. 

Moran=s “I” Statistic 

Moran=s “I” statistic (Moran, 1950) is one of the oldest indicators of spatial 

autocorrelation. It is applied to zones or points that have attribute variables associated with them 

(intensities). For any continuous variable, Xi, a mean,ܺത, can be calculated and the deviation of 

any one observation from that mean, ݏ௑, can also be calculated. The statistic then compares the 

value of the variable at any one location with the value at all other locations (Ebdon, 1988; 

Griffith, 1987; Anselin, 1992). Formally, it is defined as: 

 (5.1)

where N is the number of cases, Xi is the value of a variable at a particular location, i, Xj is the 

value of the same variable at another location (where i =/ j), X is the mean of the variable and Wij

is a weight applied to the comparison between location i and location j. 

5.5 
 

 

In Moran=s initial formulation, the weight variable, Wij, was a contiguity matrix. If zone j 

is adjacent to zone i, the interaction receives a weight of 1. Otherwise, the interaction receives a 

weight of 0. Cliff and Ord (1973) generalized these definitions to include any type of weight. In 

more current use, Wij, is a distance-based weight which is the inverse distance between locations 

i and j (1/dij). CrimeStat uses this interpretation. Essentially, it is a weighted Moran=s I where the 

weight is an inverse distance. 

Note that in adopting a distance-based weight, there are advantages and disadvantages. 

Contiguity (or adjacency) is a property of a zone, not a point. Thus, adjacency defines whether 

one zone is next to another zone whereas distance is the distance between single points that 

represent the zones (e.g., centroids). If two zones are, say, 0.25 miles apart, it is not known 

whether they are adjacent or not. In other words, in adopting a distance-based weight, 

information about adjacencies is lost. On the other hand, a distance-based weight is 

standardized. If two zones are adjacent, it is not known how far apart they are separated. 

Adjacencies can be misleading since they don’t indicate the size of the adjacent zones whereas a 

specified distance is always constant. 

The weighted Moran=s I is similar to a correlation coefficient in that it compares the sum 

of the cross-products of values at different locations, two at a time, weighted by the inverse of 

the distance between the locations and with the variance of the variable. Like a correlation 

coefficient, it typically varies between -1.0 and + 1.0. However, this is not absolute as an 

example later in the chapter will show. When nearby points have similar values, their crossproduct is high. Conversely, when nearby points have dissimilar values, their cross-product is 

low. Consequently, an AI@ value that is high indicates more spatial autocorrelation than an AI@

that is low. 

 However, unlike a correlation coefficient, the theoretical value of the index does not equal 

0 for lack of spatial dependence, but instead is negative but very close to 0: 

 (5.2)

Values of AI@ above the theoretical mean, E(I), indicate positive spatial autocorrelation 

while values of AI@ below the theoretical mean indicate negative spatial autocorrelation. 

Adjust for Small Distances 

CrimeStat calculates the weighted Moran=s I formula using equation 5.1. However, there 

is one problem with this formula that can lead to unreliable results. The distance weight between 

5.6 
 

 

 

 

 

two locations, Wij, is defined as the reciprocal of the distance between the two points, consistent 

with Moran’s original formulation: 

 (5.3)

Unfortunately, as dij becomes small, then Wij becomes very large, approaching infinity as 

the distance between the points approaches 0. If the two zones were next to each other, which 

would be true for two adjacent blocks for example, then the pair of observations would have a 

very high weight, sufficient to distort the AI@ value for the entire sample. Further, there is a scale 

problem that alters the value of the weight. If the zones are police precincts, for example, then 

the minimum distance between precincts will be a lot larger than the minimum distance between 

a smaller geographical unit, such as a block. We need to take into account these scales. 

CrimeStat includes an adjustment for small distances so that the maximum weight can 

never be greater than 1.0. The adjustment scales distances to one mile, which is a typical 

distance unit in the measurement of crime incidents. When the small distance adjustment is 

turned on, the minimal distance is automatically scaled to be one mile. The formula used is: 

 (5.4)

in the units are specified. For example, if the distance units, dij, are calculated as feet, then: 

where 5,280 is the number of feet in a mile. This has the effect of insuring that the weight of a 

particular pair of point locations will not have an undue influence on the overall statistic. The 

traditional measure of AI@ is the default condition in CrimeStat, but the user can turn on the small 

distance adjustment by clicking on the appropriate box. 

Testing the Significance of Moran=s “I” 

The empirical distribution can be compared with the theoretical distribution by dividing 

by an estimate of the theoretical standard deviation: 

 (5.5)

where AI@ is the empirical value calculated from a sample, E(I) is the theoretical mean of a 

random distribution and SE(I) is the theoretical standard deviation of E(I). 

5.7 
 

 

 

 

There are several interpretations of the theoretical standard deviation that affect the 

particular statistic used for the denominator as well as the interpretation of the significance of the 

statistic (Anselin, 1992). The most common assumption is that the standardized variable, Z(I), 

has a sampling distribution which follows a standard normal distribution, that is with a mean of 0 

and a variance of 1. This is called the normality assumption.3

 A second interpretation assumes 

that each observed value could have occurred at any location, that is the location of the values 

and their spatial arrangement is assumed to be unrelated. This is called the randomization 

assumption and has a slightly different formula for the theoretical standard deviation of 5.13.4 

CrimeStat outputs the Z-values and p-values for both the normality and randomization 

assumptions. 

Example: Testing Houston Burglaries with Moran=s “I” 

To illustrate the use of Moran=s I with point locations, the data must have intensity values 

associated with each point. Since most crime incidents are represented as a single point, they do 

not naturally have associated intensities. It is necessary, therefore, to adapt crime data to fit the 

form required by Moran=s I. One way to do this is assign crime incidents to geographical zones 

and count the number of incidents per zone. 

Figure 5.2 shows 2006 burglaries in the City of Houston by individual Traffic Analysis 

Zones (TAZ). TAZ’s are groupings of census blocks but designed to equalize the number of 

trips to and from the zone in the base year. They are typically very small in downtown Houston 

(typically a block in size) and much larger in the suburban parts of the City. With a GIS 

program, 26,480 burglary locations were overlaid on top of a map of 1,179 TAZ’s and the 

number of burglaries within each TAZ were counted and then assigned to the TAZ as a variable 

(see the >Assign primary points to secondary points= routine in Chapter 6).5 The numbers varied 

from 0 burglaries (for 250 TAZ’s) up to 284 burglaries incidents (for 1 TAZ). The map shows 

the plot of the number of burglaries per TAZ. 

3 The theoretical standard deviation of AI@ under the assumption of normality is (Ebdon, 1985): 

5 The TAZ data were obtained from the Houston-Galveston Area Council, the Metropolitan Planning 

Organization for the Houston metro area. 

