The author(s) shown below used Federal funding provided by the U.S. 

Department of Justice to prepare the following resource: 

Document Title: Research on Offender Decision-Making 

Utilizing Geo-Narratives: Final Report 

Author(s): Eric Jefferis, Andrew Curtis, Lauren Porter 

Document Number: 252133 

Date Received: October 2018 

Award Number: 2013-R2-CX-0004 

This resource has not been published by the U.S. Department of 

Justice. This resource is being made publically available through the 

Office of Justice Programs’ National Criminal Justice Reference 

Service. 

Opinions or points of view expressed are those of the author(s) and 

do not necessarily reflect the official position or policies of the U.S. 

Department of Justice. 
	 	

	 	 	 	 	

					

KENT STATE UNIVERSITY

Research	on	Offender	Decision-Making	Utilizing	

Geo-Narratives:	Final	Report

Eric Jefferis, Andrew Curtis, Lauren Porter

6/30/2018

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.
	 	

	 	

Table of Contents

BACKGROUND..........................................................................................................................................2

METHODS.....................................................................................................................................................4

FINDINGS......................................................................................................................................................9

OTHER GRANT PRODUCTS..........................................................................................................................13

CONCLUSION ..........................................................................................................................................14

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

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1
BACKGROUND 

Like many American cities, Akron witnessed a steady decline across and within its 

different neighborhoods as a result of the steep downturn in the national economy that led to the 

foreclosures crisis in 2008. This increase in urban decay resulted in a complex and dynamic 

landscape that contains geographies of vulnerability, including crime attractors. 

Although the link between urban blight and crime is commonly perceived by both 

citizens and police, and although it has been described by other academics (Dalton, Gradeck and 

Mercaldo 2008, Lee and Wilson 2013); it has traditionally been hard to quantify from the 

perspective of individuals’ perceptions, including ex-offenders and neighborhood residents. 

Capturing all relevant factors that contribute to crime is important to decision makers 

who want to apply principles informed by the Environmental Criminology, Social 

Disorganization, Broken Windows, and CPTED theories. In addition to traditional data, such as 

crime locations and 911 call-out data, other variables of interest include vacant properties, high 

grass, overgrown bushes, graffiti, and neighborhood disrepair. However, vitally important spatial 

data, that of crime-geography experience and perception are often ignored because of the 

difficulty in data collection and analysis. Yet the need to develop translational methods to 

include these “insights” is compelling - - to know which streets and buildings are important to 

criminals, both the where and why, might provide vital for neighborhood crime reduction 

planning. This project sought to address this data deficiency for several neighborhoods of Akron, 

Ohio, while also providing a methodological frame suitable for implementation by other police 

departments for a range of different city sizes. 

The project aimed to answer the following research questions: 

• To what degree is there spatial consistency in neighborhood “crime” insight maps 

created by ex-offenders match with existing crime and 911 call-out data hotspots? 

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Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.

2
How do these perceptions fit with non-offenders and neighborhood police 

officers? 

• Do these neighborhood “crime” insight maps reveal environmental signatures that 

can be translated to other Akron neighborhoods in order to explain and predict 

crime patterns? 

To answer these questions, ex-offenders, neighborhood officers, and key community 

members provided geonarratives of three Akron neighborhoods for one year. Simultaneously 

data from three other Akron neighborhoods were collected using the spatial video system. 

In keeping with the Scanning-Analysis-Response-Assessment (SARA) framework, 

especially as detailed in the publication “Crime Analysis for Problem Solvers” (Clarke and Eck 

2005) the identification of crime hotspots are an invaluable policing strategy, but are in and of 

themselves limited. Additional context is required to understand the hotspots. For example, 

although a neighborhood may be identified as a hotspot, this clustering of crimes will involve 

multiple micro-spaces where crime is indeed elevated, while along connecting streets no crime 

occurs (Groff, Weisburd and Yang 2010). Over-policing for the entire area may be detrimental to 

community relations because of this fine-scale geography. In addition, even within the hotspot 

micro spaces, temporal fluctuations will occur, while other beat-level insights (for example, key 

houses or corners, escape routes) should also be used to contextualize the crime space. 

This project contextualizes neighborhood crime geography as seen through the eyes of 

local law enforcement, ex-offenders, and community residents. The novel method utilized here, 

spatial video, is also used to assess the implementation of Crime Prevention through 

Environmental Design (CPTED) principals such as the impact of land clearing on the 

revitalization of neighborhoods. From the subsequent coding and analysis of physical visible 

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Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.

