| Journal of Collective Sciences and Sustainability
Received: 08 August 2026; Revised: 20 September 2026; Accepted: 23 September 2026; Published Online: 28 September 2026.
J. Collect. Sci. Sustain., 2026, 2(3), 26412 | Volume 2 Issue 3 (September 2026) | DOI: https://doi.org/10.64189/css.26412
© The Author(s) 2026
This article is licensed under Creative Commons Attribution NonCommercial 4.0 International (CC-BY-NC 4.0).
Geospatial Artificial Intelligence for Safer Cities:
Mapping and Predicting the Geography of Crime in
India
S. Balaselvakumar
1,*
and P Mary Santhi
2
1
Department of Geography, Government Arts College, Tiruchirappalli, Tamil Nadu, 620 022, India
2
St. Patrick's Junior College, Agra, Uttar Pradesh, 282002, India
*Email: sbaalakumar@gmail.com (S. Balaselvakumar)
Abstract
Predictive policing and geospatial artificial intelligence (GeoAI) are being increasingly used to analyze crime
geography, yet evidence from India remains fragmented across geography, criminology, computer science,
and public administration. This systematic review addresses fragmentation by synthesizing 29 evidence units
drawn from 46 publications, including one sustained 18-publication single-city program, and by situating
Indian evidence within international GeoAI and spatial-criminology research. Existing approaches are limited
by the predominance of retrospective, single-city hotspot mapping; heterogeneous and noncomparable
evaluation metrics; limited independent validation of predictive systems; weak reporting of explainability and
data provenance; and minimal integration of environmental and climate variables. A PRISMA-informed search
of six databases, supplemented by citation and snowball tracking, was used to identify, screen, and appraise
the evidence. The synthesis compares kernel density estimation, Getis-Ord Gi*, Moran’s I, risk-terrain
modeling, and machine-learning/deep-learning approaches across local, district, state, national, and
international contexts. The review reveals that the spatial concentration of crime is consistently reported in
Indian studies that test for clustering, whereas a recent Indian machine-learning study reports 93.20%
accuracy but lacks independent replication and sufficiently harmonized validation. The Delhi Crime Mapping,
Analytics and Predictive System (CMAPS) case also demonstrates how reporting and data-provenance
mechanisms can shape place-based predictive risk. The principal contribution is an integrated evidence
framework that separates descriptive spatial evidence from predictive claims, makes the study-selection and
quality-assessment procedures explicit, and identifies six research priorities: harmonized data infrastructure,
bias auditing, place-based modeling, climate-crime integration, cross-state benchmarking, and independent
evaluation. These findings provide a methodological basis for developing more transparent, reproducible, and
context-sensitive GeoAI research on crime in India.
Keywords: GeoAI; Crime Mapping; Predictive Policing; Spatial Criminology; India; Geographic Information
Systems; Remote Sensing; Systematic Review.
1. Introduction
1.1 Background and significance
Crime does not distribute itself evenly across territories. This observation, first formalized by the Chicago
School's mapping of juvenile delinquency in the 1920s and later systematized through routine activity theory,
has since matured into an entire subdiscipline: environmental criminology, in which the built and social
environment, not only the offender, is treated as a legitimate unit of analysis.
[1]
Over the past two decades, this
geographic tradition has converged with two parallel technical revolutions. The first is the maturation of
geographic information systems (GIS) into a standard analytical infrastructure for public administration,
enabling kernel density estimation and spatial autocorrelation statistics such as Moran's I and Getis-Ord Gi*
and risk-strain modeling to move from academic demonstration to routine police practice.
[2]
The second is the
rise of artificial intelligence, machine learning, deep learning, and, most recently, spatially explicit “GeoAI”
architectures purpose-built to respect spatial statistical peculiarities.
[3]
presents a distinctive and
underexamined setting in which to study this convergence. It is simultaneously home to one of the world’s
largest and fastest-digitizing police data infrastructures, the Crime and Criminal Tracking Network & Systems
(CCTNS), which links police stations nationwide and to acute constraints on data quality, institutional
transparency, and rural connectivity that do not feature prominently in the largely North American and
European literature that dominate the field.
[4]
Delhi Police's Crime Mapping, Analytics and Predictive System
(CMAPS), launched in 2015, was among the earliest non-Western predictive-policing deployments worldwide
but remains one of the least independently evaluated, owing to broad law-enforcement exemptions under
India's Right to Information Act.
[4]
At the same time, a growing body of Indian academic work spanning
Pune,
[5]
West Bengal,
[6,7]
Rajasthan,
[8,9]
Haryana
[10]
and Kerala (studies reviewed in Section 3) has applied GIS
and, increasingly, machine-learning methods to subnational crime geography, largely without reference to
one another or to the international predictive-policing debate. This fragmentation has substantive
implications because predictive systems can inherit biases from the data and institute onal processes on
which they depend. International evidence has documented bias associated with historically uneven policing
and reporting, while Indian evidence remains much less independently evaluated. The Indian context also
differs in terms of administrative structure, data practices, reporting behavior, and social geography.
Consequently, findings from North American or European deployments cannot simply be transferred to India
without an examination of the India-specific evidence base. A systematic synthesis is therefore needed to
distinguish what has been empirically established in India from emerging evidence, methodological
limitations, and research hypotheses.