5.8 

4 

 The formula for the theoretical standard deviation of AI@ under the randomization assumption is (Ebdon, 

1985): 
Figure 5.2:
 

Clearly, aggregating incident locations to zones, such as TAZ’s, eliminates some 

information since all incidents within a block are assigned to a single location (the centroid of the 

block). The use of Moran=s I, however, requires the data to be in this format. Using data in this 

form, Moran=s I was calculated using the small distance adjustment because many TAZ’s are 

very close together, especially in downtown Houston. 

Figure 5.3 shows the output of the “I” in CrimeStat. AI@ was 0.251790, the theoretical 

value of AI@ as -0.000849, and the standard error of “I” as 0.002796. The test of significance 

using the normality assumption gave a Z-value of 213.20, a highly significant value. Below are 

the calculations for burglaries by TAZ: 

Comparing Moran’s “I” for Two Distributions 

Figure 5.4 shows the distribution of households in the city by TAZ. The calculations for 

the “I” of households are similar (not shown). It turns out that the “I” of households is 0.298117 

while the theoretical “I” and the standard error of “I” are the same as for burglaries (because of 

the same zonal geography). One can compare an “I” value for one distribution with the “I” value 

for another distribution. For example, a Z-test can then be made of whether the “I” value of 

burglaries is statistically different than that of households. The calculations are shown below: 

where Iburg is the “I” value for burglaries, Ihh is the AI@ value for households, and SE(I) is the 

standard deviation of AI@ for households under the assumption of normality. The Z-test of the 

difference is -16.57, a highly significant difference. The high Z-value suggests that burglaries are 

even more clustered than the clustering of households. To put it another way, they are more 

clustered than would be expected based on the household distribution. As mentioned, this is an 

approximate test since the joint distribution of AI@ for two empirical distributions of AI@ is not 

known. 

Geary=s C Statistic 

Geary=s C statistic is similar to Moran=s I (Geary, 1954). In this case, however, the 

interaction is not the cross-product of the deviations from the mean, but the deviation in 

intensities of each observation’s location with one another. It is defined as: 

5.10 
Figure 5.3: 

Moran's I Statistic Output 
Figure 5.4:
 

 

 

(5.6)

where N is the number of cases, Xi is the value of a variable at a particular location, i, Xj is the 

value of the same variable at another location (where i =/ j), X is the mean of the variable and Wij

is a weight applied to the comparison between location i and location j.

The values of “C” typically vary between 0 and 2, although 2 is not a strict upper limit 

(Griffith, 1987). The theoretical value of “C” is 1; that is, if values of any one zone are spatially 

unrelated to any other zone, then the expected value of “C” would be 1. Values less than 1 (i.e., 

between 0 and 1) typically indicate positive spatial autocorrelation while values greater than 1 

indicate negative spatial autocorrelation. Thus, this index is inversely related to Moran=s “I”. It 

will not provide identical inference because it emphasizes the differences in values between pairs 

of observations comparisons rather than the co- variation between the pairs (i.e., product of the 

deviations from the mean). The Moran coefficient gives a more global indicator whereas the 

Geary coefficient is more sensitive to differences in small neighborhoods. 

Adjusted “C”

A more intuitive interpretation of “C” can be obtained by calculating an adjusted“C”: 

 (5.7) ܥ െ 1 ൌ ܥ	݁݀ݐݏݑ݆݀ܣ

In this case, the adjusted “C” will be on the same scale as Moran’s “I”. An adjusted “C”

value that is positive indicates positive spatial autocorrelation while an adjusted “C” value that is 

negative indicates negative spatial autocorrelation. An adjusted “C” of 0 indicates no spatial 

autocorrelation and is also the expected adjusted “C”. CrimeStat calculates both the regular and 

adjusted “C” values. 

Adjust for Small Distances

Like Moran=s “I”, the weights are defined as the inverse of the distance between the 

paired points: 

 (5.3) repeat

However, the weights will tend to increase substantially as the distance between points 

decreases. Consequently, a small distance adjustment is allowed that ensures no weight is 

greater than 1.0: 

5.13
 

 

 

 

(5.4) repeat

The adjustment scales the distances to one mile in the distance units specified on the 

Primary file page (miles, feet, kilometers, meters, or nautical miles). This is the default 

condition although the user can calculate all weights as the reciprocal distance by turning off the 

small distance adjustment. 

Testing the Significance of Geary=s “C”

The empirical “C” distribution can be compared with the theoretical distribution by 

dividing by an estimate of the theoretical standard deviation 

 (5.8)

where C is the empirical “C”, E(C) is the theoretical mean of a random distribution and SE(C) is 

the theoretical standard deviation of E(C). The usual test is to assume that the sample Z follows 

a standard normal distribution with mean of 0 and variance of 1 (normality assumption), though 

it is possible to calculate the standard error under a randomization assumption (Ripley, 1981).6

Note that for testing, the regular “C” value should be used since an adjusted standard 

error of “C” is not easily calculated. The adjusted “C” is useful for a quick intuitive appraisal as 

well as for the Geary Correlogram (see below). 

Example: Testing Houston Burglaries with Geary=s “C”

Using the same data on burglaries in the City of Houston, figure 5.5 illustrates the output. 

The regular “C” value for burglaries was 0.625702 with a Z-value of -20.00 (p#.0001). The “C” 

value of burglaries is smaller than the theoretical “C” of 1. Converting this measure an adjusted 

“C” gives 0.374298 and indicates positive spatial autocorrelation. That is, the index suggests that 

TAZ’s with a high number of burglaries are adjacent to TAZ’s also with a high number of 

burglaries. Thus, Geary=s C confirms the evidence for positive spatial autocorrelation identified 

by Moran’s “I”. 

The theoretical standard deviation for C under the normality assumption is (Ripley, 1981):

5.14

6 
Figure 5.5: 

Geary's C Statistic Output 
 

 

Comparing this to the distribution of households in Houston, the C value of households is 

also below the theoretical “C” of 1 and points to positive spatial autocorrelation (“C” = 

0.643120 with a Z-value of -19.07; p#.0001). Since both the regular “C” for burglaries and for 

households are below 1 (hence, indicating positive spatial autocorrelation), let us test the 

difference between the two as indicated by a Z-test of the difference. 

In this case, there is no statistical difference between the distribution of burglaries and the 

distribution of households. Though both distributions show evidence of positive spatial 

autocorrelation, the Geary test cannot show a difference between the two whereas the Moran’s 

“I” did show a difference. 

Typically, Geary’s “C” will be consistent with Moran’s “I” though there are slight 

differences between the indices, as we see in this example. Because of the nature of the 

weighting, the Geary index is more sensitive to local clustering (second-order effects) than the 

Moran index, which is better seen as measuring first-order spatial autocorrelation. This 

illustrates how these indices have to be used with care and cannot be generalized by themselves. 