3
data, and the ride-along narratives we coded houses for blight and examined the impact of house 

razing on local criminal activity. 

The method implemented here, where neighborhood insight is mapped as part of 

contextualized hot spot identification, is translatable allowing police departments from regions 

with different urban landscapes and challenges to develop and improve location-specific policing 

strategies. The practical innovative methods developed here will help inform decisions on which 

specific areas within neighborhoods are most likely to decrease crime and disorder if attended to 

using environmental principles to make better informed decision. 

METHODS

A spatial video geonarrative system (SVG) is utilized. The spatial video set-up includes 

two cameras mounted on side windows of a car, one pointed horizontally, the other angled 

slightly downward. The use of four cameras is designed to maximize the field of view, but also 

to build in redundancy on either side in case one camera fails. Each camera is charged from the 

cigarette lighter power supply of the vehicle (through a four way splitter). Each camera contains 

a 32GB micro SD card which holds approximately 4 hours of data. A dual microphone cable and 

splitter box has also been developed by the project team in order to capture within car 

commentary which is then recorded by each camera (Mills et al. 2010, Curtis, Duval-Diop and 

Novak 2010, Curtis and Mills 2011). In this way, once transcribed, any comments made during 

the drive can be mapped to exact locations using the camera’s internal GPS. 

The spatial video consists of a global position system (GPS)-encoded video that can be 

used to collect high definition video for multiple angles (depending on the number of cameras 

used) and can be viewed in freeware (Contour Storyteller) to display both the image and 

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necessarily reflect the official position or policies of the U.S. Department of Justice.

4
collection pathway. The technology employed allows for the data collectors to travel at normal 

city road speeds (up to 35 mph) while still capturing HD images. By developing project specific 

coding systems, these data can be translated into a geographic information system (GIS) for 

visualization and analysis, including combination with other (crime) data layers. For Akron, an 

extreme sports camera kit field-tested in Youngstown, Ohio was utilized. Four target and four 

comparison neighborhoods were in Akron were selected based on their comparable crime rates 

and demographics. 

While the primary focus of using 

spatial video is for collecting ex-offender 

insight, the entire neighborhood was also 

collected and coded to generate maps to 

supplement these insights. Coding 

employed in pre-tests for Youngstown, OH 

included the modification of a classification 

scheme of houses developed by Kent State 

University professor Andrew Curtis, 

whereby 1 = non occupied, 2 = cleared lot, 3 = rebuilding and 4 = occupied (Curtis and Mills 

2011). In addition, other important, theoretically informed variables such as signs of blight, pride 

in the home, “crime” indicators (such as youth or multiple cars), security measures etc. were also 

coded. Through this technique, other previously unknown environmental signatures were able to 

be revealed. 

Once coding was complete, including entry into a GIS, spatial analysis was performed. 

For example, kernel density surface heat maps and spatial statistical maps were created for the 

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Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.

5
	 	 	 	 	 	

analysis of blighted homes. Another analytical approach modified from spatial epidemiology is 

the use of a spatial filter to calculate overlapping rate surfaces where video extracted codes act as 

both numerators (blighted homes) and denominators (all buildings). 

During spatial video collection, ex-offenders, police officers, and neighborhood residents 

accompanying the Kent State team narrated the neighborhood. This semi-structured process 

allowed for free-form descriptions of anything he/she believed to be important. The narrative, 

which also has GPS locations attached to it, is then transcribed and digitized as polygons on the 

neighborhood map. Points, lines and polygons will be mapped in Google Earth and then 

imported into ArcGIS. In this GIS, comments will be broken into attribute columns (for example, 

gangs, drugs etc), while any other important spatial information is extracted. 

Prior to engaging in the ride-along, the participants were asked to complete a task of 

describing the neighborhood on a 

map – indicating the “good” and 

“bad” areas and the types of 

activities they believe happen in 

those areas. These perceptual 

maps are utilized in Curtis et al. 

(2018). A total sample of 107 

riders were performed with exoffenders, police officers, and 

Step 1: Marking Hot and Cold Spots

neighborhood members. 

6

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Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.
	 	

• spatial video

on video and audio was transcribed to protect their identity. The Kent State University IRB 

Step 2: Ride-along

The following images 

depict the steps involved in 

translating a spatial video 

geonarrative to a GIS system and 

coding the data. This researchers 

drove the vehicles and controlled 

the recording devices from the 

rear seats. Images of the 

participants were never captured 

reviewed and approved the research protocol. 

