Efficient spatial allocation of municipal solid waste (MSW) collection infrastructure is essential for improving urban environmental management, service accessibility, and territorial equity in high-density metropolitan areas. In this study, the territorial and operational performance of MSW collection point optimization was assessed through the application of a Christaller-based hexagonal model in Los Olivos District, Lima, Peru. A quantitative, applied research approach was adopted under a comparative cross-sectional and non-experimental design, in which the existing collection configuration was systematically compared with an optimized spatial scenario. The baseline system consisted of 23 temporary accumulation/storage points (K3) distributed under operationally functional but territorially heterogeneous conditions, whereas the optimized configuration comprised 59 proposed K3 points spatially redistributed to enhance accessibility and service uniformity. The optimized network was generated using a regular hexagonal tessellation with a a circumradius of $R$ = 300 m per K3 point, corresponding to a pedestrian service radius/apothem of $\sim$260 m. Each hexagonal service unit covers approximately 0.2338 km$^2$, which was subdivided into 16 analytical zones. Spatial modelling and geostatistical processing were conducted using official cartographic datasets, population density layers, collection route information, Geographic Information Systems (GIS), Euclidean distance matrices, and geometric optimization procedures. Performance was evaluated using key indicators, including primary collection points/containers (K2)–K3 distance, K3–main road distance, estimated collection time, average MSW load per K3 point, population coverage, and territorial coverage. Statistical consistency was assessed using paired Student’s $t$-tests and Wilcoxon signed-rank tests with a significance threshold of 1%. Under the optimized scenario, the average K2–K3 distance was reduced from 413.78 m to 224.38 m, corresponding to a 45.76% reduction. The average MSW load per K3 point decreased by 61.03%, estimated sector-level collection time decreased from 11.50 min to 6.79 min, and territorial coverage increased from approximately 32% to 77%. Population coverage also increased from 33.17% to 78.88%. These findings suggest that the Christaller-based hexagonal model provides a scalable and reproducible spatial planning framework for optimizing MSW collection infrastructure in densely populated urban districts.

This study evaluates the spatial exposure of road infrastructure and road users to nomadic herder–farmer conflicts (HFCs) in Nigeria, focusing on four critical nomadic pastoral corridors (NPCs): Kaduna–Plateau (North West–North Central, NW–NC), Taraba–Benue (North East–North Central, NE–NC), Nasarawa–Benue (North Central–North Central, NC–NC), and Benue–Enugu (North Central–South East, NC–SE). Using geospatial data, the study develops a Road Exposure Index (REI) by integrating road density, population density, and conflict density across local government areas within these NPCs. Kernel density estimation (KDE) was applied in ArcGIS to visualize the spatial distribution of REI values and identify areas of heightened exposure during HFC-affected years. The results reveal substantial variations in REI across the corridors, with tertiary roads in the Benue–Enugu (NC–SE) and Nasarawa–Benue (NC–NC) corridors showing particularly high levels of exposure. These areas are important because they include major food-producing zones, suggesting that HFC-related road exposure may have implications for food accessibility and rural–urban market connectivity. The findings highlight the need for targeted intelligent transport system (ITS) infrastructure and security interventions to improve road monitoring, enhance transport resilience, and reduce mobility risks in conflict-affected corridors in Nigeria.

As globalization deepens and the digital economy advances, Shandong Province, one of China’s major economic regions, is undergoing shifts in its population structure alongside changes in its economic growth drivers. Exploring the impacts of education support, technological progress, and population aging on regional economic development is of great significance for formulating scientific regional economic policies. Based on macro-level data for Shandong Province from 2004 to 2022, this study constructs a double-logarithmic regression model. The ordinary least squares (OLS) method is used to estimate the model parameters, and stepwise regression is applied to address the multicollinearity problem. The empirical results show that fiscal education expenditure is significantly and positively associated with regional gross domestic product (GDP), suggesting that education support may play an important role in promoting regional economic development. Population aging also shows a significant positive association with regional economic development in the corrected model, indicating that the silver economy and the continued utilization of older adults’ human capital may provide new development opportunities. However, the direct effect of technological progress, measured by technology market turnover, is not fully verified in the current model. The findings suggest that Shandong Province should continue to prioritize education development, improve elderly-care industries and flexible retirement mechanisms, and transform demographic challenges into potential drivers of regional development.