Each of them emphasizes slightly different information regarding spatial autocorrelation, yet 

neither is sufficient by itself. They should be used as part of a larger analysis of spatial 

patterning.7 

Getis-Ord AG@ Statistic 

The Getis-Ord AG@ statistic is also an index of global spatial autocorrelation but for 

values that fall within a specified distance of each other (Ord & Getis, 1995; Getis & Ord, 1992). 

When compared to an expected value of AG@ under the assumption of no spatial association, it 

has the advantage over other two global spatial autocorrelation measures in that it can distinguish 

between >hot spots= and >cold spots=, which neither Moran=s AI@ nor Geary=s AC@ can do. 

The AG@ statistic calculates the spatial interaction of the value of a particular variable in a 

zone with the values of that same variable in nearby zones, similar to Moran=s AI@ and Geary=s 

Anselin (1992) points out that the results of the two indices are determined to a large extent by the type of 

weighting used. In the original formulation, where adjacent weights of 1 and 0 were used, the two indices 

were linearly related, though moving in opposite directions (Griffith, 1987). Thus, only adjacent zones had 

any impact on the index. With inverse distance weights, however, zones farther removed can influence the 

overall index so it is possible to have a situation whereby adjacent zones have similar values (hence, are 

positively autocorrelated) whereas zones farther away could have dissimilar values (hence, are negatively 

autocorrelated). 

5.16 

7 
 

AC@. Thus, it is also a measure of spatial association or interaction. Unlike the other two 

measures, it only identifies positive spatial autocorrelation, that is, where zones have similar 

values to their neighbors. It cannot detect negative spatial autocorrelation where zones have 

different values to their neighbors. But, unlike the other two global measures, it can distinguish 

between positive spatial autocorrelation where zones with high values are near to other zones 

with high values (high positive spatial autocorrelation) from positive spatial autocorrelation 

which where zones with low values are near to other zones also with low values (low positive 

spatial autocorrelation). Further, the AG@ value is calculated with respect to a specified search 

distance (defined by the user) rather than to an inverse distance, as with the Moran=s AI@ or 

Geary=s AC@. 

The formulation of the general AG@ statistic presented here is taken from Lee and Wong 

(2005). It is defined as: 

 (5.9)

for a variable, X. This formula indicates that the cross-product of the value of X at location Ai@

and at another zone Aj@ is weighted by a distance weight, wj(d) which is defined by either a >1' if 

the two zones are equal to or closer than a threshold distance, d, or A0" otherwise. The crossproduct is summed for all other zones, j, over all zones, i. Thus, the numerator is a sub-set of 

the denominator and can vary between 0 and 1. If the distance selected is too small so that no 

other zones are closer than this distance, then the weight will be 0 for all cross-products of 

variable X. Hence, the value of G(d) will be 0. Similarly, if the distance selected is too large so 

that all other zones are closer than this distance, then the weight will be 1 for all cross-products 

of variable X. Hence, the value of G(d) will be 1. 

There are actually two “G” statistics. The first one, G*, includes the interaction of a zone 

with itself; that is, zone Ai@ and zone Aj@ can be the same zone. The second one, G, does not 

include the interaction of a zone with itself. In CrimeStat, we only include the “G” statistic (i.e., 

there is no interaction of a zone with itself) because, first, the two measures produce almost 

identical results and, second, the interpretation of “G” is more straightforward than with G*. 

Essentially, with G, the statistic measures the interaction of a zone with nearby zones (a 

>neighborhood=). See articles by Getis and Ord (1996) and by Khan, Qin and Noyce (2006) for a

discussion of the use of G*.

5.17 
 

 

 (5.10)

 (5.11) 

 

 

 

Testing the Significance of “G” 

By itself, the “G” statistic is not very meaningful. Since it can vary between 0 and 1, as 

the threshold distance increases, the statistic will always approach 1.0. Consequently, “G” is 

compared to an expected value of “G” under no significant spatial association. The expected 

“G” for a threshold distance, d, is defined as: 

where W is the sum of weights for all pairs and N is the number of cases. The sum of the 

weights is based on symmetrical counts of those zones within the threshold distance. That is, if 

zone 2 is within the threshold distance of zone 1, then zone 2 contributes a weight of 1 to zone 1. 

However, zone 1 contributes a weight of 1 to zone 2 as well. In other words, if two zones are 

within the threshold (search) distance, then they both contribute 2 to the total weight. 

Note that, since the expected value of “G” is a function of the sample size and the sum of 

weights which, in turn, is a function of the search distance, it will be the same for all variables of 

a single data set in which the same search distance is specified. However, as the search distance 

changes, so will the expected “G” change. 

Theoretically, the “G” statistic is assumed to have a normally distributed standard error. 

If this is the case (and we often do not know if it is), then the standard error of “G” can be 

calculated and a simple significance test based on the normal distributed be constructed. The 

variance of G(d) is defined as: 

where 

 (5.12)

and where: 

(5.13)

(5.14)

(5.15)

(5.16)

 (5.17) 

5.18 
 

 

 

 

 

 

 

 

 

 

(5.18)

(5.19) 

(5.20) 

(5.21) 

(5.22) 

(5.23) 

(5.24) 

where i is the zone being calculated, j is all other zones, and N is the sample size (Lee and Wong, 

2005). Note that this formula is different than that written in other sources (e.g., see Lees, 2006) 

but is consistent with the formulation by Getis and Ord (1992; 1993). 

The standard error of G(d) is the square root of the variance of G. Consequently, a Z-test 

can be constructed by: 

 (5.25) 

(5.26)

Relative to the expected value of G, a positive Z-value indicates spatial clustering of high 

values (high positive spatial autocorrelation or >hot spots=) while a negative Z-value indicates 

spatial clustering of low values (low positive spatial autocorrelation or >cold spots=). A AG@ value 

around 0 typically indicates either no positive spatial autocorrelation, negative spatial 

autocorrelation (which the Getis-Ord cannot detect), or that the number of >hot spots= more or 

less balances the number of >cold spots=. 

Note that the value of this test will vary with the search distance selected. One search 

distance may yield a significant spatial association for “G” whereas another may not. In other 

words, the statistic is useful for identifying distances at which spatial autocorrelation exists. 

In practice, one should use a small search 

distance to identify local spatial autocorrelation. 

Also, and this is an important point, the expected value of “G” as calculated in equation 

5.10 is only meaningful if the variable is positive. For variables with negative values, such as 

residual errors from a regression model, one cannot use equation 5.10 but, instead, must use a 

simulation to estimate confidence intervals. 

5.19
 

 

Simulating Confidence Intervals for “G”

One of the problems with this test is that “G” may not actually follow a normal standard 

error. That is, if “G” was calculated for a specific distance, d, with random data, the distribution 

of the statistic may not be normally distributed. This would be especially true if the variable of 

interest is a skewed variable with some zones having very high values while the majority of 

zones having low values. 