The blighted house shown here 

is associated with a precise 

geographic coordinate on the map 

in top corner and the narrative is 

tagged with a precise time-stamp 

and associated geographic location. 

Spatial Video

Mapping Geonarrative and	Visual Data

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necessarily reflect the official position or policies of the U.S. Department of Justice.

7
	 	 	 	 	 	 	 	 	 	 	 	 	 	 	

	 	 	 	 	 	 	 	 	 	 	

	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	

	 	 	 	 	 	 	 	 	 	

	 	 	

	 	 	 	 	

	 	

	 	 	

	 	 	 	 	 	

“This is another vacant house right by the hill. They were using this as a

prostitute house. You	see the FOG (graffiti) right there. And	it’s hard	to come

An illustration of the richness of in on it because you would have to come in through the back and they can

shoot up the top and out onto the other streets...”

just one geonarrative interview. 

Example of coding from spatial video 

to GIS. In this case, a “blight” scale is 

being coded, but any visually 

identifiable variable can be coded and 

analyzed systematically and routinely 

utilizing the geospatial video cameras. 

Coding Video into GIS

1	= Abandoned (or little living presence)

2	= Cleared lot

4	= Lived in neighborhood home

5-10	= Boarded / broken windows, vegetation overgrowth, blight

Neighborhood Blight

8

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necessarily reflect the official position or policies of the U.S. Department of Justice.
FINDINGS

This project has been successful in a number of ways. Perhaps foremost, it has advanced a 

methodology that will benefit a number of fields, including: criminal justice, public health, and 

geography. The use of SVG has been found to be possible utilizing off-the-shelf cameras in 

combination with typical GIS systems. While we wrote code to automate various processes, the 

basic tasks of SVG can be accomplished on a small-scale with basic tools. The process requires 

careful redundancies to overcome occasional technical difficulties, but we succeeded in 

interviewing 107 individuals without any substantial loss of data due to our use of multiple 

cameras and backup audio recordings. More importantly, the utility of SVG for capturing 

nuanced data has been demonstrated. Collecting data in the field - where people live and work -

provides visual cues that can’t be replicated on a paper map in a neutral setting. 

Multiple journal articles have been published as a result of this grant and numerous additional 

articles will follow that will continue to explore the micro-nuances of place and crime. Also, we 

hope to publish a book detailing our project, given its complexity and depth. The citations and 

abstracts for published articles are presented below. One article that is still under review, but is 

particularly relevant, is also listed as a product. In the section that immediately follows, 

additional products that emerged from this grant are listed. 

Curtis, A., Curtis, J., Porter, L., Jefferis, E., & Shook, E. (2016). Context and spatial nuance 

inside a neighborhood’s drug hotspot: Implications for the crime-health nexus. Annals of 

the American Association of Geographers. DOI: 10.1080/24694452.2016.1164582 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.

9
Abstract: New geographic approaches are required to tease apart the underlying sociospatial 

complexity of neighborhood decline to target appropriate interventions. Typically maps of 

crime hotspots are used with relatively little attention being paid to geographic context. This 

article helps further this discourse using a topical study of a neighborhood drug microspace, 

a phrase we use to include the various stages of production, selling, acquiring, and taking, to 

show how context matters. We overlay an exploratory data analysis of three cohort spatial 

video geonarratives (SVGs) to contextualize the traditional crime rate hotspot maps. Using 

two local area analyses of police, community, and ex-offender SVGs and then comparing 

these with police call for service data, we identify spaces of commonality and difference 

across data types. 

Curtis, J., Curtis, A, Ajayakuma, J., Jefferis, E., & Mitchell, S. (Forthcoming). Same Space – 

Different Perspectives: Comparative Analysis of Geographic Contexts through Spatial 

Video Geonarratives and Sketch Maps. International Journal of Geographic Information 

Sciences. 

Abstract: The importance of including a contextual underpinning to spatial analysis of social data 

is gaining traction in the spatial science community. The challenge, though, is how to capture 

these data in a rigorous manner that is translational. One method that has shown promise in 

achieving this aim is the spatial video geonarrative (SVG) and in this paper we pose questions 

that advance the science of geonarratives through a case study of criminal ex-offenders. Eleven 

ex-offenders provided sketch maps and SVGs identifying high crime areas of their community. 

Wordmapper software was used to map and classify the SVG content; its spatial filter extension 

was used for hotspot mapping with statistical significance tested using Monte Carlo simulations. 

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necessarily reflect the official position or policies of the U.S. Department of Justice.

