Crime Hot Spot Analysis in ArcGIS Online

This tutorial is an example of exploratory data analysis in ArcGIS Online.

This example uses hot-spot analysis locations of robbery in Baltimore during 2017.

Additional techniques for downloading and analyzing crime point data are described in this tutorial on Point Cluster Analysis in ArcGIS Online.

Literature Review


In 2016 Baltimore had an estimated population of 621,000, and it is the core city in the 20th most-populous metropolitan area in the United States (USCB 2018). Baltimore is part of a densely-populated area in the Northeast that stretches from Washington, DC up to Boston, MA.

Baltimore was founded in 1729 and remains a major seaport. Following a 20th century decline in manufacturing, the city shifted to a post-industrial service economy. In 2015 the largest employers in the city were two major universities (Johns Hopkins and the University of Maryland) and their associated medical centers (Maryland Department of Commerce 2015).

Notable tourist attractions in Baltimore include the Fort McHenry National Monument (inspiration for the national anthem), The Baltimore Museum of Art, The Inner Harbor, Camden Yards (home of the Baltimore Orioles professional baseball team), and the Baltimore and Ohio Railroad Museum (Law 2018).

Oriole Park at Camden Yards (Keith Allison / Wikipedia)

Crime in Baltimore

Baltimore is notorious for its crime. Its overall 2017 violent crime rate of 2,027 per 100,000 inhabitants was over five times the US rate of 382.9 per 100,000 residents. Its murder rate of 55.8 per 100,000 inhabitants was over eleven times the national rate of 5.3 (FBI 2018b, tables 1 and 8).

This high level of crime may be attributable in part to high levels of poverty. 23% of Baltimore's population lives in poverty, compared to 15.1% in the US as a whole. The median household income of $44,262 is below the US median of $55,322. 53.4% of households rent compared to 36.4% in the US as a whole (USCB 2018).

Assault in Baltimore

The FBI (2018a) defines aggravated assault as, "An unlawful attack by one person upon another for the purpose of inflicting severe or aggravated bodily injury. This type of assault usually is accompanied by the use of a weapon or by means likely to produce death or great bodily harm. Simple assaults are excluded."

In 2017 there were 5,827 aggravated assaults known to Baltimore police. Given a population of 613,217, this gives an assault rate of around 917 per 100,000 inhabitants. This is almost four times the US rate of 249 per 100,000 inhabitants (FBI 2018b).

Using a unit cost of a single assault as $107,020 calculated by McCollister, French, and Fang (2010, Table 1), the total cost to society of those assaults was around $624 million dollars, when considering costs of the criminal justice system, medical costs, property loss, lost productivity, etc.

Social Disorganization Theory

Lersch (2004) identifies two broad classes of theories that are used to explain why crimes occur where they occur: social ecology theories and rational choice theories. These two groups of theories are complimentary and offer different perspectives on why crimes are committed more frequently in some places than in others.

The first of these classes, social ecology theories, are macro-level theories that focus on how behaviors are shaped by ecology: the dynamic interaction between individuals and their social and physical environment.

Social disorganization theory is a social ecology theory that asserts that crime is the result of an inability of a community structure to realize the common values of its residents and maintain effective social controls (Lersch 2004, 46; Sampson and Groves 1989, 177). Social disorganization theory has its roots in research performed in the School of Sociology at the University of Chicago beginning in the 1920s, most notably by Robert Park, Ernest Burgess, Clifford Shaw, and Henry McKay. Accordingly, these ideas are often referred to as Chicago School ideas.

Although the complexity of human society makes analysis of social disorganization similarly complex, a handful of neighborhood social variables are proxies for breakdowns in formal and informal social controls, and tend to correlate with higher levels of crime in specific areas (Lersch 2004, 50-53; 148; Sampson and Raudenbusch 1999; 2001):

Crime Prevention Through Environmental Design (CPTED)

The second of the two broad classes of theories, rational choice theories, are micro-level theories that focus on criminals as rational actors who carefully weigh the pros and cons of potential criminal acts, and then make rational decisions about if, where, and when to perform those acts.

These theories have their roots in the classical criminology of Cesare Beccaria (1738 - 1794), who asserted that the key to crime prevention is deterrence through punishment that is certain, swift, and proportionate to the severity of the crime. In the 20th century, this tradition was the foundation of Derek Cornish and Ronald Clarke's (1986) rational choice theory, which asserts, Criminal involvement refers to the processes through which individuals choose to become initially involved in particular forms of crime, to continue, and to desist.