Consequently, the user has an alternative for estimating the confidence intervals using a 

Monte Carlo simulation. In this case, a permutation type simulation is run whereby the original 

values of the intensity variable, Z, are maintained but are randomly re-assigned for each 

simulation run (Anselin, 2008). This will maintain the distribution of the variable Z but will 

estimate the value of “G” under random assignment of this variable. The user can take the usual 

95% or 99% confidence intervals based on the simulation. 

Keep in mind that a simulation may take time to 

run especially if the data set is large or if a large 

number of simulation runs are requested. 

Example: Testing Simulated Data with the Getis-Ord AG@ 

To understand how the Getis-Ord AG@ works and how it compares to the other two global 

spatial autocorrelation measures - Moran=s AI@ and the adjusted Geary=s AC@, three simulated data 

sets were created. In the first, a random pattern was created (Figure 5.6). In the second, a data 

set showing positive spatial autocorrelation was created (Figure 5.7) and, in the third, a data set 

showing negative spatial autocorrelation was created (Figure 5.8). 

Table 5.1 compares the three global spatial autocorrelation statistics on the three 

distributions. For the Getis-Ord AG@, both the actual AG@ and the expected AG@ are shown. A one 

mile search distance was used for the Getis-Ord AG@. 

5.20



 

 

 

 

 

 

_____________________ 

Table 5.1:

Global Spatial Autocorrelation Statistics for Simulated Data Sets 

N = 100 Grid Cells

 -------------Getis-Ord “G”------------

 Adjusted

Pattern Moran=s AI@ Geary=s AC@ Observed AG@ Expected AG@ 

(1 mi search) (1 mi search) 

Random -0.007162 n.s. 0.034722n.s 0.151059n.s 0.159596 

Positive spatial 

autocorrelation 0.061449**** 0.166734  0.230586** * 0.159596 

Negative spatial 

autocorrelation -0.041719* -0.016479n.s. 0.140833n.s. 0.159596 

n.s not significant

* p#.05 

** p#.01 

*** p#.001 

  p#.0001 

The random pattern is not significant for all three measures. That is, neither the Moran 

AI@, the adjusted Geary@C@, nor the Getis-Ord AG@ are significantly different than the expected 

value under a random distribution. This is what would be expected since the data were assigned 

randomly. 

For the positive spatial autocorrelation pattern, on the other hand, all three measures 

show highly significant differences with a random distribution. Moran=s AI@ is highly positive. 

The adjusted Geary=s AC is above 0, indicating positive spatial autocorrelation and the Getis-Ord 

AG@ has a AG@ value that is significantly higher than the expected AG@ based on the theoretical 

standard error. The Getis-Ord AG@, therefore, indicates that the type of spatial autocorrelation is 

high positive. 

Finally, the negative spatial autocorrelation pattern (Figure 5.8 above) shows different 

results for the three measures. Moran=s AI@ shows negative spatial autocorrelation and is 

significant (p≤.05). Geary=s AC also shows negative spatial autocorrelation but is not significant. 

Finally, the Getis-Ord AG@ is slightly smaller than the expected AG@, which indicates low positive 

spatial autocorrelation, but it is not significant. 

5.24
 

In other words, all three statistics can identify positive spatial correlation. Of these, 

Moran=s AI@ is a more powerful test than either Geary=s AC@ or the Getis-Ord AG@. By ‘power’ is 

meant the ability to correctly reject a false null hypothesis (or, in statistical language, to avoid a 

Type II error). A data set for which Moran’s “I” is barely statistically significant might very well 

fail with Geary’s “C” or the Getis-Ord “G” since the Geary and Getis-Ord indices are not a 

powerful as the Moran index. 

However, only Moran=s AI@ and Geary=s AC@ are able to detect negative spatial 

autocorrelation, the latter barely. On the other hand, only the Getis-Ord AG@ can distinguish 

between high positive and low positive spatial autocorrelation. The Moran and Geary tests 

would show these conditions to be identical, as the example below shows. 

Example: Testing Houston Burglaries with the Getis-Ord AG@ 

Now, let us take the 26,480 burglaries in the City of Houston for 2006 aggregated to 

1,179 traffic analysis zones (figure 5.2 above). To compare the Getis-Ord AG@ statistic with the 

Moran=s AI@ and the regular Geary=s AC@, the three spatial autocorrelation tests were run on this 

data set. The Getis-Ord AG@ was tested with a search distance of 1 mile and 1000 simulation runs 

were made on the AG@. Table 5.2 shows the three global spatial autocorrelation statistics for 

these data.

The Moran and Geary tests show that the Houston burglaries have significant positive 

spatial autocorrelation (zones have values that are similar to their neighbors). Moran=s AI@ is 

significantly higher than the expected AI@ and the adjusted Geary=s AC@ is also significantly higher 

than the adjusted expected AC@. However, the Getis-Ord AG@ is lower than the expected AG value 

and is significant whether using the theoretical Z-test or the simulated confidence intervals 

(notice how the AG@ is lower than the 2.5 percentile). This indicates that, in general, zones with 

low values are nearby other zones with low values. In other words, there is low positive spatial 

autocorrelation, suggesting a number of >cold spots=. 

Uses and Limitations of the Getis-Ord AG@ 

The advantage of the AG@ statistic over the other two spatial autocorrelation measures is 

that it can distinguish between >hot spots= and >cold spots=. With Moran=s AI@ or Geary=s AC@, an 

indicator of positive spatial autocorrelation means that zones have values similar to their 

neighbors. However, the positive spatial autocorrelation could be caused by many zones with 

low values being concentrated, too. In other words, one cannot tell from those two indices 

whether the concentration is a hot spot or a cold spot. The Getis-Ord AG@ can do this. 

5.25
 

 

 

 

 

 

 

Table 5.2: 

Global Spatial Autocorrelation Statistics for City of Houston Burglaries: 2001

N = 1,179 Traffic Analysis Zones 

Adjusted 

Moran=s AI@ Geary=s AC@ Getis-Ord AG@ 

(1 mile search) 

Observed 0.251790 0.374298 0.007063 

Expected

Observed –Expected

Standard Error 

-0.000849 

0.252639 

0.002796 

0.000000 

0.374298 

0.018717 

0.061753 

-0.054690 

0.007491 

Z-test 90.36 20.00 -7.30 

p-value

Based on simulation: 

** **   ** ** 

2.5 percentile:

97.5 percentile: 

---

---

---

---

0.048664 

0.076445 

_____________________ 

n.s not significant

* p#.05 

** p#.01 

*** p#.001 

**** p#.0001

The main limitation of the Getis-Ord AG@ is that it cannot detect negative spatial 

autocorrelation, a condition that, while rare, does occur. With the haphazard pattern above 

(Figure 5.8), this test could not detect that there was negative spatial autocorrelation. For this 

condition, Moran=s AI@ or possibly Geary=s AC@ would be more appropriate tests. In the example, 

Geary’s “C” did not detect it but Moran’s “I” did 

Moran Correlogram

Moran=s “I”, Geary=s “C”, and the Getis-Ord “G” indices are summary tests of global 

autocorrelation. That is, they summarize all the data with respect to spatial autocorrelation but 

do not distinguish different subsets. For examining particular sub-sets of data that are spatially 

autocorrelated, such as ‘hot spots’, ‘cold spots’ or space-time clusters, a different approach is 

required. Chapter 9 discusses the local Moran and local Getis-Ord statistics. 

