









































Pa
ge

 
1



Pa
ge

 
95

American Journal of  
Geospatial Technology (AJGT)

Assessment of  the Distribution of  Crimes and Factors Influencing Crime in the Temeke 
Municipality, Tanzania Using Geospatial Technology

Nickson Alnkiza Ernest1*, Ally Khalfani Kyengya1

Volume 4 Issue 1, Year 2025
ISSN: 2833-8006 (Online)

DOI: https://doi.org/10.54536/ajgt.v4i1.4790
https://journals.e-palli.com/home/index.php/ajgt

Article Information ABSTRACT

Received: March 14, 2025

Accepted: April 23, 2025

Published: August 14, 2025

This article assesses the distribution of  Crimes at Temeke municipality Dar es Salaam, using 
quantitative approaches. Both probability and non-probability sampling were applied to get 
the study area and participants in this study. Data collection was done through participatory 
mapping, remote sensing, document review, GPS survey, and observation, two sorts of  
crimes, robbery and burglary were studied in this research. ArcGIS software was used to 
create hotspot maps, crime patterns, and visualization. Results indicated areas with a high 
risk of  crime, and it increased over time from 2005 to 2022, and spatial factors for crime 
occurrence, Both family status of  people along the study area, economic status, education, 
poor infrastructure, low living standard and more seen to be factors for crime occurrence. 
Hence, the spatial distribution of  crimes in Temeke municipality is associated with both 
spatial and spatial factors that lead people to not succeed in their daily life 

Keywords
Crimes, Geospatial Technology, 
GIS, Participatory Mapping Crime 
Reduction 

1 Department of  Geography, University of  Dar es Salaam, Mlimani Campus, P. O. Box 35091, United Republic of  Tanzania
* Corresponding author’s e-mail: ernestnickson1997@gmail.com

INTRODUCTION 
Across the globe, official reports and data on police-
recorded crimes have consistently shown a rise in violent 
crimes, property crimes, and drug-related offenses from 
2003 to 2012, with a notable increase in murders in the 
Americas (UNODC, 2014). In the 2010s alone, nearly 
half  a million murders occurred worldwide, with 5% 
in Europe, 31% in Africa, and 36% in the Americas 
(UNODC, 2013).
The application of  geospatial technology in analyzing 
crime distribution and the factors influencing crime is still 
in its early stages globally. Only a few countries have begun 
to integrate Geographical Information Systems (GIS) 
into law enforcement practices (Ahmed & Salihu, 2013; 
Yelwa & Bello, 2012). For instance, a study by Schmitz 
et al. (1999) on the use of  GIS in policing in Southern 
Africa showed how law enforcement agencies could 
improve their operations through crime mapping. Crime 
mapping helped the South African Police Service (SAPS) 
solve serial murder cases, such as the Brixton murder and 
the Dquad robbery, by analyzing cellular data and crime 
hotspots (Stylianides, 2000). Additionally, Mswela (2019) 
highlighted that Malawi took a significant step in fighting 
violent crimes against people with albinism by deploying 
GPS technology. People in crime hotspots were given 
GPS devices to assist in tracking and preventing crimes 
against them.According to Mswela (2019), the integration 
of  automatic monitoring systems has significantly 
reduced violent attacks against people with albinism in 
Tanzania.
The application of  Global Positioning System (GPS) 
technology has become essential in both the private and 
public sectors.