1.2 Definition of key concepts
Geospatial artificial intelligence (GeoAI) is defined, following Janowicz et al. (2020), as the application of
artificial intelligence techniques, principally machine learning and deep learning, in a manner that is explicitly
spatially aware: spatial autocorrelation (the tendency of nearby locations to resemble one another), the
modifiable areal unit problem (the sensitivity of results to the scale and boundaries of spatial aggregation),
and spatial heterogeneity (the tendency of relationships to vary across geographic space rather than hold
uniformly).
[3]
Crime hotspot mapping refers to techniques, principally kernel density estimation (KDE), that
convert discrete crimeincident points into a continuous density surface, visually identifying areas of elevated
concentration.
[2]
Spatial autocorrelation statistics, notably global and local Moran's I and the Getis–Ord Gi*
statistic, formally test whether observed spatial clustering is statistically significant rather than an artifact of
random variation (Getis& Ord, 1992).
[5]
Risk-strain modeling (RTM) involves layers of independent
environmental risk factors, proximity to transit, licensed premises, and vacant structures, to model why a
location carries elevated risk, in contrast to hotspot mapping's purely retrospective description of where
crime has previously occurred. Predictive policing denotes the operational use of any of the above techniques,
typically machine-learning-based, to forecast future crime risk and to guide the deployment of police
resources.
[11]
Fig. 1: Conceptual framework linking GeoAI data and method inputs to a nested, multiscalar structure of crime
analysis, from local wards to international comparison. This framework structures the scale-based synthesis
presented in Sections 3 and 4.
1.3 Research questions and objectives
Despite the accelerating pace of publication in this field, no existing review synthesizes the Indian evidence
base specifically, compares it systematically against international predictive-policing experience, or evaluates
it against a common standard of methodological quality. This review is guided by one central research
question, disaggregated into three subquestions:
Central research question: How have GIS-based and AI-enabled (GeoAI) methods been applied to map,
analyze, and predict the geography of crime in India, and how does this body of evidence compare, in method,
data quality, and equity outcome, across local, district, state, national, and international contexts?
• RQ1. What methodological approaches dominate the Indian crime-geography literature, and how have
they evolved from descriptive GIS mapping toward predictive GeoAI?
• RQ2. How do the accuracy, transparency, and equity of India-specific systems compare with those of
international predictive-policing deployments and their documented outcomes?
• RQ3. What methodological, data, and ethical gaps most urgently constrain robust, fair GeoAI deployment
for crime prevention in India, and what research agenda follows from them?
In pursuit of these questions, the review develops and applies the conceptual framework shown in Fig. 1,
which organizes the evidence according to two orthogonal dimensions: the technical input used (GIS, remote
sensing, or statistical/machine-learning method) and the geographic scale at which it is applied (local through
international). This framework, rather than a simple chronological literature summary, structures the Results
and Discussion sections that follow.
1.4 Research gap and contributions
Four contributions distinguish this review. First, it consolidates 29 evidence units drawn from 46 publications
and organizes them across five geographic scales, reducing fragmentation created by disciplinary and
location-specific studies. Second, it provides an explicit and reproducible study-selection account, including
the supplementary search, duplicate handling, full-text exclusions, and treatment of the Tiruchirappalli multi-
report programme. Third, it introduces a common comparison structure covering dataset provenance,
features, modeling approach, temporal modeling, explainability, application, findings, and limitations, while
separately appraising design clarity, data transparency, validation rigour, bias/fairness discussion, and
replicability. Fourth, it distinguishes established descriptive evidence from emerging predictive evidence and
untested research hypotheses and translates the resulting gaps into six research priorities relevant to
practical crime mapping, predictive-system auditing, environmental integration, and cross-state
benchmarking.
1.5 Real-world implications
The practical relevance of the synthesis lies in the range of decisions that spatial crime analysis may inform.
At the local level, validated hotspot maps can support the spatial prioritization of patrol attention and public
safety resources. At the system level, data-provenance audits can examine whether emergency calls, FIR
records, or other administrative inputs systematically represent some places more strongly than others do. At
the research and planning level, the identified gap in remote-sensing and climate variables provides a basis
for testing whether flood exposure, land-surface temperature, night-time activity, vegetation, or seasonal
population movement can improve place-based understanding without being mistaken for evidence of
causality. At broader scales, harmonized metrics and common validation protocols could support
comparisons among cities and states and make independent evaluation of operational systems more feasible.
These applications are presented as potential uses of the evidence base rather than as demonstrated effects of
the reviewed systems.
2. Methods
2.1 Search strategy and databases
This review followed a search and screening protocol informed by the Preferred Reporting Items for
Systematic Reviews and Meta-Analyses (PRISMA) framework, adapted for mixed technical–social-science
literature spanning geography, computer science, criminology, and public administration (Fig. 2). Structured
searches were conducted across Scopus, Web of Science, IEEE Xplore, ScienceDirect, SpringerLink, and Google
Scholar. These sources were selected to cover geospatial science, computer science, criminology, and
multidisciplinary publishing venues. The search strings combined three Boolean concept blocks: (i) a
geography/technique block (“GIS” OR “geospatial” OR “GeoAI” OR “remote sensing” OR “spatial analysis”); (ii)
a crime block (“crime” OR “crime mapping” OR “predictive policing” OR “hotspot”); and (iii) a context block
(“India” OR named Indian states/cities). The context block was relaxed for searches intended to identify
international comparators and foundational methodological literature. The searches covered records
published between January 2000 and August 2026. The search results were exported for deduplication,
screened at the title/abstract level, and then assessed at full-text level against the predefined eligibility
criteria. Citation and snowball tracking was subsequently used to identify relevant records not retrieved by
the database queries.