This focus on individual choices leads to the questions of what kinds of physical spaces would be most attractive for criminals to choose for their crimes, and what can be changed in those spaces to make them less attractive. In the early 1960s, the urban critic Jane Jacobs in her seminal book The Death and Life of Great American Cities analyzed the way that new physical designs for cities were destroying neighborhood cohesion and creating city districts that are custom-made for easy crime. (Jacobs 1961, 31). Oscar Newman (1972) used Jacobs' ideas as the foundation for defensible space theory, which evolved into crime prevention through environmental design (CPTED).

CPTED (Jeffrey 1971; Lersch 2004, 158-161) is a crime prevention technique based on rational choice theory that involves three interrelated strategies: access control, surveillance, and territorial enforcement.

Access control (sometimes called target hardening) involves limiting opportunities for a motivated offender to come into contact with a potential target:

Surveillance involves making it as easy as possible to observe (and deter) potential offenders:

Territorial reinforcement involves physical features that clearly indicate the difference between public and private space, and make it clear who the space belongs to. While surveillance and access control features often reinforce territory, other techniques are useful as well:


Federal Bureau of Investigation (FBI). 2018a. "UCR Offense Definitions." Accessed 11 November 2018.

Federal Bureau of Investigation (FBI). 2018b. "Crime in the United States 2017." Accessed 11 November 2018.

Jacobs, Jane. 1961. "The Death and Life of Great American Cities." New York: Vintage Books.

Jeffrey, C. Ray. 1971. "Crime Prevention Through Environmental Design." Beverly Hils: Sage Publications.

Law, Lana. 2018. "14 Top-Rated Tourist Attractions in Baltimore." Accessed 11 November 2018.

Lersch, Kim Michelle. 2004. "Space, Time, and Crime." Durham, NC: Carolina Academic Press.

Maryland Department of Commerce. 2015. "Major Employers in Baltimore City, Maryland." Accessed 11 November 2018.

McCollister, Kathryn E., Michael T. French, and Hai Fang. 2010. "The Cost of Crime to Society: New Crime-Specific Estimates for Policy and Program Evaluation." Drug Alcohol Dependency 108 (1-2): 98-109. Accessed 16 November 2018.

Newman, Oscar. 1972. "Defensible Space." New York: Macmillan.

Sampson, Robert and W. Byron Groves. 1989. "Community Structure and Crime: Testing Social-Disorganization Theory." American Journal of Sociology 94 (4): 774-802. Accessed 16 November 2018.

Sampson, Robert and Stephen Raudenbush. 1999. "Systematic social observation of public spaces." American Journal of Sociology 105: 603-651.

Sampson, Robert and Stephen Raudenbusch. 2001. "Disorder in urban neighborhoods: Does it lead to crime?" National Institute of Justice Research in Brief. Washington, DC: US Department of Justice. Accessed 18 November 2018.

United States Census Bureau (USCB). 2017. "About the Bureau." Accessed 18 November 2018.

United States Census Bureau (USCB). 2018. "2012-2016 American Community Survey 5-Year Estimates." Accessed 11 November 2018.

Wikipedia. 2018. "Crime prevention through environmental design." Accessed 16 November 2018.

Methods and Data Sources: Robbery Hot and Cold Spots

Crime hot spots were identified using the ArcGIS Online hot spot tool, which uses the Getis-Ord Gi* statistic. The statistic value is a probability percentage (p-value) that a particular concentration of incidents that is larger or smaller than might be expected by random chance. Areas with unusually high concentrations of incidents are referred to as hot spots and areas with unusually sparse concentrations of incidents are referred to as cold spots.

Two residential locations were arbitrarily chosen: a point in a hot spot and a point in non-hot spot with an average or less-than-average density of incidents. Google Maps was then used to identify the ZIP Codes for those locations.

The Baltimore Police Department was the source of incident point data for aggravated assaults in 2017.

The chosen hot spot location was at 39.30796, -76.645101, which is in the Easterwood neighborhood, and which has a ZIP Code of 21217.

No cold spots were observed on the map, so the chosen non-hot spot location was at 39.359439, -76.682646, which is in the Glen neighborhood with a ZIP Code of 21215.

Aggravated Assault Hot Spots in Baltimore in 2017

Adding, Filtering, and Clustering Crime Data

Many cities make geocoded crime incident data available to the public that can be downloaded and imported into GIS software. However, the available formats vary (usually shapefiles and CSV files) and significant cleanup is often required to get the data into a format suitable for import.