Commercially, GPS technology plays a crucial role in daily 
activities by providing accurate location information. 
For over 30 years, organizations like the Tanzania 
Forest Service (TFS) and Tanzania National Parks 
(TANAPA) have leveraged GPS technology in efforts 
to protect endangered species such as elephants and 
rhinos (TANAPA & TFS, 2018; Tomkiewicz, 1996; TCP, 
1998). The national blueprint for 2050 envisions that 
Geographic Information Systems (GIS) will play a key 
role in enhancing peace and security operations across the 
country. In particular, police officers are equipped with 
GPS-linked devices, allowing them to quickly respond 
to service calls and efficiently carry out their duties.As 
noted by Okeyo (2021), technological advancements 
have opened new doors in the security sector. These 
innovations are now seen as driving factors behind 
the distribution of  criminal activities and their spatial 
patterns in Tanzania. GPS and GIS have significantly 
enhanced the capacity of  law enforcement agencies to 
combat crime and maintain public order. For instance, the 
installation of  closed-circuit television (CCTV) cameras 
in strategic locations across Dar es Salaam has greatly 
improved security. CCTV reports have enabled the police 
to recover stolen goods within three days of  the crime. 
Moreover, real-time crime mapping has become an 
indispensable tool for police officers. These maps allow 
law enforcement agencies to plan and execute more 
effective crime prevention strategies. In light of  these 
developments, a research study was conducted to assess 
the impact of  these technologies on crime reduction, 
security enhancement and the spatial distribution of  crime 
and factors influencing crime in Temeke municipality, 
Tanzania using Geospatial technology.



Pa
ge

 
96

https://journals.e-palli.com/home/index.php/ajgt

Am. J. Geo Spat. Technol. 4(1) 95-103, 2025

MATERIALS AND METHODS
Area of  the Study 
The study was conducted in Kurasini Ward, Dar es 
Salaam, which was selected due to its higher incidence of  
offenses compared to other areas in Temeke Municipality, 
as noted in the Police Report (2019/2020). Kurasini Ward 
is strategically located near unofficial beaches, marine 
ports, and dry ports, as indicated by the National Bureau 
of  Statistics (2020). Among the 23 wards in Temeke, 

including Mbagala, Chamazi, Charambe, Toangoma, 
Miburani, Tandika, Keko, Mtoni, Minazini, Mjimwema, 
and Kijichi, Kurasini was deliberately chosen for its 
prominence in crime occurrence.Temeke Municipality 
itself  is situated in the southern part of  Dar es Salaam, 
bordered by the Pwani region to the south, Ilala to 
the west and north, and the Indian Ocean to the east. 
Geographically, the area lies between 39º12’ - 39º33’ East 
and 6º48’ - 7º33’ South.

Figure 1: Location of  Temeke Municipality and Kurasini ward

Research Design and Sampling
The study utilized a quantitative research design to 
collect numerical data, enabling a detailed analysis 
of  crime distribution in Kurasini Ward, Temeke 
Municipality, Dar es Salaam. The specific focus of  the 
study allowed the researcher to target crime-prone areas, 
offering valuable insights into the relationship between 
geospatial technology and crime patterns. To gather the 
quantitative data, GPS and GIS software such as QGIS 
and ArcGIS were used to analyze spatial data related to 
crime incidents. The main objective of  the quantitative 
approach was to understand how crimes are distributed 
within Kurasini Ward. Purposive sampling was employed 
to select Kurasini Ward, given its reputation as an area 
with a high frequency of  crimes. The target population 
included detectives from Kurasini, the Ward Executive 
Officer (WEO), and Street Chairpersons, as they were key 
stakeholders with access to crime-related data in the area. 
For quantitative data collection, probability sampling was 
used to select 84 detectives from the detective unit. The 
process involved listing the detectives’ names, placing 
them on separate pieces of  paper, and drawing 73 pieces 

randomly from the basket. This method ensured that 
87% of  the population was included in the sample, and 
every detective had an equal opportunity to be selected 
(Welman, 2001). 

Collection of  Data
This study utilized both primary and secondary data 
sources. Primary data sources included community 
mapping, in-depth interviews, and field observations, 
while secondary sources encompassed remote 
sensing, raster data, digitization, and crime-related 
documents such as investigative journals, reports, and 
books from the detective department. The diverse 
collection methods ensured a comprehensive dataset, 
incorporating all facets of  the study, which would have 
been challenging to achieve with just one method.In 
community mapping, participants shared their personal 
experiences and knowledge about the distribution and 
extent of  offenses in the study area, forming the basis 
for creating crime distribution and hotspot maps. A base 
map, derived from satellite imagery with appropriate 
resolution, was used for participatory mapping to 