An alternative approach is to calculate the spatial autocorrelation statistics by different 

distance intervals. The Moran Correlogram calculates the AI@ value by different distance 

intervals (or bins). When graphed, the plot indicates how concentrated or distributed is the 

spatial autocorrelation (Cliff and Haggett, 1988; Bailey and Gatrell, 1995). Essentially, a series 

of concentric circles is overlaid on the points and the Moran=s I statistic is calculated for only 

5.26
those points falling within each circle. The radius of the circle changes from a small circle to a 

very large one. As the circle increases, the AI@ value approaches the global value. 

In CrimeStat, the user can specify how many distance intervals (i.e., circles) are to be 

calculated. The default is 10, but the user can choose any other integer value. The routine takes 

the maximum distance between points and divides it into the number of specified distance 

intervals, and then calculates the AI@ for those points falling within that radius. 

Adjust for Small Distances 

If the ‘Adjust for small distances’ box is checked, small distances are adjusted so that the 

maximum weighting is 1 (equation 5.4 above). This ensures that the AI@ values for individual 

distances will not become excessively large or excessively small for points that are close 

together. The default value is no adjustment. 

Simulation of Confidence Intervals 

A permutation Monte Carlo simulation can be run to estimate approximate confidence 

intervals around the "I" value. Each simulation inputs random data and calculates the AI@ value. 

The distribution of the random AI@ values produce an approximate confidence interval for the 

actual (empirical) AI@. To run the simulation, specify the number of simulations to be run (e.g., 

100, 1000, 10000). The default is no simulations. The output percentiles are the 0.5th, 2.5th, 

97.5th and 99th. Pairing the 2.5th with the 97.5th or the 0.5th with the 99.will create approximate 

95% or 99% confidence intervals. 

Example: Moran Correlogram of Baltimore County Vehicle Theft and Population 

For the three correlograms, we will use a different example than Houston burglaries. 

These are 1996 data on vehicle thefts from Baltimore County, MD. Figure 5.9 shows the 

distribution of 1996 vehicle thefts by Traffic Analysis Zones (TAZ) while figure 5.10 shows the 

Moran Correlogram for these thefts. Also shown in the graph are the maximum and minimum 

values from a Monte Carlo simulation of 1000 runs and the 2.5th and 97.5th percentiles to 

simulate approximate 95% confidence intervals (called ‘credible intervals’). 

As seen, the AI@ value at zero distance is about 0.60. As the distance between zones 

increase (i.e., the search circle radius gets larger), the AI@ value drops off slowly until about 19 

miles whereupon it approaches the global AI@ value. Further, the curve for the “I” values is 

always higher than the 97th percentile curve from the random simulation and indicating that 

vehicle thefts are more clustered than what would be expected on the basis of chance for all 

5.27 
Figure 5.9: 
Figure 5.10: 

0.80

Moran Correlogram: 

Baltimore County Vehicle Theft: 1996 

"I" with 95% Confidence Intervals (N=1000 Monte Carlo Simulations) 

Figure 5.10: 

0.400.60

Vehicle theft 

I(97.5 percentile)

0.000.20

Moran's "I" 0 5 10 15 20 25 3

I(maximum)

-0.40-0.20

I(minimum)

I(2.5 percentile)

-0.80-0.60

Distance (miles)
 

distance separations. In other words, vehicle thefts appear to be highly clustered, much more so 

than would be expected by chance. 

Now, compare this distribution with that the 1996 population (Figure 5.11). The 1996 

data were estimated by the Baltimore Metropolitan Council, the regional planning agency. 

Comparing this map with Figure 5.9, intuitively it can be seen that population is more dispersed 

than vehicle thefts. Consequently, the Moran Correlogram shows much less spatial 

autocorrelation. The “I” value for zero distance is 0.39, lower than the 0.60 for vehicle thefts. 

The graph then drops off very quickly and approaches the global “I” value at about 3 miles. 

Further, from about 2 miles on, the “I” value is not different than what might be expected by 

chance since the curve falls between the 2.5th percentile and the 97.5th percentile. In other 

words, nearby TAZ’s tend to have similar population levels, but there is no relationship between 

the population of TAZ’s and those farther away. 

Figure 5.13 compares the Moran Correlogram of vehicle theft with that of population by 

looking at only the positive “I” values. As seen, vehicle theft has a much higher AI@ value for 

short distances than for population. The reason is most likely that a disproportionate number of 

vehicle thefts occur in commercial areas which, in turn, are more concentrated than the 

distribution of population. 

Uses and Limitations of the Moran Correlogram 

In other words, the Moran Correlogram provides information about the scale of spatial 

autocorrelation, whether it is more concentrated (as with the vehicle theft example) or more 

diffuse (as with the population example). This can be useful for gauging the extent to which 

>hot spots= are truly isolated concentrations of incidents or whether they are by-products of 

spatial clustering over a larger area. In Chapter 7, we will examine a clustering algorithm that 

examines a hierarchy of clusters (e.g., first-order clusters that are within larger second-order 

clusters which, in turn, are within even larger third-order clusters). The Moran Correlogram 

provides a quick snapshot of the extent of spatial autocorrelation as a function of scale. 

A second use for the Moran Correlogram is to estimate the type of kernel function that will be 

used for interpolation. In Chapter 8, this methodology is explained in detail. But, the key 

decision is to select a mathematical function that will interpolate data from point locations to 

grid cells. The shape of the Moran Correlogram and the spread is a good indicator of the type 

of mathematical function to use. 

5.30 
Figure 5.11: 
Figure 5.12: 

0.80

Moran Correlogram: 

Baltimore County Population: 1996 

Figure 5.12: 

"I" with 95% Confidence Intervals (N=1000 Monte Carlo Simulations) 

0.400.60 Population 

I(maximum)

0.000.20

Moran's "I" 0 5 10 15 20 25 3

I(97.5 percentile)

-0.40-0.20

I(minimum)

I(2.5 percentile)

-0.80-0.60

Distance (miles)
Figure 5.13: 

0.70

Two Moran Correlograms of Baltimore County 

Vehicle Theft & Population: 1996 

Figure 5.13: 

0.500.60

Vehicle theft 

0.300.40

Moran's "I" 

Population 

0.100.200.00

0 5 10 15 20 25 3

Distance (miles)
 

A third use for the Moran Correlogram is in identifying the degree of decline in spatial 

autocorrelation with distance (sometimes called distance decay) in choosing an appropriate 

parameter for spatial regression models. Chapter 19 will discuss this methodology. 

