The Central Region of Kenya has undergone significant changes in land cover due to a broad range of drivers. These changes are more pronounced in forestland conversions. Past researches within the study area have identified drivers of land cover change without quantifying the influence of these drivers. Predictor variables include population density, precipitation, elevation, slope, forest fires, soil texture, proximity to roads, rives and towns. Land cover changes were analyzed using multi-temporal land cover maps between year 1990 and 2014. Boosted regression trees model was applied to determine the significant drivers and quantify their relative influence on key forestland transitions. The local and spatial influence of the drivers has further been analyzed by geographical weighted regression using coefficients determined at each sample point. Significant land cover changes continuously occurred over the study period. Forestland reduced from 38.90% in 1990 to 38.14% in 2014. Grassland reduced from 32.59 to 22.57%, cropland increased from 28.05 to 38.83% and wetland changed from 0.07 to 0.04%. Other land which constitutes of bare land and built up increased from 0.38 to 0.42%. The results show population density had the highest contribution to forestland changes throughout the study period, with a minimum contribution of 20.02% to a maximum of 26.04%. Other significant variables over the study period are precipitation, slope, elevation and the proximity variables. The results indicate that the relative influence of the drivers to forestland conversion varies with time, location and type of transition.

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The main objective of this study was to investigate the linkage between land use and alcohol outlet density and crime in Juja sub-county, Kenya. Crime data ( n = 1560) was obtained from the Juja Police Station for the years 2017, 2019, 2020, and 2021. Land use land cover classification of our study area was performed to obtain land use classes (commercial, agricultural, forest, grassland, industrial, residential, and waterbody), and zonal operations at the zone level ( n = 233) were performed to obtain summary values of each land use type per zone. Alcohol outlet density was also calculated at the zonal level. Population was identified as a crime determinant factor in addition to land use and alcohol outlet density. From these factors, the 4 most significant ones (residential, agricultural, population, and off-premise outlet density) were identified using ordinary least squares (OLS) model. This revealed that off-premise alcohol outlet density had a statistically significant negative relationship with crime, while residential areas had the highest statistically significant positive relationship with crime. While on-premise alcohol outlet density did demonstrate the highest positive coefficient with crime, this relationship was not statistically significant. This infers that while on-premise alcohol outlet density may explain crime in areas where such establishments are dense, with high crime rates, they may not explain crime in other areas of the sub-county that equally recorded high-density crime rates. The random forest algorithm was then adopted to predict crime from the most significant variables.

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