Pa
ge

 
97

https://journals.e-palli.com/home/index.php/ajgt

Am. J. Geo Spat. Technol. 4(1) 95-103, 2025

delineate various levels of  offenses. A total of  sixteen 
respondents, including detectives, street chairpersons, 
and a ward executive officer (all with 6-8 years of  
experience in crime-related matters), were involved in 
the process. These participants contributed valuable 
insights on the historical and current occurrences of  
crimes. During the second phase, participants took part 
in the mapping activity by marking locations on the map 
where offenses had occurred and denoting the severity 
of  these crimes using differently colored pebbles, guided 

by predetermined questions. The third phase involved 
analyzing the marked areas as offense hotspots to assess 
the distance relationships between the marked points. 
The mapping process utilized a high-resolution 12m × 
12m picture with a 1:4000 scale, covering features such 
as Kurasini roads, the Indian Ocean, schools, churches, 
and universities to help orient participants. The 
information gathered from the community mapping was 
then converted into a vector format and visualized using 
ArcGIS 10.7 Prosoftware.

Figure 2: Participatory Mapping with research participants

This study incorporated GPS, surveying, and field 
observation techniques to validate the crime data 
gathered from respondents via community mapping 
and interviews. These methods ensured the accuracy 
of  crime hotspots identified by participants. A transect 
walk was also conducted, combining GPS, surveys, and 
field observations to further confirm the data collected 
during community mapping and interviews with key 
respondents. Significant locations, such as Kilwa Road, 
Mafuta Road, and Mandela Road, were identified as major 
contributors to crime, with GPS marking these sites to a 
precision of  0-2 meters. The GPS point data was then 
overlaid on satellite imagery, which helped verify the 
participants’ identification of  crime locations and allowed 
for an in-depth analysis of  the spatial distribution of  crime 
incidents, ensuring a thorough and precise understanding 
of  crime patterns in the area.

Data Analysis
Hotspot analysis was carried out across the study area 
to identify regions prone to crime by evaluating both 
spatial and socio-economic factors influencing crime 
occurrences. The objective was to generate maps that 
illustrate crime patterns and pinpoint specific locations 
where criminal activities concentrate.The analysis used 
ArcMap software to detect and visualize crime patterns. 
The first step involved creating a polygon grid (fishnet) 
with a 220-meter cell size, covering the entire study area. 
This was done through ArcGIS by navigating: Data 
Management Tools > Sampling > Create Fishnet. Once 
the grid was established, point data layers representing 
crime incidents were linked to the polygon grid based 
on their spatial locations. Subsequently, crime hotspot 

areas were identified using the spatial statistics tool. The 
results were visualized to highlight hotspot patterns. The 
significance of  these hotspots was assessed at a 99% 
confidence level (z-score). A high z-score with a small 
p-value indicates a significant hotspot, while a low negative 
z-score with a small p-value signals a significant cold 
spot. The z-score’s magnitude reflects clustering intensity, 
with a z-score of  zero indicating no clustering. The 
statistical significance of  the z-scores was then compared 
against a predefined confidence level. At a significance 
level of  0.05 (95% confidence), a z-score below -1.96 is 
considered statistically insignificant, while a z-score of  
1.96 or higher indicates statistical significance. To further 
validate the findings, GPS data points were overlaid 
onto the hotspot maps, allowing cross-verification of  
the identified crime locations. This combined approach 
provided a comprehensive understanding of  crime 
distribution within the study area.
The sub-ward shapefiles were initially imported into 
Excel and saved as a comma-delimited text file (CSV 
format). This CSV file was subsequently uploaded into 
ArcGIS, where it was overlaid with the sub-ward shape 
files to verify the responses from the community mapping 
exercise. This step enabled the spatial validation of  the 
crime locations marked by community participants, 
ensuring the accuracy of  the identified crime hotspots 
within the defined sub-ward boundaries.

RESULTS AND DISCUSSION 
Extent (ranks) of  Factors for Occurrence of  Crime
Table shows the extent of  the factors for crime 
occurrence, such as family structure, occupation status, 
and income status and education level.