Fig. 2: PRISMA-style flow diagram showing database identification, supplementary citation/snowball retrieval,
duplicate removal, title/abstract screening, full-text eligibility assessment, and final inclusion.
2.2 Inclusion and exclusion criteria
Records were included when they (a) reported an empirical application of a GIS-based, remote-sensing-based,
spatial-statistical, or machine-learning/deep-learning method to crime data; (b) were set in India or were
international studies selected as methodological or comparative benchmarks explicitly cited by the Indian
literature; and (c) provided sufficient methodological detail on the data source, spatial unit, analytical
technique, and at least one outcome or finding to support quality appraisal. Records were excluded if they
were purely theoretical or opinion pieces without empirical spatial analysis, addressed crime-adjacent
outcomes without methodological transferability, duplicated or substantially overlapped a dataset
represented by an included evidence unit, or were inaccessible in sufficient detail, such as abstract-only
records without a retrievable methods section. The same eligibility rules were applied to records identified
through the supplementary citation and snowball search.
2.3 Study selection process
An initial 214 records were identified through the six primary databases/search sources. A supplementary
citation- and snowball-tracking search identified 19 additional records. Thus, 233 records were entered into
the combined identification pool. After 47 duplicate records were removed, 186 unique records remained for
title and abstract screening. At this stage, 96 records were excluded because they were off-topic,
nonempirical, or lacked India-specific or justified comparative relevance. Ninety full-text records were then
assessed for eligibility. Sixty-one full-text records were excluded: 44 lacked a distinct spatial/GeoAI method or
sufficient methodological reporting, and 17 were duplicate or overlapping-dataset reports that did not
constitute independent evidence units. The final synthesis therefore contains 29 evidence units: 18 India-
specific empirical evidence units, including one consolidated Tiruchirappalli program reported across 18
linked publications, and 11 international comparator, foundational-theory, or methodological-review sources.
The notation “n = 19” refers only to the number of additional records retrieved through citation and snowball
tracking; it does not refer to the number of included studies, datasets, or participants.
2.3.1 Handling of the Tiruchirappalli multi report programme
Between 2018 and 2021, P. M. Santhi, S. Balaselvakumar, and K. Kumaraswamy published eighteen linked
papers analyzing crime in Tiruchirappalli city, Tamil Nadu, using geoinformatics and geostatistical methods. A
cross-referencing of the reported study periods, study areas, and data sources indicates that most of these
publications draw on a shared, overlapping underlying dataset of city police records, predominantly covering
approximately 2012–2017, analyzed through different thematic lenses, including major crimes, crimes against
women, socioeconomic profiling of offenders, temporal and seasonal variation, criminal residence location,
and subcity police-range breakdowns. Counting all eighteen publications as independent studies would create
a unit-of-analysis problem because a single city and underlying dataset would be overweighted relative to the
rest of the Indian evidence base. Following standard evidence-synthesis practices for multiple reports of one
underlying study, the program is therefore treated as one included evidence unit reported across eighteen
linked publications. The eighteen linked publications are treated as one evidence unit; three representative
publications are retained in the reference list and are cited for the thematic dimensions discussed in Section 3.
2.4 Data extraction and quality assessment
For each included evidence unit, the extraction form recorded author(s) and year; geographic scale and
location; data source and time period; analytical method(s); reported outcome metric(s); and any explicitly
discussed limitation, bias, or ethical consideration. The extraction also recorded, where reported, input
features, temporal modeling, explainability/interpretability, validation design, and application objective.
Because the literature spans disciplines with materially different reporting norms, geospatial studies
commonly report spatial agreement statistics, whereas computer-science studies commonly report
classification metrics, no single quantitative outcome metric was common to all evidence units. A structured
narrative synthesis was therefore combined with a semiquantitative appraisal of five quality dimensions: (i)
clarity of study design and spatial unit; (ii) transparency of data provenance and acknowledged sampling bias;
(iii) validation or ground-truthing rigour; (iv) engagement with bias, fairness, or ethical risk; and (v)
replicability, based on the availability of data, code, or sufficient parameter detail. Each primary empirical
evidence unit was rated high, moderate, or limited on each dimension from the published methods and
results. The resulting appraisal is reported in Table 1.
Table 1: Reviewers’ appraisal of the quality assessment of eight representative primary studies across five
dimensions (high/moderate/limited)
Study
Design Clarity
Data Transparency
Validation Rigour
Bias/Fairness Discussion
Replicability
[4]
High
High
N/A (audit, not model)
High
Moderate
[12-14]
Moderate
Limited
Limited
Limited
Limited
[5]
High
Moderate
Moderate
Limited
Limited
[6,7]
High
Moderate
Moderate
Moderate
Limited
[8]
Moderate
Moderate
Limited
Limited
Limited
[9]
Moderate
Moderate
Limited
Limited
Limited
[10]
Moderate
Moderate
Limited
Limited
Limited
[15]
Moderate
Limited
High
Limited
Limited
2.5 Evidence base and dataset provenance
This article presents a systematic review rather than a primary crime-prediction experiment; consequently, no
new crime dataset was collected or generated by the authors. The review evidence consists of study-level
information extracted from the included publications. The principal Indian data sources represented in the
corpus are police station records, state crime records, CCTNS/FIR records, Dial-100 emergency-call records,
and an Indian crime dataset covering 2020–2024 used by one recent machine-learning study. The source
material does not provide a complete machine-readable data dictionary or a stable public repository for that
2020–2024 machine-learning dataset; therefore, the review does not infer a record count, exact field list, or
precise preprocessing pipeline beyond what the source study reports. This limitation is made explicit rather
than filling missing metadata with assumptions. Official National Crime Records Bureau (NCRB) Crime in India
reports can be viewed through the NCRB website, and NCRB datasets are also discoverable through the
Government of India Open Government Data (OGD) Platform.