On the other hand, like all global spatial autocorrelation statistics, the Correlogram will 

not indicate where there is clustering or dispersion, only that it exists. For that, we will have to 

examine tools that focus on concentrated events (or the opposite, the lack of concentration). 

Geary Correlogram 

The Geary Correlogram is similar to the Moran Correlogram in that it calculates the 

Geary AC@ index for different distance intervals/bins. The user can select any number of distance 

intervals. The default is 10 distance intervals. The size of each interval is determined by the 

maximum distance between zones and the number of intervals selected. The output includes 

both the regular “C” and the adjusted “C”. The graph presented on the results tab show the 

adjusted “C” since this is more intuitive and can be compared to the Moran Correlogram. 

Adjust for Small Distances 

If the ‘Adjust for small distances’ box is checked, small distances are adjusted so that 

the maximum weighting is 1 (see equation 5.4 above.) This ensures that the AC@ values for 

individual distances won't become excessively large or excessively small for points that are 

close together. The default value is no adjustment. 

Geary Correlogram Simulation of Confidence Intervals 

Since the Geary=s AC@ statistic may not be normally distributed, the significance test is 

frequently inaccurate. Instead, a permutation Monte Carlo simulation is run whereby the 

original values of the variable, Z, are maintained but are randomly re-assigned for each 

simulation run. This will maintain the distribution of the variable Z but will estimate the value 

of AC@ under random assignment of this variable. Specify the number of simulations to be run 

(e.g., 1000, 5000, 10000). Note, a simulation may take time to run especially if the data set is 

large or if a large number of simulation runs are requested. 

Example: Geary Correlogram of Baltimore County Vehicle Thefts 

Using the same data set on the Baltimore County vehicle thefts as shown in Figure 5.9 

above, the Geary Correlogram was run with 100 intervals (bins). The routine was also run with 

1000 simulations to estimate confidence intervals around the AC@ value. Because it is more 

intuitive visually, the adjusted “C” was used instead of the regular “C”. 

5.34 
Figure 5.14 illustrates the distance decay of the adjusted AC@ as a function of distance 

along with the simulated 95% confidence interval. The theoretical adjusted AC@ under random 

conditions is also shown. As seen, the AC@ values are above 0 for all distances tested. However, 

when compared with the 2.5th and 97.5th percentiles from the simulated rescaled AC@ for all 

intervals, the adjusted “C” values are not outside these percentiles for the very short distances 

but are from about 1.5 miles separation or greater. In other words, the graph suggests that the 

distribution of “C” for nearby zones is not different than what would be expected by chance. 

Only with increasing distance is the distribution clearly more clustered than chance. 

This illustrates a subtle difference between the Geary and Moran indices. The Geary is 

more sensitive to local variations while the Moran reacts more to global variations. The Geary 

shows that there is positive spatial autocorrelation in vehicle theft for the immediate 

neighborhood around zones, but it is not much different than might be expected on chance. 

However, with increasing distance, positive spatial autocorrelation is shown. This suggests a 

type of sub-regional clustering of vehicle thefts; local clustering is limited but the events tend to 

be concentrated in only part of Baltimore County. As seen in Figure 5.9 above, the TAZ’s 

nearer the border with the City of Baltimore had much higher vehicle theft numbers than the 

rural parts of the County. 

The Geary Correlogram can also be used for comparison to other distributions, such as 

the comparison of vehicle theft with population as shown in Figure 5.13. This example will not 

be repeated here for the Geary Correlogram, but it does show that vehicle theft has higher “C” 

values than population over most distances, similar to the Moran Correlogram. 

Uses and Limitations of the Geary Correlogram 

Similar to the Moran and the Getis-Ord correlograms (see below), the Geary 

Correlogram is useful in order to determine the degree of spatial autocorrelation and how far 

away from each zone it typically extends. Since it is an average over all zones, it is a general 

indicator of the spread of the spatial autocorrelation. This can be useful for defining limits to 

search distances in other routines, such as the single kernel density interpolation routine where a 

fixed bandwidth would be defined to capture the majority of spatial autocorrelation. Its biggest 

limitation is that it is not as powerful a test as the Moran Correlogram. 

Getis-Ord Correlogram 

The Getis-Ord Correlogram calculates the Getis-Ord AG@ index for different distance 

intervals/bins. The statistic requires an intensity variable in the primary file and calculates the 

Getis-Ord AG@ index for different distance intervals/bins. The user can select any number of 

5.35 
Geary's "C"

Geary Correlogram: 

Baltimore County Vehicle Theft: 1996 

Figure 5.14:

Adjusted "C" with 95% Confidence Intervals from 1000 Monte Carlo Simulations 

Adjusted "C" with 95% Confidence Intervals from 1000 Monte Carlo Simulations 0.80

Vehicle theft 0.60 Vehicle theft 

C(97.5 percentile)

C(maximum)

C(97.5 percentile)

0.400.200.00 C(2.5 percentile)

0 5 10 15 20 25

C( ) minimum)

-0.20 C(2.5 percentile)-0.40

C(minimum)

-0.60-0.80

Distance (miles)

3
 

 

distance intervals. The default is 10 distance intervals. The size of each interval is determined 

by the maximum distance between zones and the number of intervals selected. 

Getis-Ord Correlogram Simulation of Confidence Intervals 

Since the Getis-Ord AG@ statistic may not be normally distributed, the significance test is 

frequently inaccurate. Instead, a permutation Monte Carlo simulation is run whereby the 

original values of the intensity variable, Z, are maintained but are randomly re-assigned for each 

simulation run. This will maintain the distribution of the variable Z but will estimate the value 

of “G” under random assignment of this variable. The user should specify the number of 

simulations to be run (e.g., 100, 1000, 10000). Note, a simulation may take time to run 

especially if the data set is large or if a large number of simulation runs are requested. 

If a simulation is run, percentiles for the 0.5th, 2.5th, 97.5th and 99th percentiles are 

provided. Pairing the 2.5th with the 97.5th or the 0.5th with the 99.will create approximate 95% 

or 99% confidence intervals. For the three correlograms, these statistics are provided for each 

of the distance bins. 

Example: Getis-Ord Correlogram of Baltimore County Vehicle Thefts 

Using the same data set on the Baltimore County vehicle thefts as in figure 5.9, the 

Getis-Ord Correlogram was run. The routine was run with 100 intervals and 1000 Monte Carlo 

simulations in order to simulate 95% confidence intervals around the AG@ value. The output was 

then brought into Excel to produce a graph. Figure 5.15 illustrates the distance decay of the AG@, 

the expected AG@, and the 2.5 and 97.5 percentile AG@ values from the simulation. 