For this example, 2017 crime incident data for Baltimore has already been imported into an ArcGIS Online hosted layer (Minn 2017 Crime Baltimore). To visualize the data:

  1. Add data, search for the crime layer in My Organization, and add it to the map
  2. View the metadata to find the name of the crime category variable
  3. Filter by crime: The name of the crime category variable and the possible values for that variable will differ by jurisdiction
  4. Turn on clustering: When the large number of points makes it impossible to see patterns, the clustering feature can be helpful to aggregate points into graduated bubbles depending on the zoom level
  5. Save your map
Adding, Filtering, and Visualizing a Crime Layer

Hot Spot Analysis Tool

  1. Perform Analysis, Analyze Patterns, Find Hot Spots
  2. Choose the point layer to analyze
  3. Give the new hot spot layer a meaningful name
  4. Uncheck Use current map extent
  5. Show credits - if this is over 20, you may be analyzing the wrong layer
  6. Run Analysis - this may take a few minutes

This tool sometimes freezes and never returns. If your analysis doesn't complete after three minutes:

  1. Check your content page to see if the hot spot layer was created
  2. Reload your map and then add the hot spot layer by hand
Hot Spot Analysis

Lat/Long and ZIP Codes

For this analysis we are going to choose a hot spot and a non-hot spot, then use Google Maps to find the ZIP Code. Hot spots are colored red and cold spots are colored blue.

  1. Find a residential location in hot spot
  2. Use the Find area, length, or location tool to find the latitude and longitude
  3. Find that latitude and longitude in Google Maps
  4. Right-click and select What's Here to find ZIP Code
  5. Look around the location to find the neighborhood name

In the example below, the hot spot location is 39.30796, -76.645101, which is in the Easterwood neighborhood, and which has a ZIP Code of 21217.

Hot Spot Lat/Long and ZIP Code

Repeat this search in a cold spot. Cold spots are shaded in blue. In this case, there are no cold spots on the map so we choose an area that has an assault but is not a hot spot.

In this example, the non-hot spot location is 39.359439, -76.682646, which is in the Glen neighborhood with a ZIP Code of 21215.

Non-Hot Spot Lat/Long and ZIP Code

Methods and Data Sources: Social Disorganization Theory

Assessment of neighborhood social disorganization was made using three proxy variables:

These variables came from the US Census Bureau's 2012-2016 American Community Survey 5-Year Estimates and the 2012 Survey of Business Owners.

The American Community Survey and American FactFinder

The US Census Bureau (USCB) is the part of the US federal government responsible for collecting data about people and the economy in the United States. The Census Bureau has its roots in Article I, section 2 of the US Constitution, which mandates an enumeration of the entire US population every ten years (the decennial census) in order to set the number of members from each state in the House of Representatives and Electoral College (USCB 2017).

Among the Census Bureau's many other programs is the American Community Survey (ACS), an ongoing survey that provides information on an annual basis about people in the United States beyond the basic information collected in the decennial census. The ACS is commonly used by a wide variety of researchers when they need information about the general public.

You can access USCB data through the American FactFinder web site.

The video below shows how to collect information from American FactFinder about the US as a whole, a specific city and county (Baltimore), and ZIP Code (21217).

ZIP Code Community Facts in American FactFinder

Methods and Data Source: CPTED

Qualitative assessment of crime prevention through environmental design (CPTED) in hot spots and non-hot spots was performed by surveying the neighborhoods using Google Maps Street View.

Linking and Embedding Scenes from Google Maps Street View

  1. In Google Maps, click the Browse Street View Images icon at the bottom right corner of the map and drag the person icon to the location you want to view
  2. When you have found a view you want to share, click the ellipsis at the top left of the map and select Share or embed image
  3. If you are sending or submitting a link, use the Link to share
  4. If you want to embed the street view on a web pate, use the iframe under Embed a map
Linking Google Street Views


Social Disorganization: Income

The 2012-2016 median household income in the United States was $55,322. The median household income in Baltimore was somewhat less at $44,262.

The median household income in our hot spot ZIP Code was $27,065, which is significantly lower than the city median. This is consistent with the association of high crime with high poverty in social disorganization theory.

The median household income in our non-hot spot ZIP Code was $36,763, which is slightly lower than the city median and quite a bit lower than the median in the US as a whole. This is less consistent with the association of low crime with low poverty in social disorganization theory.

Social Disorganization: Renters

The 2012-2016 percentage of households in the United States that rented rather than owned their residence was 36.4%. The renter percentage in Baltimore was significantly higher at 53.4%.

The percentage of renters in our hot spot ZIP Code was 69.1%, which was significantly higher than the city percentage. This is consistent with the association of high crime and neighborhood instability in social disorganization theory.

