Volume 27, Issue 80, July - December, 2026

Spatio-Temporal Patterns of Human–Leopard Conflict at the Urban–Forest Interface of Jaipur, India

Aishwarya Mahajan¹, Joy Gardner², Nikhil Sharma³, Saurabh Kumar Gupta⁴, Suraj Kumar Singh^5♦

¹Centre for Climate Change & Water Research, Suresh Gyan Vihar University (SGVU), Jaipur, Rajasthan, India, 302017
²Hope and Beyond Wildlife Conservation NGO, Jaipur, Rajasthan, India
³St. Wilfred's P.G. College, Sector-10, Meera Marg, Madhyam Marg, Mansarovar, Jaipur, Rajasthan – 302020, India
⁴Centre for Climate Change & Water Research, Suresh Gyan Vihar University (SGVU), Jaipur, Rajasthan, India, 302017
⁵Centre for Climate Change & Water Research, Suresh Gyan Vihar University (SGVU), Jaipur, Rajasthan, India, 302017

^♦Corresponding Author
Suraj Kumar Singh, Centre for Climate Change & Water Research, Suresh Gyan Vihar University (SGVU), Jaipur, Rajasthan, India

ABSTRACT

Human and leopard conflicts are being reported more and more in the growing urban areas of India, but there are still relatively few studies that use spatial data to identify conflict hotspots which could then be used to direct management efforts. This study uses an exploratory, descriptive GIS-based hotspot analysis of humanleopard conflict in Jaipur city and its surroundings based on 81 conflict incident records during 2018–2025. Conflict records were obtained from the Rajasthan Forest Department, Jaipur Zoo and verified media reports and were analysed in QGIS and ArcGIS for annual trend, seasonal pattern, diurnal distribution, land-use / land cover (LULC) association and spatial clustering. The dataset is small (n = 81 over 8 years) and should be treated as a descriptive baseline of reported incidents rather than a complete count of human-leopard interactions. Reported incidents are subject to reporting bias since urban incidents are more likely to be reported than rural ones. There is also geolocation uncertainty when village or locality centroids were used instead of exact locations. With these caveats in mind, the analysis yielded the following descriptive findings. The annual mean was 10.13 ± 5.38 incidents with no significant monotonic trend (Kendall’s τ = −0.265, p = 0.373). The majority of the conflicts were urban in nature (47 cases, 58.0%), then came rescue operations (21, 25.9%), followed by livestock depredation (5, 6.2%), human injury (4, 4.9%) and road mortality (4, 4.9%); the distribution of conflict types was significantly different from what would be expected by chance (χ² = 86.10, df = 4, p < 0.001). There was a significant concentration of conflicts in the winter season (37.0%) and in the postmonsoon season (32.1%) (χ² = 13.47, df = 3, p = 0.004), as well as during the evening and early-night hours (41.6% of conflicts occurring between 18:00 and 23:59 h; twosample Kolmogorov-Smirnov test comparing day and night: D = 0.653, p < 0.001). The locations of the conflicts were much more concentrated in built-up and urban areas (79.2% of the incidents compared with 62.3% of the mapped area; p = 0.3730, p = 0.3731, p = 0.3732; observed-to-expected ratio = 1.27), although this could partly be due to a higher probability of reporting in urban areas rather than indicating a real ecological preference for such areas. Kernel Density Estimation (KDE) indicated that the top 10% of density values accounted for 11.21 km² and included 59.0% of the incidents, and the Average Nearest Neighbour analysis confirmed that there was significant clustering (NNI = 0.615, z = −6.41, p = 0.3733); however, these spatial statistics must be interpreted in view of the geolocation uncertainty and reporting bias noted above. There were only a small number of cases of human injuries (p = 0.3734) and leopard deaths on the roads (p = 0.3735), which meant that a formal trend analysis of the severity of conflicts was not necessary. Although the number of injury incidents after 2022 was more apparent, they still do not indicate a statistically significant increase in severity. The results as a whole suggest that a focused, landscape-wide strategy for achieving human-leopard coexistence should be adopted, this strategy including hotspot-based monitoring, outreach campaigns customised to season and time, rapid-response measures, and actions to reduce road mortality. In future studies it would be necessary to use larger datasets based on probability, independent data on leopard abundance, carry out a bandwidth-sensitivity analysis, and validate the findings against known leopard movement corridors in order to strengthen the conclusions.

Keywords: Human–leopard conflict; hotspot mapping; kernel density estimation; urban–forest interface; Jaipur; Jhalana Leopard Reserve; GIS; seasonal pattern; diurnal activity; wildlife–vehicle collision; reporting bias; exploratory spatial analysis

Species, 2026, 27(80), e29s3309

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Published: 07 November 2026