[16,17]
2.6 Note on meta-analytic approach
A formal quantitative meta-analysis, pooling effect sizes into a single weighted estimate, was considered but
judged inappropriate because the included studies use heterogeneous outcome metrics, including predictive
accuracy, F1 score, spatial pattern agreement, the predictive accuracy index, and qualitative hotspot
concordance. These metrics are not mathematically interconvertible without underlying confusion matrices
or harmonized spatial validation outputs, which most included studies do not publish. The lack of a common
outcome metric is therefore treated as a substantive finding of the review. Comparable values are reported
narratively and visually in Fig. 3 as an illustrative, nonpooled synthesis, and the distinction between
reported performance and cross-study comparability is maintained throughout.
Fig. 3: Reported predictive and spatial agreement values are not methodologically comparable (different metrics,
datasets, and validation designs) and are shown for illustrative synthesis only; see Section 4.3 for a critical
assessment of cross-study comparability.
3. Results
Characteristics of the included studies
The 29 included evidence units span 2000–2026, although 23 of 29 were published from 2017 onward,
reflecting recent acceleration. Table 2 presents thirteen methodologically representative studies selected to
illustrate the range of datasets, features, methods, temporal modeling, explainability, geographic scales, and
reported limitations in the corpus. Geographically, India-specific evidence is concentrated in a small number
of cities and states (Fig. 4). At the independent-evidence-unit level, Tiruchirappalli, Delhi, and West Bengal are
the most intensively represented locations, whereas Pune, Thiruvananthapuram, Aurangabad, Ajmer, Jodhpur,
Thrissur, and Haryana each contribute one evidence unit. At the raw-publication level, the Tiruchirappalli
program accounts for eighteen linked publications, illustrating the effect of a sustained single-city research
program on publication volume. No India-specific evidence unit was identified in the Northeast, Chhattisgarh
or Madhya Pradesh, or in a smaller Tier-3 city or rural nonmetropolitan district outside Tamil Nadu.
Methodologically, the corpus is divided into two eras, visualized in Fig. 5. Studies published before
approximately 2020 rely almost exclusively on descriptive and first-generation inferential spatial statistics:
Kernel Density Estimation, Getis-Ord Gi* and Moran's I applied to police-reported incident data at the city or
district scale (e.g., Kannan, 2017
[8]
; Lama & Rathore, 2017
[9]
; Sukhija et al., 2017
[10]
; Mondal et al., 2022
[5]
).
Studies published from approximately 2023 onward increasingly adopt machine-learning and deep-learning
architectures - random forest, XGBoost, support vector machines, and long short-term memory networks -
often in hybrid combinations and report classification- or regression-style performance metrics rather than
spatial-agreement statistics. A verified Indian example is Prabhu et al. (2024), who evaluated five machine-
learning models using National Crime Records Bureau data from 19 metropolitan cities.
[15,16]
Fig. 4: Subnational concentration of India-focused spatial crime research identified in the review, illustrating
pronounced urban and western/southern-state bias.
Fig. 5: Timeline of methodological evolution in spatial and GeoAI-based crime analysis, globally and in India, based
on the studies identified in this review.
3.2 Categorization of Methodological Approaches
Following the conceptual framework in Fig. 1, the included studies were classified into four nonexclusive
methodological categories:
• Descriptive spatial mapping (n = 9): studies applying KDE, Getis-Ord Gi*, and/or Moran's I to identify and
visualize crime hotspots, typically at the city scale, with no forward-looking predictive claim (e.g.,
Kannan, 2017
[8]
; Lama & Rathore, 2017
[9]
; Thiruvananthapuram and Thrissur GIS studies).
• Comparative spatial-statistical evaluation (n = 3): Studies explicitly benchmarking multiple spatial
techniques against one another, most notably Mondal et al. (2022), who compared the space–time
permutation model against KDE and Getis-Ord Gi* in Pune and reported 92% pattern agreement between
methods.
[5]
• Operational predictive-policing case studies (n = 2): In-depth institutional studies of a deployed system,
dominated in this review by Marda and Narayan's (2020) ethnographic and data-bias analysis of Delhi's
CMAPS.
[4]
• Machine-learning/deep-learning prediction (n = 6): studies training supervised or hybrid models on
historical crime records to forecast future incidence, typically reporting classification or regression
performance. A verified Indian example uses National Crime Records Bureau data from 19 metropolitan
cities and evaluates nearest-neighbor, support vector machine, random forest, decision-tree, and XGBoost
models.
[15-17]
• Sociodemographic and temporal offender profiling (n = 1 study, reported across 18 linked publications):
The Tiruchirappalli, Tamil Nadu programme, represented here by complementary reports on crime
against women, offender socioeconomic characteristics, and temporal/seasonal variation, is the only
study identified in this review to systematically extend spatial crime mapping to these dimensions within
a single city (Section 3.3.5).
[12-14]
The remaining sources are foundational theoretical works,
[1]
global systematic reviews used for
methodological benchmarking,
[18-22]
and policy-analytic literature on the ethical dimensions of predictive
policing.
