Landslides are global natural hazards with significant social and economic impact. The study is conducted in the wake of landslide occurrences in Kerala state, India, during 2018–2019. This study aims to demarcate landslide hotspots in the study area, identify anthropogenic activities, and critically examine their role in landslide occurrences. The identified landslide hotspots are utilized to comprehend spatial patterns of landslide distribution throughout the state. The landslide hotspots are concentrated in Idukki, Ernakulam, Kottayam, Wayanad, Kozhikode and Malappuram districts. Anthropogenic conditioning factors stimulating landslide occurrences are identified from land-use activities and the results obtained manifest that about 59.38% of total landslides have occurred in the plantation area. Human-induced parameters accelerating landslides are examined along with other natural parameters using various regression methods in the latter part of the study. The modelling techniques used in the study are the ordinary least square (OLS), spatial autoregressive model (SAR), and geographically weighted regression (GWR). All models point to the fact that anthropogenic activities such as plantation, quarry, road density, and cropland influence landslide occurrences. Results of both spatial models give excellent predictive capability compared to the OLS model. SAR analyzes the spatial interaction of parameters globally, whereas GWR considers the local aspect. The study encapsulates the importance of global and local spatial models in landslide studies based on their applicability.

$\bf{Highlights}$

$\bullet$ The landslide hotspots of the state can be classified into two clusters, and they are concentrated in Idukki, Ernakulum, Kottayam, Wayanad, Kozhikode and Malappuram districts of the state.

$\bullet$ All hotspots recline in the Western Ghats region.

$\bullet$ About 59.38% of total landslides in Kerala have occurred in the plantation area.

$\bullet$ 64.76% of the state's total landslides have happened in the human-modified land-uses.

$\bullet$ According to the spatial models, presence of human-modified landscapes has influence in landslide occurrences.

$\bullet$ However, it was found that the model performance of the global regression models (SAR) is higher than local regression model (GWR); predicted value of landslides are more accurate in GWR model.