10
Then, each subject's sketch map and SVG were compared. Results reveal that SVGs consistently 

produce finer spatial scale data and more locations of relevance than the sketch maps. SVGs also 

provide explanation of spatial-temporal processes and causal mechanisms linked to specific 

places, which are not evident in the sketch maps. SVG can be a rigorous translational method for 

collecting data on geographic context of many phenomena. Therefore, this paper makes an 

important advance in understanding how environmentally immersive methods contribute to 

understanding of geographic context. 

Curtis, A., Curtis, J., Schook, E., Smith, S., Jefferis, E., Porter, L., Schuch, L., Felix, C., & 

Kerndt, (2015). Spatial video geonarratives and health: Case studies in postdisaster 

recovery, crime, mosquito control and tuberculosisin the homeless. International Journal 

of Health Geographics 14(1). 10.1186/s12942-015-0014-8 

Abstract: 

Background: A call has recently been made by the public health and medical communities to 

understand the neighborhood context of a patient’s life in order to improve education and 

treatment. To do this, methods are required that can collect “contextual” characteristics while 

complementing the spatial analysis of more traditional data. This also needs to happen within a 

standardized, transferable, easy-to-implement framework. Methods: The Spatial Video 

Geonarrative (SVG) is an environmentally-cued narrative where place is used to stimulate 

discussion about fine-scale geographic characteristics of an area and the context of their 

occurrence. It is a simple yet powerful approach to enable collection and spatial analysis of 

expert and resident health-related perceptions and experiences of places. Participants comment 

about where they live or work while guiding a driver through the area. Four GPS-enabled 

cameras are attached to the vehicle to capture the places that are observed and discussed by the 

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Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.

11
participant. Audio recording of this narrative is linked to the video via time stamp. A program 

(G-Code) is then used to geotag each word as a point in a geographic information system (GIS). 

Querying and density analysis can then be performed on the narrative text to identify spatial 

patterns within one narrative or across multiple narratives. This approach is illustrated using case 

studies on post-disaster psychopathology, crime, mosquito control, and TB in homeless 

populations. Results: SVG can be used to map individual, group, or contested group context for 

an environment. The method can also gather data for cohorts where traditional spatial data are 

absent. In addition, SVG provides a means to spatially capture, map and archive institutional 

knowledge. Conclusions: SVG GIS output can be used to advance theory by being used as input 

into qualitative and/or spatial analyses. SVG can also be used to gain near-real time insight 

therefore supporting applied interventions. Advances over existing geonarrative approaches 

include the simultaneous collection of video data to visually support any commentary, and the 

ease-of-application making it a transferable method across different environments and skillsets. 

Porter, L., Curtis, A., Jefferis, E., & Mitchell, S. (Under Review). Hidden Hot Spot? The Case 

of the Corner Store. Criminology. 

Abstract: 

Scholars typically use calls to the police to study geographic crime patterning. However, crime 

reporting may be systematic across space. In the present study we generate perceptual hot spots 

from the crime insights of 36 ex-offenders, police, and residents of a high crime neighborhood in 

Ohio. We overlay perceptual hot spots of drugs and violence with those gleaned from calls for 

service. As expected, we find that areas identified by participants versus those identified from 

calls for service do not map neatly onto each other. One micro-place in particular - a corner store 

- emerges as a perceptual hot spot across all groups, but not in calls for service. Utilizing spatial 

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Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.

12
	 	

 

and narrative data, we delve deeper to understand the dynamics of this “hidden hot spot.” First, 

we find that the corner store is relatively isolated. There are few occupied residences around it 

and our participants actively avoid it. Second, the storeowners and local criminals have an 

arrangement which helps to insulate the store from police intervention. Research relying solely 

on call data may risk missing hot spots that elicit few calls. As such, our methodology can help 

to illuminate and understand the dynamics of these micro-places. 

OTHER GRANT PRODUCTS

• In 2017, Jefferis and Curtis received funding from the Ohio Office of Criminal Justice 

Services to collaborated with Ravenna, Ohio PD to collect spatial video data in order to 

develop GIS-based system to analyze opioid overdoses. This project utilized SVG and 

was informed by the NIJ grant. https://www.kent.edu/einside/news/walking-stepsopioid-addicts-using-gps 

• The NIJ project resulted in substantial interest from our practitioner partners at the Akron 

Police Department. The Akron PD applied for and received a grant to assign spatial 

video cameras to a pilot group of officers for environmental data collection with officers 

narrating, to be used to supplement the crime analysis unit’s available data for tactical 

and strategic purposes. The Kent State research team provided technical assistance to the 

Akron PD on the use of the cameras and data extraction. The Akron PD presented on 

their initiative at the IACA in collaboration with Dr. Curtis. 