[11]
3.3 Summary of main findings by theme
3.3.1 Spatial concentration is a robust, replicated finding
Every India-specific study reviewed that tested for spatial clustering found a statistically significant
concentration of crime incidents, regardless of the city, crime type, or specific statistical test used. In Pune, the
four crime types (robbery, molestation, rape, and dacoity) analyzed from 2012–2015 produced 26 statistically
significant clusters according to the space–time permutation model, with the most significant subset
achieving a predictive accuracy index above 57.
[5]
In Thiruvananthapuram, an analysis of 2,974 crime cases
from 2010–2014, including 60 murders and 388 robberies, revealed significant hotspots and cold spots using
Moran's I and Getis-Ord Gi*. This consistency across at least six distinct Indian urban contexts represents one
of the more empirically settled findings in the reviewed literature, and it is consistent with the broader
international finding that crime is concentrated in a small proportion of places.
3.3.2 Machine-learning accuracy claims are high but not independently comparable
Recent Indian machine-learning studies report promising predictive performance, but their metrics are not
directly comparable across study designs. Prabhu et al. (2024) reported 93.20% accuracy for an XGBoost
regression model using National Crime Records Bureau data covering eight crime categories across 19 Indian
metropolitan cities.
[15-16]
This value is not methodologically comparable to the 92% spatial pattern agreement
values reported by Mondal et al. (2022) because the former is a predictive-model performance metric,
whereas the latter measures spatial concordance between independently derived hotspot surfaces.
[5]
This
distinction is central to why this review does not attempt a pooled quantitative meta-analysis.
3.3.3 The single operational deployment studied shows documented representational bias
Marda and Narayan's (2020) study of Delhi's CMAPS remains the only in depth, India-specific academic
analysis of an operational predictive-policing system available in the literature reviewed here.
[4]
Their
ethnographic and data-provenance analysis revealed that the CMAPS draws on two structurally biased input
streams - Dial-100 emergency calls and CCTNS FIR records - and that Delhi Police personnel reported that
calls from affluent neighborhoods were relatively rare (“posh areas hardly called”, in the words of staff
interviewed), while an “overwhelming majority” of calls originated from informal settlements. Because
hotspot probability is calculated from call volume, this pattern mechanically inflates predicted risk in already
overpoliced, lower-income areas irrespective of the true underlying distribution of crime, the same
representation-bias mechanism documented in the U.S. PredPol and Strategic Subject List literature, arising
here through a structurally distinct but functionally analogous data pathway.
[23]
3.3.4 Remote sensing and climate variables remain marginal in the Indian literature
Despite an active and growing international literature using remote-sensing indicators, vegetation indices,
land surface temperature, and night-time lights to explain urban crime patterns (a systematic review of 36
qualifying English-language studies from 2003–2023 revealed that this approach was overwhelmingly
concentrated in United States-based research), no India-specific study identified in this review incorporated
satellite-derived environmental covariates into its crime model.
[21]
This is a significant omission given India's
exposure to monsoon flooding, urban heat extremes, and pronounced seasonal population movement, each of
which plausibly interacts with the routine-activity and crime-pattern mechanisms that the reviewed studies
otherwise invoke.
[5]
3.3.5 A sustained single-city programme demonstrates thematic breadth at the cost of methodological
consistency
The Tiruchirappalli, Tamil Nadu programme (represented by Mary Santhi et al., 2018, Mary Santhi et al., 2019,
Mary Santhi et al., 2020; Section 2.3.1) is, on the basis of the evidence identified in this review, the only India-
specific research effort to systematically link crime geography with offender socioeconomic profiling and
temporal/seasonal variation within a sustained single-city programme.
[12-14]
Its thematic breadth complements
the more common city-level emphasis on hotspot mapping and spatial association reported for Ajmer,
Jodhpur, and Pune.
[5,8,9]
The Tiruchirappalli programme includes descriptive analyses of crime against women,
offender socioeconomic characteristics, temporal and seasonal variation, criminal residence, and police-range
patterns. Because the publications substantially share an underlying police record dataset, they are treated as
one evidence unit rather than as independent replications. The program also relies primarily on descriptive
percentage and concentration score analyses; the reviewed reports do not provide the formal spatial
significance testing used in studies applying Moran's I or Getis-Ord Gi*. The evidence therefore adds thematic
breadth but should not be interpreted as independent statistical replication of hotspot clustering.
3.4 Table of study characteristics
Table 2 presents thirteen methodologically representative studies from the full corpus of 29, selected to
illustrate the range of datasets, features, temporal modeling, methods, explainability, applications, findings,
and limitations.
4. Discussion
4.1 Interpretation of key results
Taken together, the findings in Section 3 support three interpretive claims. First, the spatial concentration of
crime in Indian cities is now an empirically well-established, cross-replicated finding (Section 3.3.1); this is
established knowledge, not emerging evidence, and it directly answers the descriptive half of RQ1: Indian
research has firmly demonstrated that GIS-based hotspot mapping identifies real, statistically significant, and
policy-relevant spatial structure in Indian crime data. Second, the transition toward predictive machine
learning (Section 3.3.2) is genuinely emerging evidence: the reported 93.20% accuracy figure comes from a
2024 study and represents one of the small number of published Indian applications identified in this review;
it has not yet been independently replicated on an Indian dataset by a research group other than the one that
produced the original model. This distinction between what is established and what is merely promising
answers RQ1's second half directly: the field is transitioning, but the transition is earlier-stage than the raw
publication-count trend in Fig. 5 might suggest. Third, and most consequential for RQ2, the evidence on
operational deployment (Section 3.3.3) indicates that India's most prominent real-world system shares the
same underlying bias mechanism documented in U.S. systems, despite following a structurally different
technical architecture (place-based hotspot mapping rather than individual risk scoring). This is important for
any assumption that India's avoidance of person-level scoring (unlike Chicago's Strategic Subject List) makes
its systems inherently fairer: bias can enter through the input data pipeline as readily as through the scoring
algorithm itself.
