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.

Flooding is a recurrent problem in rapidly developing urban districts such as Lokogoma, Abuja, where inadequate drainage, poor land-use planning, and weak infrastructure maintenance increase flood vulnerability. This study assessed the causes, impacts, and effectiveness of existing flood mitigation measures in Lokogoma District, Abuja, Nigeria. A mixed-methods approach was adopted, involving field observation, questionnaire surveys, stakeholder interviews, secondary data review, and geographic information system (GIS)-assisted mapping. A total of 381 questionnaires were administered to residents in flood-prone residential areas, and the data were analyzed using descriptive statistics, mainly frequencies and percentages. The findings show that 79% of respondents had been affected by flooding, while 53.3% reported that flooding occurs frequently. Poor drainage conditions were identified as the leading cause of flooding, accounting for 27.6% of responses, followed by building on waterways and improper planning, each accounting for 22.0%. The major impacts of flooding included disruption of daily activities, economic loss, loss of personal belongings, health issues, property damage, and displacement from homes. Existing mitigation measures included drainage systems, sandbags/barriers, building elevation, flood walls, community education, early warning systems, vegetative barriers, land-use planning, and flood insurance. However, respondents expressed mixed views about their effectiveness, with 44.9% rating the measures as effective or very effective and 39.4% rating them as ineffective or very ineffective. The study concludes that flooding in Lokogoma is driven by inadequate drainage capacity, poor maintenance, improper waste disposal, weak development control, and limited community participation. It recommends upgrading secondary and tertiary drainage systems, improving solid waste management, enforcing land-use and building regulations, conducting regular drainage maintenance, reviewing flood risk assessments, and strengthening public awareness and community participation. These measures can enhance flood resilience and support more sustainable urban development in Lokogoma District.

This paper advanced a new methodological framework for understanding and classifying the urban–rural continuum in post-2000 South Africa. The abolition of administrative distinctions through the Municipal Systems Act in 2000 rendered traditional definitions of “urban” and “rural” obsolete, thus creating a conceptual gap in spatial classification. Drawing on the international frameworks, the study proposed a functionally grounded approach that transcends the urban–rural binary. Using a positivist design and quantitative spatial modelling, the research introduced the Dominant Impact Factor (DIF), a composite indicator integrating population size, participation of labour force, and economic production, to assess relative municipal dominance. Municipalities were subsequently categorized through a quartile-based classification into urban, mixed, and rural types, and further refined using geo-referencing and a spatial grid for fine-scale spatial differentiation. Findings revealed pronounced demographic and economic concentration in a small number of highly urbanized municipalities, contrasted with extensive and sparsely populated rural territories. The framework reconceptualized settlement systems as dynamic, relational, and functionally interlinked rather than dichotomous. The study aligns the spatial classification practice in South Africa with globally methodological standards, to offer a robust, transparent, and scalable tool for evidence-based planning, governance, and formulation of policy.

The Nusantara Capital City (IKN) in Indonesia has undergone rapid urbanization which threatens sustainable forest management and the well-being of the indigenous community, leading to conflicts over land rights and resources. This study explored trust, norms, social networks, proactive action, and care between indigenous and migrant forest communities in IKN to support collaborative governance. It contributed to forest governance research by applying social capital theory to a protected urban area under state-led development. The research explained differences in household participation and offered a framework connecting bonding and bridging ties to co-management. Surveys based on the Social Capital Assessment Tool (SCAT) and the Social Capital Integrated Questionnaire (SC-IQ) were conducted with 90 households (45 indigenous and 45 migrant) across six villages in Penajam Paser Utara and Kutai Kartanegara districts from March to September 2024. Spearman’s rank tests was employed to analyze relationships between traits and social capital. The analysis results indicated that both communities possessed strong social capital, particularly trust in leaders (scores 3.49–3.56) and norms (scores 3.53), yet demonstrated moderate trust in government and environmental commitment. Migrants generally have higher education, income, land ownership, and bridging social capital, whereas indigenous groups maintain strong bonding capital rooted in tradition and legitimacy of local leaders. Traits significantly correlated with social capital (indigenous: r = 0.756, p < 0.001; migrants: r = 0.823, p < 0.001). Overall speaking, effective forest city development depended on government policies, local leadership, environmental awareness, transparency, and acknowledgment of customary governance, as these elements could foster community-based forest management and equitable urban development in tropical forest areas.

Rapid urbanization in Bangladesh has exponentially exacerbated environmental stressors, most notably in Dhaka and Rajshahi, where climate-related concerns are becoming more prevalent. This study adopted geographic information system (GIS) and remote sensing techniques to delineate and assess climate risk zones in Dhaka City Corporation (DCC) and Rajshahi City Corporation (RCC) in 2020 and 2024. The evaluation involved the incorporation of land use/land cover (LULC), land surface temperature (LST), and air pollution indicators. Sentinel-2A multispectral imager (MSI) was used to calculate LULC, Landsat-8 optical land imager (OLI) for LST, and Sentinel-5P for atmospheric pollutants, such as NO₂, SO₂, CO, and PM_2.5. The analysis revealed that the built-up land in Dhaka was expanded by 4.38% whereas in Rajshahi, it was 8.91%. Rajshahi recorded a maximum LST of 46.7°C in 2024, when compared to 37.6°C in Dhaka. The level of air pollution was consistently high in Dhaka, with an average concentration of NO₂ reaching 36.4 µmol/m², almost quadrupled the 9.81 µmol/m² in Rajshahi. Weighted overlay analysis demonstrated that 5.38% and 1.63% of the areas in Dhaka and Rajshahi, respectively, were categorized as very high-risk zones in 2024. The very low-risk zones accounted for less than 1.5% in both cities. These findings suggested significant regional differences in urban climate risk as Dhaka was experiencing more severe circumstances, due to dense urbanization and rising pollution levels. The study unraveled the potential of GIS and remote sensing-based multi-parameter integration for urban climate risk zoning, as well as the establishment of city-specific adaptation and mitigation measures in Bangladesh.