Note that the AG@ value increases with distance from close to 0 to close to 1 at the largest 

distance, around 33 miles. The actual AG@ is higher than the expected AG@ for all distances until 

the maximum, indicating that there is consistent high positive spatial autocorrelation in the data 

set. Since the Getis-Ord can distinguish a hot spot from a cold spot, the excess of AG@ over the 

expected AG@ indicates that there are somes zones with substantial numbers of vehicle thefts. 

Notice how the expected AG@ also falls above the 97.5 percentile suggesting that there are more 

>hot spots= than >cold spots=. That is, if the zones were spatially re-arranged, then would not 

expect as much concentration as actually occurred. 

5.37 
Getis-Ord "G" 

1.00

Figure 5.15: Figure

Getis-Ord Correlogram: 

Baltimore County Vehicle Theft: 1996 

"G" with 95% Confidence Intervals from 1000 Monte Carlo Simulations 

Vehicle theft 

0.80 Theoretical random "G" 

V hi l th ft Vehicle theft 

0.60 G(97.5 percentile) Theoretical random "G" 0.40

G(2.5 percentile)

G(97.5 percentile)

G(2.5 percentile)

0.200.00

0 5 10 15 20 25 3

Distance (miles)
Uses and Limitations of the Getis-Ord Correlogram 

Similar to the Moran Correlogram and the Geary Correlogram, the Getis-Ord 

Correlogram is useful in order to determine the degree of spatial autocorrelation and how far 

away from each zone it typically extends. Since it is an average over all zones, it is a general 

indicator of the spread of the spatial autocorrelation. This can be useful for defining limits to 

search distances in other routines, such as the single kernel density interpolation routine or the 

MCMC spatial regression module (see Chapters 10 and 19). 

Unlike the other two correlograms, however, it can distinguish hot spots from cold spots. 

In the example above, there are more hot spots than cold spots since the AG@ is greater than the 

expected AG@ for all distances. The biggest limitation for the Getis-Ord Correlogram is that it 

cannot detect negative spatial autocorrelation whereby zones have different values from their 

neighbors. For that condition, which is rare, the other two correlograms should be used. 

Running the Spatial Autocorrelation Routines 

The six routines are defined on the Spatial Autocorrelation tab under spatial description. 

With the Moran and Geary routines, the user simply checks the box for each routine. If distance 

is to be adjusted for small distances, the user must check the appropriate box. For the Getis-Ord 

“G” routine, the user must specify a search distance and a unit of distance measurement (the 

default is 1 mile). For the three correlograms, the user must specify the number of intervals and 

the number of simulations that are to be run, if any. 

The output for the six routines is somewhat similar. For the three global indices, 

statistics are provided on the index (“I”, “C” or”G”) and the expected value. For the three 

correlograms, these statistics are provided for each of the distance bins. If a simulation is run, 

percentiles for the 0.5th, 2.5th, 97.5th and 99th percentiles are provided. Pairing the 2.5th with the 

97.5th or the 0.5th with the 99.will create approximate 95% or 99% confidence intervals. 

Guidelines for Examining Spatial Autocorrelation 

To summarize, a number of indices for examining spatial autocorrelation have been 

presented. These indices are used with data in which there is an attribute variable, a count or 

interval variable associated with specific locations. Typically, the indices are used with data on 

zones since zonal information is published by many different agencies. However, the indices 

could also be used with individual data if there are attributes associated with the individual 

records. 

5.39 
While there is no single way to utilize these indices, the following are suggestions for 

using them. First, identify whether there is positive spatial autocorrelation using Moran’s “I” 

and Geary’s “C”. Positive spatial autocorrelation indicates that zones are located near to other 

zones with similar values, either zones with high values on the variable being located near to 

zones also with high values or the opposite condition (low values nearby other low values). 

If both the Moran “I” and Geary “C” (either regular or adjusted values) are both 

significant, this is strong evidence that there is sizeable spatial autocorrelation in the data. 

Whether the spatial autocorrelation is due to global (regional) factors or local clustering cannot 

be easily determined from the indices. On the other hand, if the Moran is significant, but the 

Geary is not, this could indicate that the clustering is a function of global concentration rather 

than local concentration since the Moran index is more sensitive to region-wide variation in the 

variable. 

If there is negative spatial autocorrelation, which does occasionally happen, this 

indicates that zones with high values are located near to zones with low values, or the opposite. 

The user is advised to use one of the hot spot techniques described in Chapters 7, 8 and 9 to see 

if the hot spots can be isolated. 

Second, if there is positive spatial autocorrelation, identify the type using the Getis-Ord 

“G” statistic. The Getis-Ord “G” is only applicable for positive spatial autocorrelation but can 

distinguish a predominance of high positive or low positive. High positive means that there are 

more zones with high values located near to other zones also with high values whereas low 

positive means the opposite (low near to low). The index is a type of average that weights the 

predominance of these types. In practice, there will be both types but the index indicates which 

is stronger. Since the Getis-Ord “G” requires a search distance, the user may have to run the 

Getis-Ord Correlogram first in order to identify a distance for which the positive spatial 

autocorrelation is most distinguishable from the theoretical random “G”. 

Third, examine the decline of the spatial autocorrelation with distance by using the three 

correlograms. While the Moran and Geary correlograms can be used for both positive and 

negative spatial autocorrelation, the Getis-Ord correlogram can only be used with positive 

spatial autocorrelation. The three correlograms will indicate how spatial autocorrelation varies 

by distance from each zone, on average. They can provide useful information about whether the 

concentration is very large, such as concentrated in the center of a metropolitan area, in which 

case the spatial autocorrelation is primarily a function of global factors. Alternatively, if the 

indices fall off very quickly, this suggests neighborhood (or local) effects rather than a 

dominant global pattern. In practice, there will be both types of factors, but the correlograms 

can indicate which is most important. 

5.40 
 

As with the global indices, the correlograms can provide useful information about the 

rate of decline in spatial autocorrelation (distance decay) for the kernel density routines 

(Chapter 10), the journey-to-crime routine (Chapter 13), the spatial regression routines (Chapter 

19), or the trip distribution module of the Crime Travel Demand Model (Chapter 28). 

In other words, identifying whether there is spatial autocorrelation and, if so, the type is 

important with zonal data (or with individual records having attributes) in that it is a first step in 

understanding where and why that spatial autocorrelation occurs. It is a necessary step in 

conducting hot spot analysis and in modeling the predictive factors that cause the spatial 

autocorrelation to occur. Chapter 9 examines hot spot identification routines appropriate for 

zonal data or individual data with attributes while Chapter 19 examines various regression tools 

for modeling the predictors of the spatial autocorrelation. 

5.41 
References

Anselin, L. (2008). Personal note on the testing of significance of the local Moran values. 

Anselin, L. (1995). Local indicators of spatial association - LISA. Geographical Analysis. 27, 

No. 2 (April), 93-115. 