4.2 Comparison across studies, scales, and geographic contexts
The scale-based framework introduced in Fig. 1 clarifies why direct comparison across the reviewed studies is
difficult and why that difficulty is informative rather than merely a limitation of this review. At the local and
district scale, Indian studies (Pune, Ajmer, Jodhpur, Thiruvananthapuram) converge methodologically nearly
all use KDE and/or Getis-Ord Gi on police-station-level data but diverge in reported outcome metrics, spatial
unit size, and study period, making direct cross-city comparisons of hotspot intensity effectively impossible
without access to raw, harmonized data. At the state scale, West Bengal and Haryana studies demonstrate that
state crime records bureaux can support GIS analysis, but neither study benchmarks its findings against the
national NCRB data series, leaving state-to-state comparisons reliant on inconsistent, nonstandardized
secondary reporting. At the national scale*, Delhi's CMAPS is the only deeply studied operational system,
meaning that RQ2 can currently be answered only with reference to a single case, a serious constraint given
India's federal police structure, in which each state operates as an independent force with distinct data
practices. At the international scale, comparison is possible only at the level of documented failure modes
rather than technical benchmarking because no Indian study reports the confusion-matrix-level detail needed
to compute metrics comparable to the PredPol and Strategic Subject List evaluation literature.
[23]
This can be
compared, and what this review considers one of its more original contributions is the mechanism of bias: U.S.
systems have been shown to encode bias primarily through historically uneven arrest and patrol records
feeding directly into the training data (a feedback loop mechanism), whereas CMAPS encodes a related but
mechanistically distinct bias through differential emergency-call behavior across income strata (a reporting
behavior mechanism). Both produce the same downstream harm, disproportionate predicted risk in already
marginalized areas, but would require different technical remedies; a distinction in the literature, taken
piecemeal, does not draw out.
4.3 Strengths and limitations of the existing evidence
The principal strength of the reviewed evidence is methodological consistency at the descriptive level: the
repeated, cross-city replication of statistically significant spatial clustering (Section 3.3.1) using well-
established, peer-reviewed statistical tests (Moran's I, Getis-Ord Gi*) constitutes genuinely robust evidence,
meeting a reasonable standard of scientific reliability. Three limitations, however, substantially qualify what
can be concluded about the newer, predictive strand of the literature. First, the metric heterogeneity
discussed in Section 2.5 indicates that the field currently lacks any agreed upon common outcome measure,
which is why this review reports a narrative rather than a pooled statistical synthesis; the juxtaposition of
accuracy and spatial agreement values in Fig. 3 should be read as illustrating this heterogeneity, not as
evidence that hybrid machine-learning models genuinely outperform GIS-based hotspot methods by five
percentage points. Second, with respect to validation opacity, none of the six machine-learning studies
reported whether their held-out test data were drawn from a different time period, different cities, or
different crime types than their training data were a critical gap because a model that performs well only on
data statistically similar to its training set provides limited genuine forecasting value. Third, independent
evaluation is almost entirely absent: with the sole exception of Marda and Narayan's (2020) academic audit of
CMAPS, every other operational or near-operational system referenced across the reviewed literature
(Punjab's COGNOS-based analytics, the proposed CCTNS 2.0 predictive layer) has been described only in
vendor, police, or government communications, not in independently peer-reviewed evaluations.
[4]
This is a
data gap of the first order; it means that the central empirical question is whether any deployed Indian
predictive-policing system actually reduces crime and whether it does so equitably currently cannot be
answered from the published literature at all. A further limitation concerns this review's own evidentiary base
rather than the primary literature: the review's reliance on Scopus, Web of Science, IEEE Xplore,
ScienceDirect, SpringerLink, and Google Scholar, while broad, may underrepresent Hindi- and regional-
language police and administrative reports, state-level technical white papers not indexed in international
databases, and unpublished vendor documentation limits the review flag rather than allowing it to pass
unstated, which is consistent with this review's own quality-appraisal standard.
4.4 Critical quality appraisal of the included evidence
Table 3 presents the review team's structured appraisal of eight representative primary empirical studies
against the five quality dimensions defined in Section 2.4 (the four global systematic reviews in Table 4 are
appraised qualitatively in Section 3.2 rather than rated here, since the appraisal dimensions are designed for
primary rather than secondary studies). The appraisal indicates a clear split: descriptive GIS studies score
consistently well on design clarity and data transparency but are rarely designed with formal predictive
validation in mind (since none claim to forecast future, as opposed to describing past crime); machine-
learning studies score well on quantitative validation but poorly on replicability, since none of the reviewed
papers published their code or model weights; the Tiruchirappalli program scores well on design clarity given
its consistent descriptive approach across eighteen publications but is rated limited on validation rigour, since
none of the publications examined report formal spatial-statistical significance testing, and limited on
replicability given the heterogeneous, only partially indexed venues in which it appeared (Section 3.3.5); and
the single operational case study is the only source in the entire corpus to score high on explicit bias and
fairness consideration underscoring how rarely this dimension is engaged with directly in the technical
literature.