• A major product that has resulted from this NIJ award is a NSF grant entitled: 

GeoVisuals Software: Capturing, Managing, and Utilizing GeoSpatial Multimedia Data 

for Collaborative Field Research. Leveraging what we learned from the NIJ project, Dr. 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.

13
 

 

 

 

 

 

 

Curtis and colleagues in Computer Science received funding from NSF to automate many 

of the very time-intensive data-manipulation and data-analysis methods that have been 

necessary to carry out the current project. The NSF project began in the spring of 2018. 

Various conference presentations, including: 

• Porter, L. Curtis, A. Jefferis, E. Using Spatial Video and Geonarratives to Understand Crime 

Hot Spots. 2014. Annual Meetings of the American Society of Criminology: San Francisco, 

CA 

• Curtis, A., Jefferis., E., Porter, L., Shook E., and E. Paull. Using Spatial Video and 

Geonarratives to Map the Health and Crime Nexus on Blighted Landscapes October 2014. 

Autocarto, Pittsburgh 

• Curtis, A., Wei-An, L., Jefferis, E., Porter, L., & Curtis, J. (2014). Adding neighborhood 

context to hotspot mapping: The use of spatial video as a geonarrative tool. 142nd APHA 

Meeting and Exposition. 

CONCLUSION 

We began this project nearly five years ago with the goal of examining nuances of microplaces utilizing a new method of spatial video geonarratives. This project served as a proof of 

concept for the SVG methodology and a springboard for multiple other projects. As mentioned 

above, Dr. Curtis received an NSF grant and we have received a small state award largely as a 

result of our success with this NIJ grant. It should be noted, however, that the spatial video 

method is now being used in a number of ways in the field of public health, for example, to 

identify lead-based paint (https://www.ohio.com/akron/lifestyle/children-at-risk-researchersidentify-hot-spots-for-lead-exposure-in-akron) and to map cholera in Haiti 

(https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4772207/). 

With regard to the original aims or our grant, the aim to determine “to what degree is 

there spatial consistency in neighborhood “crime” insight maps created by ex-offenders match 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.

14
 

	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	

	 	 	 	 	 	 	 	 	 	

	 	 	

	 	 	 	 	 	 	 	 	 	 	 	 	 	

	 	 	 	 	

	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	

	 	 	 	 	 	 	

	 	 	 	 	 	 	 	 	 	 	 	 	 	

	 	 	 	 	

with existing crime and 911 call-out data hotspots?” is best answered by the forthcoming Curtis 

et al. article and the Porter et al. article that is still under review. The answer is nuanced (as one 

might expect) and requires a full read of the articles. The second aim regarding whether 

“neighborhood “crime” insight maps reveal environmental signatures that can be translated to 

other Akron neighborhoods in order to explain and predict crime patterns?” is similarly 

complex. We were surprised during our ride-alongs by the lack of any consistent “indicators” of 

troubled houses being identified by our participants. On the other hand, we found that researchercoded indicators of blight were predictive of crime in a manuscript that is yet to be published. 

This second aim has been the more challenging of the two, but we continue to pursue it as we dig 

deeper into the wealth of data that was collected during the grant period. 

References

Clarke, R. V. & J. E. Eck	(2005) Crime Analysis	for Problem Solvers. Washington, DC: Center for Problem

Oriented Policing.

Curtis, A. & J. W. Mills (2011) Crime in	urban	post-disaster environments: A	methodological framework

from New Orleans. Urban Geography, 32, 488-510.

Dalton,	E.,	R. Gradeck & A. Mercaldo (2008) Using maps of home foreclosures to understand national

and local problems. Geogr Public Saf, 1, 5-8.

Groff, E. R., D. Weisburd & S.-M. Yang (2010) Is it important to examine crime trends at a local “micro”

level?:	 A longitudinal analysis of street to	 street variability in	 crime trajectories. Journal of	

Quantitative Criminology, 26, 7-32.

Lee, J. & R. E. Wilson. 2013. Geospatial Modeling	 and Simulation of Property	 Crime in Urban

Neighborhoods: An Example Model with Foreclosure.	 In Crime Modeling	 and	 Mapping	 Using	

Geospatial Technologies,	249-267. Springer.

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.

15