Anselin, L.. (1992). SpaceStat: A Program for the Statistical Analysis of Spatial Data. Santa 

Barbara, CA: National Center for Geographic Information and Analysis, University of 

California. 

Bailey, T. C. & Gatrell, A. C. (1995). Interactive Spatial Data Analysis. Longman Scientific & 

Technical: Burnt Mill, Essex, England. 

Cliff, A. D. & Haggett, P. (1988). Atlas of Disease Distributions. Blackwell Reference: Oxford. 

Cliff, A. & Ord, J. (1973). Spatial Autocorrelation. Pion: London. 

Ebdon, D. (1988). Statistics in Geography (second edition with corrections). Blackwell: Oxford. 

Freedman, D. A. (1999). Ecological inference and ecological fallacy. International 

Encyclopedia of the Social and Behavioral Sciences, Technical Report No. 549, October. 

http://www.stanford.edu/class/ed260/freedman549.pdf. Accessed March 26, 2012. 

Geary, R. (1954). The contiguity ratio and statistical mapping. The Incorporated Statistician, 5, 

115-145. 

Getis, A. & Ord, J. K. (1996). Local spatial statistics: an overview. In Longley, P. & Batty, M. 

(eds), Spatial Analysis: Modelling in a GIS Environment. GeoInformation International: 

Cambridge, England, 261-277. 

Getis. A, & Ord, J. K. (1993) Erratum, Geographical Analysis, 25, 276. 

Getis, A. & Ord, J. K. (1992). The analysis of spatial association by use of distance statistics, 

Geographical Analysis, 24, 189-206. 

Griffith, D. A. (1987). Spatial Autocorrelation: A Primer. Resource Publications in Geography, 

The Association of American Geographers: Washington, DC. 

5.42
References (continued) 

Khan, G., Qin, X. & Noyce, D. A. (2006). Spatial analysis of weather crash patterns in 

Wisconsin. 85th Annual meeting of the Transportation Research Board: Washington, DC. 

Langbein, L. I. & Lichtman, A. J. (1978). Ecological Inference. Sage University Paper series on 

Quantitative Applications in the Social Sciences, series no. 07-010. Beverly Hills and London: 

Sage Publications. 

Lee, J. & Wong, D. W. S. (2005). Statistical Analysis with ArcView GIS and ArcGIS. J. Wiley & 

Sons, Inc.: New York. 

Lees, B. (2006). The spatial analysis of spectral data: Extracting the neglected data, Applied GIS, 

2 (2), 14.1-14.13. 

Levine, N. (1999). The effects of local growth management on regional housing production and 

population redistribution in California, Urban Studies. 1999. 36 12, 2047-2068. 

Levine, N. & Lee, P. (2013). Crime travel of offenders by gender and age in Manchester, 

England. Leitner, M. (ed), Crime Modeling and Mapping Using Geospatial Technologies, 

Springer. 145-178. 

Moran, P. A. P. (1950). Notes on continuous stochastic phenomena. Biometrika, 37, 17-23. 

Ord, J. K. & Getis, A. (1995). Local spatial autocorrelation statistics: Distributional Issues and an 

Application. Geographical Analysis, Vol. 27, 1995, 286-306. 

Ripley, B. D (1981). Spatial Statistics. John Wiley & Sons: New York. 

Ripley, B. D. (1976). The second-order analysis of stationary point processes. Journal of Applied 

Probability 13: 255-66. 

5.43
Attachments 

5.44 
 Calculated Moran’s I by Crime Types

Global Moran’s I and Small Distance Adjustment: 

Spatial Pattern of Crime in Tokyo 

Takahito Shimada 

National Research Institute of Police Science 

National Police Agency, Chiba, Japan 

Crimestat calculates spatial autocorrelation indicators such as Moran’s I and 

Geary’s C. These indicators can be used to compare the spatial patterns among 

crime types. Moran’s I is calculated based on the spatial weight matrix where the 

weight is the inverse of the distance between two points. There is a problem that 

could occur for incident locations in that the weight could become very large as the 

distance between points become closer. In Crimestat, the small distance adjustment 

is available to solve this problem. The adjustment produces a maximum weight of 1 

when the distance between points is 0. 

The number of reported crimes in Tokyo increased from 1996 to 2000 

although the city is generally very safe. For this analysis, 68,400 cases reported in 

the eastern parts of Tokyo were aggregated by census tracts (N=350). Then 

Crimestat calculated Moran’s I for each crime type with and without the small 

distance adjustment. 

The “I” value for most crime types, including burglary, theft, purse snatching, 

showed significantly positive autocorrelation. The results with and without the 

small distance adjustment were generally very close. The Pearson’s correlation 

between the original and adjusted Moran’s I is .98. Among 10 crime types, relatively 

strong spatial patterns were detected for car theft, sexual assaults, and residential 

burglary. 

Spatial Patterns of 

Residential Burglary: 

Moran’s I = 0.023. z=7.58 
Preliminary Statistical Tests for Hotspots: 

Examples from London, England 

Spencer Chainey 

Jill Dando Institute of Crime Science 

University College

London, England 

Preliminary statistical tests for clustering and dispersion can provide insight 

into what types of patterns will be expected when the crime data is mapped. Global 

tests can confirm whether there is statistical evidence of clusters (i.e. hotspots) in 

crime data which can be mapped, rather than mapping data as a first step and 

struggling to accurately identify hotspots when none actually exist. 

Using CrimeStat, four statistical tests were compared for robbery, residential 

burglary and vehicle crime data for the London Borough of Croydon, England. For 

the incident data, the standard distance deviation and nearest neighbor index were 

used. For crime incidents aggregated to Census block areas, Moran’s I and Geary’s 

C spatial autocorrelation indices were compared. The crime data is for the period 

June 1999 – May 2000. 

Crime type Number 

of crime 

records 

Standard 

distance 

NN 

Index 

z-score 

(test

statistic) 

Evidence of 

Clustering? 

Robbery 1132 3119.5 m 0.47 -34.2 Yes 

Residential 

burglary 

3104 3664.6 m 0.46 -57.5 Yes 

Vehicle crime 9314 3706.2 m 0.26 -137.0 Yes 

Crime type Moran’s I Geary’s C 

All crime 0.0067 1.14 

Robbery 0.0078 1.15 

Residential 

burglary 

0.014 0.99 

Vehicle crime 0.0082 1.08 

With the point statistics, all three crime types show evidence of clustering. 

Vehicle crime shows the more dispersed pattern suggesting that whilst hotspots do 

exist, they may be more spread out over the Croydon area than that of the other two 

crime types. For the two spatial autocorrelation measures, there are differences in 

the sensitivities of the two tests. For example, for robbery, there is evidence of 

global positive spatial autocorrelation (overall, Census blocks that are close together 

have similar values than those that are further apart). On the other hand, the 

Geary coefficient suggests that, at a smaller neighbourhood level, areas with a high 

number of robberies are surrounded by areas with a low number of robberies.