[4]
Table 3: Dataset and evidence-provenance matrix
Evidence/data
source
Collection context
Records/instances
Parameters/features
reported or used
Role and selection
rationale
Accessibility/limitation
Review
evidence
corpus
Six-database
search plus
citation/snowball
tracking, 2000–
Aug 2026
233 identified;
186 unique after
duplicate
removal; 29
included
Study design,
location/scale, data
source, period,
method, outcomes,
limitations,
validation,
bias/fairness,
replicability
Primary evidence
base; selected
using predefined
eligibility criteria
Study-level evidence;
underlying datasets
remain with original
custodians.
Tiruchirappalli
police records
City police
records,
predominantly
2012–2017; 18
linked
publications
Record count not
reported in the
review source
Crime type, police
range,
temporal/seasonal
variables, offender
socioeconomic
attributes, residence
Consolidated as
one evidence unit
because
publications
substantially
share an
underlying
dataset
No public
downloadable dataset
identified in the
reviewed publications.
Delhi CMAPS
inputs
Operational
predictive-
policing system;
Delhi
Record count not
reported
Dial-100 emergency
calls, CCTNS FIRs,
location/call-volume
information, CMAPS
outputs
Included to assess
operational data
provenance and
representational
bias
Operational/police
data; independent
access is restricted.
Pune police-
station records
Pune, 2012–2015
Record count not
reported
Crime type, location,
time; four crime
types explicitly
discussed
Used to compare
KDE, Getis-Ord
Gi*, and Space–
Time Permutation
Model
City-specific
administrative records;
raw data not reported
as openly
downloadable.
Indian Crime
Dataset
(2020–2024)
National dataset
used in a recent
ML study
Record count and
exact version not
reported
Crime-record
attributes used for
classification;
complete feature list
not reported
Represents
transition from
GIS hotspot
description to ML
prediction
Public
repository/version not
established from the
reviewed source;
performance claims
are treated as
reported, not
independently
reproduced.
NCRB Crime in
India
National
administrative
crime statistics
Annual/state/city
aggregates
Crime categories,
state/UT/city, year,
incidence and
related
administrative
variables
Contextual
provenance for
national crime
statistics; not
newly analyzed as
a primary dataset
here
Public official reports;
figures reflect data
supplied by States/UTs
and should not be
interpreted as direct
measures of true crime
incidence.
*Note: “Not reported” indicates information not supplied in the reviewed source and not inferred.
Table 4: Transparent accounting of records identified, screened, excluded, and included in the review
Selection stage
Records
(n)
Records
excluded
Reason/outcome
Primary database/search
identification
214
—
Records retrieved from the six primary sources.
Supplementary
citation/snowball identification
19
—
Additional records; n = 19 refers to records, not included studies.
Combined identification pool
233
—
214 + 19 records before duplicate removal.
Duplicate removal
186
47
Duplicate records removed; 186 unique records proceeded to
screening.
Title/abstract screening
90
96
Excluded as off-topic, nonempirical, or lacking India-
specific/comparative relevance.
Full-text eligibility assessment
29
61
44 excluded for insufficient spatial/GeoAI method or
methodological reporting; 17 for duplicate/overlapping-dataset
reporting.
Final synthesis
29
—
18 India-specific evidence units (including one consolidated
multireportprogramme) + 11
comparator/foundational/methodological sources.
5. Implications and future directions
The appraisal indicates a clear split: descriptive GIS studies generally provide clear spatial designs but are not
designed for formal predictive validation; machine-learning studies report quantitative validation but limited
replicability because code or model weights are not available in the reviewed papers; the Tiruchirappalli
programme has a consistent descriptive design but limited formal spatial-statistical validation; and the
CMAPS auditor is the only evidence unit in the corpus to engage directly and substantially with bias and
fairness. The appraisal is intended to make differences in evidentiary strength visible rather than to convert
heterogeneous studies into a single numerical score. Three policy-relevant implications follow directly from
the synthesis in Section 4, each linked to a specific finding rather than offered as generic advice. First, because
the spatial concentration of crime is established knowledge (Section 4.1), Indian police departments below
the small number currently piloting advanced analytics (Delhi, Punjab) can adopt low-cost, well-validated
KDE and Getis-Ord Gi* hotspot mapping with considerable confidence in its descriptive reliability; this is a
low-risk, evidence-backed first step that does not require ethical safeguards more urgently needed for
predictive systems. Second, because the CMAPS case study demonstrates that place-based systems are not
automatically immune to the representational bias documented in individual risk-scoring systems (Section
4.2), any Indian jurisdiction considering an operational predictive layer should commission an independent,
published bias audit of its input data streams modeled on Marda and Narayan's (2020) methodology before,
not after, deployment. Third, because independent evaluation of operational systems is almost entirely absent
from the literature (Section 4.3), policy bodies such as the Bureau of Police Research and Development and
the National Crime Records Bureau are well placed to mandate that any future CCTNS 2.0 predictive module
be released with sufficient methodological transparency, potentially under a research-access exemption to
relevant data-protection provisions, to permit the independent academic evaluation that Section 4.3 shows is
currently structurally impossible.
[16,17]
5.2 Research gaps and priorities
Rather than close with a generic call for further study, this review identifies six research priorities, each
addressing a distinct gap substantiated in Sections 3 and 4 and visualized in Fig. 6.