Pa
ge

 
98

https://journals.e-palli.com/home/index.php/ajgt

Am. J. Geo Spat. Technol. 4(1) 95-103, 2025

Figure 3: The Model of  factors influencing crime

Figure 4: Extent factors for crime occurrences around the study area from 2005-2022

Table 1: Responses on Ranks of  factors for crime occurrence 
variable Kind of  crime picked 

by respondents
factors for Number of  

criminals 
Criminals 
in %

1 occupation 
status

Robbery Unemployment 11197/15549 72%
Burglary Unemployment 6583/9678 68%

2 Income Robbery low-income level 12631/15549 81.2%
Burglary  low-income level 6499/9678 67.2%

3 family status Robbery separated/divorced 7605/15549 78.1%
Burglary separated/divorced 3507/9678 80.8%

4 Education Robbery primary education/no education 9874/15549 63.5%
Burglary primary education/no education 5870/9678 60.7%

This study identifies several factors that contribute to 
crime occurrence in urban areas, with income level 
standing out as the most influential. The research 
found that income level had a more significant impact 
on criminal behavior than other variables such as 
occupational status, family status, and education level. 

Family-related issues, especially family breakdowns, were 
also found to play a critical role in criminal involvement. 
A lack of  parental care, guidance, and counseling from 
both parents was recognized as a major driver for 
individuals engaging in criminal activities. Furthermore, 
family status was identified as the second most influential 



Pa
ge

 
99

https://journals.e-palli.com/home/index.php/ajgt

Am. J. Geo Spat. Technol. 4(1) 95-103, 2025

factor in criminal behavior, particularly in Kurasini, 
when compared to education and occupational status. 
Field respondents highlighted the crucial role of  family 
dynamics, emphasizing how family structure significantly 
shapes decisions to engage in criminal behavior within 
urban settings.

Participatory Mapping of  Criminal Incidents in 
Kurasini Ward 
The study in Kurasini Ward focused on criminal activities 
across five streets: Kurasini, Minazini, Mivinjeni, Kiungani, 

and Shimo la Udongo. The research utilized a community 
mapping technique to gather data on crime hotspots. 
Participants anonymously marked locations on a map 
where they believed crimes had occurred. Each individual 
was asked to privately identify the type of  crime linked 
to the marked areas. The results provided critical insights 
into the area’s most prone to criminal activity within the 
ward, highlighting specific streets and locations identified 
by the community. These findings emphasize areas where 
targeted interventions and preventive measures are most 
needed to combat crime.

The study further categorized the types of  offenses and their 
distribution across Kurasini Ward, with a focus on robbery 
incidents. Figure 5 illustrates the distribution of  robbery 
crimes, marked in red on the map. Robbery incidents were 
notably concentrated around certain locations such as 
Wimo Maasai Bar, Liquid Bar, and Red Kilwa Bar. These 
crime hotspots were linked to the proximity of  major roads 
like Kilwa and Mandela Road, which provided easy escape 
routes for robbers, especially during nighttime (Floyd, 2018). 

Additionally, robbery cases were prevalent around bus stops 
such as Mivinjeni, Kurasini, Ufundi, Bandari, and Uhasibu. 
The streets surrounding these bus stops—particularly 
Minazini, Shimo la Udongo, and Kurasini—were also prone 
to robberies. The presence of  unstructured housing in these 
areas contributed to the ease with which criminals could 
operate. Poor visibility in these neighborhoods allowed 
robbers to commit crimes and escape without being easily 
detected (Taylor & Hochuli, 2016).

Figure 5: Robbery crime incidents in the study area from 2005-2022



Pa
ge

 
10

0

https://journals.e-palli.com/home/index.php/ajgt

Am. J. Geo Spat. Technol. 4(1) 95-103, 2025

Figure 6 shows burglary incidents, represented by blue 
points, which are primarily concentrated along streets 
such as Shimo la Udongo, Kurasini, and Mivinjeni. 
These areas are characterized by unplanned housing 
developments and unauthorized beachfront zones 
(particularly Kurasini), offering offender’s opportunities 

for quick escape (Taylor & Hochuli, 2016). The majority 
of  burglary incidents were observed in locations within 
12 meters of  major roads, especially along Kilwa Road, 
and more specifically on Mivinjeni Street, Shimo la 
Udongo Street, and Minazini Street.