The percentage of renters in our hot spot ZIP Code was 51.3%, which was slightly lower than the city percentage. This is consistent with the association of low crime and neighborhood stability in social disorganization theory.

Social Disorganization: Mixed Use

The 2012-2016 ratio of paid employees to population in the United States was 126,752,238 / 308,745,538 or 0.41. The ratio in Baltimore County was similar at 323,939 / 805,029 or 0.40.

The ratio in the hot spot ZIP Code was 4,541 / 37,111 or 0.12, which is well above the county and US ratio. This is inconsistent with social disorganization theory where the expectation that this high crime area would have a mixed land use and a large number of paid workers in the neighborhood relative to the number of people that live there.

The ratio in the non-hot spot neighborhood was 19,510 / 60,161 or 0.32, which is only slightly below the county and US ratio. This is consistent with social disorganization theory where the expectation is that this low crime area would be primarily residential and have a lower ratio of employees to population.

CPTED: Access Control

The hot spot neighborhood is an area of late 19th- or early 20th-century row houses with limited access control. This is consistent with CPTED.

1712 Presbury Street (hot spot neighborhood)

Although the overall character of the non-hot spot neighborhood is very different from the hot spot neighborhood, the differences in access controls are only slightly better. This is somewhat consistent with CPTED.

5905 Bland Avenue (non-hot spot neighborhood)

CPTED: Surveillance

The crime hot spot neighborhood has a mix of weak and strong surveillance characteristics. In general this seems consistent with CPTED.

Street Life on North Fulton Avenue (hot spot neighborhood)

The surveillance characteristics of the non-hot spot neighborhood contrast notably. This is consistent with CPTED.

3031 Glen Avenue (non-hot spot neighborhood)

CPTED: Territorial Reinforcement

The high building density of the hot spot neighborhood blurs the line between public and private. The environmental markers indicate that this territory belongs to a wide variety of people, including criminals. This is consistent with CPTED.

1717 Westwood Avenue (hot spot neighborhood)

As with surveillance, the contrast between the two neighborhoods in territorial reinforcement is stark. The environmental markers indicate that criminals are not welcome here. This is consistent with CPTED.

5915 Simmonds Avenue (non-hot spot neighborhood)



While records of highly-visible crimes like murder and arson are fairly complete, more than half of violent crimes in general and three-quarters of rapes are not reported to police (Bureau of Justice Statistics 2012; RAINN 2018). In neighborhoods with poor community/police relations, victims may be less inclined to report crime to the police. Accordingly, analysis based on incident data from police departments will be incomplete for some crimes.

Geocoding Errors and Anonymization

Incident locations are recorded as street addresses rather than latitude and longitude, presenting the possibility that some crime points will be misplaced in the process of geocoding those addresses into latitudes and longitudes. In addition, reported crime locations are randomly offset to protect the identities of victims. Accordingly, spatial analysis based on those crime points will have spatial inaccuracies.


Research methods in general and qualitative methods in particular are especially prone to being affected by the unique perspectives and biases of the researchers. While some methods like phenomenology incorporate those subjectivities into the analysis, research is often designed to attempt to minimize the effect of subjectivities.

Researchers who grew up in secure, low-density suburbs will have implicit biases about neighborhoods and people that differ from the experiences of the researchers. Researchers may also be less sensitive to important physical cues that would be more obvious to people who grew up in those neighborhoods.

For example, an evaluation of whether a set of windows represents improved surveillance or reduced access control may be influenced by a researcher's past experiences with crime (as perpetrator or victim). Providing clearer guidelines for evaluating windows based on analysis of how windows have been used by criminals in the past might improve the predictive power of such evaluation.

Street View Limitations

Google Street View images are snapshots at particular points in time from specific vantage points in the street. Google Street View images are also selected and modified in post-processing to preserve the privacy of neighborhood residents. Therefore, these images provide only a limited (and somewhat deceptive) perspective on the life in neighborhoods at different times of day and over longer periods of time.

While Google Street View is a powerful tool for geographic analysis, it is no substitute for the rich experiential data available from field work and ethnography.


Bureau of Justice Statistics. 2012. "Nearly 3.4 million violent crimes per year went unreported to police from 2006 to 2010." Accessed 18 November 2018.

Rape, Abuse and Incest National Network (RAINN). 2018 "The Criminal Justice System: Statistics." Accessed


Abraham Lincoln is a Science, Technology, and Society major in the Department of Science, Technology, and Society at Farmingdale State College, with a concentration in Health, Wellness, and Society. He currently lives in Springfield, IL. His research interests include constitutional law and human trafficking policy. His career plans include working as a lawyer in general practice, and serving as a state legislator.