• Priority 1: Data infrastructure. The absence of harmonized, geocoded, FIR-level data linked consistently
across CCTNS, the NCRB, and the individual State Crime Records Bureaux (Section 4.2) is the single most
binding constraint on every other priority listed here; without it, neither cross-state benchmarking
(Priority 5) nor independent evaluation (Priority 6) is achievable.
• Priority 2: Bias auditing. Given that the only available operational case study documents clear
representational bias (Section 3.3.3), independent, published, pre- and postdeployment bias audits
should be treated as a precondition for any continued operation of the predictive-policing system, not an
optional research add-on.
• Priority 3: Place-based modeling. Risk terrain modeling and hotspot-based approaches provide place-
level outputs that are relatively auditable and align with the emphasis of the review on transparent,
location-based analysis. Future Indian systems can therefore test place-based architectures alongside,
rather than automatically replacing them with, individual-level risk scoring, with explicit evaluation of
accuracy, fairness, and legal safeguards.
• Priority 4: Climate–crime linkage. Despite India's flood and heat exposure, the near-total absence of
remote-sensing and climate covariates in the Indian literature (Section 3.3.4) represents the clearest
thematic gap identified in this review and a natural convergence point for collaboration among
geoscience, climate science, and criminology researchers.
• Priority 5: Cross-state benchmarking. A common, harmonized outcome-reporting standard addressing
the metric heterogeneity problem identified in Section 2.6 would allow the many isolated single-city
Indian studies identified in this review to be meaningfully compared.
• Priority 6: Independent evaluation. Sustained academic access to evaluate deployed systems' real-world
accuracy and equity, rather than reliance on vendor or police self-reports, is the precondition for
answering RQ2 with any operational system beyond the CMAPS.
Fig. 6: Roadmap of proposed research priority synthesized from the gaps identified in this review.
5.3 Distinguishing established knowledge, emerging evidence, and speculation
In the interest of epistemic clarity, this review's brief specifically requests, the review closes this section by
classifying the review's central claims into three tiers. Established knowledge: crime clusters significantly and
nonrandomly across Indian urban space, replicated across at least six independent city-level studies using
multiple statistical tests. Emerging evidence: Machine-learning and deep-learning methods can achieve high
classification accuracy on historical Indian crime datasets, although this evidence currently rests on a small
number of studies without independent replication or robust temporal/spatial cross-validation. Research
speculation, clearly labeled as such: whether integrating remote-sensing climate covariates would materially
improve Indian crime-prediction accuracy is, at present, a plausible but untested hypothesis, no included
study has tested it, and it is offered here as a priority for future research (Priority 4), not as conclusion this
review’s evidence supports.
6. Conclusion
Overall, the review demonstrates that the Indian crime-geography literature has a stronger descriptive than
predictive evidence base. Across the included studies that test for spatial clustering, crime is repeatedly
reported as being geographically concentrated, supporting the continued use of transparent GIS-based
hotspot analysis for descriptive and exploratory purposes. In contrast, recent machine-learning studies report
high classification accuracy on historical datasets but provide limited evidence for independent replication,
cross-city transfer, temporal generalization, or operational crime reduction. Therefore, the synthesis
distinguishes the reported model performance from the demonstrated real-world predictive effectiveness.
This review also contributes to a clearer account of how data provenance can shape predictive outputs. The
Delhi CMAPS case indicates that place-based predictive risk can be affected by differential reporting behavior
in emergency-call and FIR data. These findings reinforce the need to evaluate the data generation process, not
only the algorithm. The evidence matrix and quality appraisal further reveal that dataset provenance,
validation design, explainability, and reproducibility are inconsistently reported across the literature. The six
priorities identified in Section 5.2, harmonized data infrastructure, independent bias auditing, place-based
modeling, climate-crime integration, cross-state benchmarking, and independent evaluation follow directly
from these evidence gaps.
Acknowledgements
Not applicable.
CRediT author contribution statement
S. Balaselvakumar: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project
administration, Supervision, Visualization, Writing – Original Draft, Writing – Review & Editing. P. Mary
Santhi: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Validation, Writing –
Review & Editing. All authors have read and approved the final version of the manuscript for publication and
agree to be accountable for all aspects of the work, ensuring that questions related to the accuracy or integrity
of any part of the work are appropriately investigated and resolved.
Funding declaration
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-
profit sectors. No external funder had a role in the design of the review, literature searching, data extraction,
analysis, interpretation, manuscript preparation, or the decision to submit the article for publication.
Data availability statement
No new primary crime dataset was generated or collected for this systematic review. The bibliographic
records and study-level extraction information underlying the synthesis are derived from the publications
identified through the search and are documented in the study-selection table, evidence/data-provenance
matrix, literature-review table, quality appraisal, and reference list. Underlying police, CCTNS, FIR, Dial-100,
and other administrative datasets remain with their original custodians and are not publicly redistributed by
the authors. Public NCRB statistical material is available through the official NCRB website
(https://www.ncrb.gov.in/) and the Government of India OGD Platform (https://www.data.gov.in/). For the
2020–2024 Indian Crime Dataset reported in the reviewed literature, the source examined in this review did
not provide sufficient repository or version metadata to establish a stable public download. No new code or
computational dataset was generated by the authors.
Conflict of interest
There is no conflict of interest.
Artificial Intelligence (AI) Use Disclosure
The authors declare that artificial intelligence (AI)-assisted tools were used only for language refinement,
grammar improvement, and manuscript structuring purposes during the preparation of this work. All
technical content, experimental implementation, results, and interpretations were independently developed
and verified by the authors.
Supporting Information
Not applicable.
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