Figure 6: Burglary crime incidents in the study area from 2005-2022

Table 2: Summary of  Distribution of  Crime Incidents around The study area
boundary Crime type Crime distribution Predominant 

crime
kurasini Robbery around Star fuel, around trans cargo, around metero retot,

Bu
rg

la
ry

Burglary Kurasini near KCD, near Matero, near metrics, around Metl sudeco
Mivinjeni Robbery Bandari bus stop, Mivinjeni bus stop, peal t lulu modern BNR, 

around Mafuta road, around Benedictine guest house, around 
Ufundi bus stand, around Kurasini bizarre food, around Kurasini 
secondary around ALkhalee j TZ cosmetics

Burglary Near Mafuta road, around a mission to sea farers, Camel Wheat near 
Avante technology, around Gapco depot Cafritea coffee,around 
Mivinjeni.

Minazini Robbery around Wimo Maasai Pub, around Berra pork, around Uhamihaji 
secondary,,around Tiktok chips ,around Ommy food,around 
Masijid and around njia ya reli

Burglary around Kwa Boko, around Continental Cargo, near Yanga place, 
near mtaa wa kwa Mjumbe nyumba 10, near Tiktok and Snak point



Pa
ge

 
10

1

https://journals.e-palli.com/home/index.php/ajgt

Am. J. Geo Spat. Technol. 4(1) 95-103, 2025

Hotspot Mapping 
Robbery and Burglary offenses hotspot maps were 
created on the study area
Crime Hotspots Mapping around Kurasini Ward 
Respondents identified hotspot areas and provided data 
that were used to generate maps illustrating the spatial 
patterns of  crime incidents in the region. These maps 

effectively depicted the distribution of  crime across 
different locations, allowing for a clearer understanding 
of  areas with high concentrations of  criminal activity. 
The spatial analysis helped to highlight trends, identify 
risk zones, and inform strategies for crime prevention 
and resource allocation.

Shio 
laudongo

Robbery around Red Kilwa pub
Burglary Near Ruta pub

Kiungan Robbery around Dicd, around THR empty container deport
Burglary -

Figure 7: Robbery crime hotspot map in the study area from 2005-2022

Robbery hotspots were classified by intensity and 
represented using different colors on the map: high-
intensity hotspots appeared in red, medium-intensity in 
light red, and moderate-intensity in dark salmon. High-
intensity robbery locations were identified near Diplomasia 
Secondary School, Uhamiaji Secondary School, Ommy 

Food, Chipsi Mwarabu, Al Khaleej Tanzania Cosmetics, 
and the Police Canteen. Medium-intensity hotspots were 
observed in areas such as Njia ya Reli, Star Fuel, TRH 
Empty Container Yard, Malawi Cargo, Wimo Maasai 
Pub, Mivinjeni Bus Stop, Mafuta Road, Ufundi Bus 
Stop, Kurasini Bizarre Food, Tiktok Chipsi and Snack, 



Pa
ge

 
10

2

https://journals.e-palli.com/home/index.php/ajgt

Am. J. Geo Spat. Technol. 4(1) 95-103, 2025

Areas with high burglary activity were indicated in 
light red on the map, while medium-intensity hotspots 
appeared in coral, and moderate hotspots were marked 
in dark salmon. High-intensity burglary zones were 
identified in locations such as Braval, the vicinity of  
Yanga Place, and Mtaa wa Kwa Mjumbe Nyumba Kumi. 
Medium-intensity hotspots were observed near Ruta Pub, 
whereas moderate ones were found around Engaruka, 
close to GAP Co. Limited, and near Mafuta Road Street. 
The crime hotspot map further revealed that Minazini 
Street—surrounded by bus stops and bordered by major 
roads like Mandera and Kilwa—had a high concentration 
of  unplanned buildings, which contributed to criminal 
behavior (Masese, 2007).

CONCLUSION
Crime locations within the study area were systematically 
mapped, leading to the identification of  several crime 
hotspot zones. These included road corridors, bars, 
schools, catering establishments, bus stops, cash 
depot areas, pubs, and churches. Furthermore, the 
analysis revealed that socioeconomic factors such as 
unemployment, low levels of  education, income disparity, 
and family breakdown played a significant role in driving 
individuals toward criminal activities as a means of  
survival.

Acknowledgment. 
We extend our sincere gratitude   to Emeritus Professor 

Figure 8: Burglary crime hotspot map in the study area from 2005 -2022

and along Mandela Road. Based on field observations 
in Kurasini Ward, the study found that robbery hotspots 
commonly occurred near luxury-oriented locations, 
particularly food establishments and pubs located along 

accessible roadways. For example, notable hotspots were 
concentrated along Mandela Road and Mafuta Road, 
especially near Wimo Maasai Pub, Ommy Food, the Police 
Canteen, and Chipsi kwa Tiktok Chipsi and Snack.



Pa
ge

 
10

3

https://journals.e-palli.com/home/index.php/ajgt

Am. J. Geo Spat. Technol. 4(1) 95-103, 2025

I. A. Kahwa (Emeritus Professor of  Chemistry, TWAS) 
and Professor E. Kahwa for their generous financial 
support, which made this research possible. Our heartfelt 
appreciation further goes to all the respondents from 
Kurasini Ward for their participation and meaningful 
contributions to the success of  this study.

REFERENCES
Ahmed, M., & Salihu, R. (2013). Spatiotemporal pattern 

of  crime using Geographic Information System 
(GIS) approach in Dala L.G.A of  Kano State, Nigeria. 
American Journal of  Engineering Research (AJER), 2(3), 
51–58.

Creswell, J. W. (2014). Research design: Qualitative, quantitative 
and mixed methods approaches (4th ed.). Sage.

Creswell, J. W., Fetters, M. D., & Ivankova, N. V. (2004). 
Designing a mixed methods study in primary care. 
The Annals of  Family Medicine, 2(1), 7–12. https://doi.
org/10.1370/afm.104

Mswela, M. (2019). Tagging and tracking of  persons with 
albinism: A reflection of  some critical human rights 
and ethical issues arising from the use of  the Global 
Positioning System (GPS) as part of  a solution to 
cracking down on violent crimes against persons 
with albinism. Potchefstroom Electronic Law Journal, 
22(1). https://doi.org/10.17159/1727-3781/2019/
v22i0a6462

Okeyo, G. (2021). Advancing crime prevention, criminal 
justice and the rule of  law: Towards achievement of  
the 2030 agenda. Country Statement of  the Republic of  

Kenya at the Fourteenth United Nations Congress on Crime 
Prevention and Criminal Justice, Kyoto, Japan.

Silverman, D. (2000). Doing qualitative research: A practical 
handbook. Sage Publications Ltd.

Stylianides, T. (2000). Crime analysis and decision support in the 
South African Police Service: Enhancing capability with the 
aim of  preventing and solving crime. DACST Innovation 
Fund Project.

Taylor, R. B., & Gottfredson, S. (1986). Environmental 
design, crime and prevention: An examination of  
community dynamics. In A. J. Reiss & M. Tonry 
(Eds.), University Press Southern Africa.

United Nations Office on Drugs and Crime, & The World 
Bank. (2003). Crime and criminal justice statistics. https://
www.unodc.org/unodc/en/data-and-analysis/
statistics/crime.html

United Nations Office on Drugs and Crime. (2009). 
Victimization survey. https://www.unodc.org/unodc/
en/data-and-analysis/statistics/crime.html

United Nations. (2014). World crime index by country: World 
crime report for the period of  2014–2015.

UNODC. (2013). UNODC homicide statistics. https://
www.unodc.org/unodc/en/data-and-analysis/
homicide.html

Welman, C., Kruger, F., & Mitchell, B. (2011). Research 
methodology (3rd ed.). Oxford University Press.

Yelwa, S., & Bello, Y. (2012). Complementing GIS 
with cluster analysis in assessing property crime in 
Katsina State, Nigeria. American International Journal of  
Contemporary Research, 2(7).


