Applied Geography最新文献

Enhancing urban inclusivity is a crucial task for sustainable urban development. One key challenge is to create seamless and accessible urban space that caters to the mobility needs of people, especially the elderly. Addressing this pressing challenge requires a comprehensive overview of barrier-free facilities (BFFs) configuration within cities. In this study, we propose an integrated framework for assessing the BFFs in Beijing by leveraging open BFFs data and mobile phone data. The proposed framework encompasses considerations of spatial distribution, accessibility, and supply-demand patterns of BFFs. Our findings reveal the significant agglomeration and heterogeneity of BFFs in Beijing, as well as serious spatial and social inequalities. Furthermore, the supply-demand relationship of BBFs in Beijing is relatively good, with 77.8% of supply-demand balanced areas. Overall, our study provides a holistic understanding of the BFFs within Beijing, offering valuable insights for informed decision-making in inclusive city planning and development.

Ecosystem service (ES) flow can address mismatches between the supply and demand of ESs. Nevertheless, how to improve ES flow considering spatial flow information and interactions among different flow systems is a research gap. Taking the Beijing-Tianjin-Hebei (BTH) urban agglomeration region of China as an example, this study combined both system and network perspectives to analyze the ES flow of grain provision before and after optimization. Metacoupling system analysis was adopted to assess intra-regional and extra-regional flow. Linear programming was used to calculate the optimal distance cost flow solution with constraints. A network model was finally applied to build and analyze flow and transmission networks. In 2020, BTH participated in nearly 10% of the national flow, among which 57% was extra-regional flow. After optimization, the proportion of extra-regional flow decreased to 36%, all of which was inflow from the nearest provinces, while intra-regional flow increased by 35%. The optimized flow reduced distance costs by 143% and decreased network complexity. Core transmission nodes with both high degree and betweenness centrality played prominent connection roles in the process of flow. Strengthening regional connections and simultaneously effectively managing core transmission nodes are of great significance for improving flow efficiency and ensuring food provision.

The water-related ecosystem services (ESs) flow is a pivotal linkage between ecological and social systems, playing an integral role in water security. Previous research has primarily assessed water security in contexts with and without the presence of ES flows, but further research is necessary to understand the influence of water yield inflow(WY_inflow) and total nitrogen inflow(TN_inflow) as critical determinants of water security. To address dynamic water security assessment and its driving mechanisms, we developed a novel water security index (WSI) that incorporates water quality and ES flows. Additionally, we employed a spatial Durbin model to investigate the impact and spatial effects of related factors on water security, applying it to the Yiluo River Watershed in China. The results revealed that: 1) the dynamic WSI of sub-watersheds incorporating ES flows was an improvement over the static WSI; 2) precipitation and the proportion of cropland significantly positively impacted static WSI, but their impact on dynamic WSI was not significant; 3) WY_inflow intensity contributed positively to dynamic WSI, while TN_inflow intensity negatively influenced dynamic WSI; and 4) most determinants of water security, including WY_inflow and TN_inflow intensity, exhibited negligible spatial spillover effects. Notably, the contribution of ES flows to water security showed an upward trend, accounting for approximately 30% by 2018. Such insights are crucial for formulating sustainable management strategies, such as ecological compensation, to enhance long-term regional water security.

Understanding the dynamics of built-up land amidst the urban-rural transformation process is crucial for balancing the benefits of urbanization against the challenges of uncontrolled expansion. Yet, how built-up land develops across different types of human settlements, particularly within emerging Global South megaregions (GSMs), is not well documented. Employing an urban-rural gradient (URG) approach, we investigate built-up land dynamics across six representative emerging GSMs spanning 1985–2020, contextualizing our findings within urbanization theory and sustainable development discourse. Our analysis reveals that urban center growth and peri-urbanization drive a substantial proportion of built-up land expansion (36.47% and 27.39%, respectively), in contrast to the minimal increases observed in rural clusters and semi-dense areas (2.48% and 2.44%, respectively). The predominant expansion mode is sprawling growth, notably evident in peripheral areas, while densifying growth is mainly confined to urban centers and mostly uninhabited areas. Sprawling shrinkage is observed in dense and semi-dense urban areas, as well as rural clusters. These nuanced dynamics illustrate the varied ways in which regional territories engage with and are shaped by the urbanization process. Through the lens of the URG analysis, our study enhances understanding of spatial transformations in the GSMs, offering informative insights for fostering sustainable cities and human settlements.

Although many scholars have revealed daily commuting satisfaction is related to both psychological factors and travel quality and efficiency under objective travel environment, few studies have investigated the relationship between accessibility and satisfaction. To fill this research gap, this paper explores whether accessibility is in accordance with satisfaction with daily commute (SWDC), and the dominant factors influencing SWDC in different time periods. Given the important objective role of accessibility, this paper used 1512 valid questionnaire data and API traffic big data from Xi'an, China, and employed the Satisfaction with Travel Scale (STS) covering three domains, and hierarchical regression methods to calculate the cumulative opportunity accessibility for four transport modes (walking, cycling, car, and public transport [PT]). The results show that the accessibility of the four transport modes was consistent with SWDC within 20-min. There were significant changes in car and PT within 40- and 60-min traffic circles; however, accessibility and SWDC results for these two modes were inconsistent. Furthermore, we found that travel attitude had the greatest impact on SWDC for walking and cycling within 20 min. Analysis of SWDC for car and PT in three time periods (i.e., 20 min, 20–40 min, and 40–60 min) showed that SWDC with car travel was only significantly affected by the built environment within 20–40 min, whereas travel characteristics played a dominant role in the other two time periods. For PT, SWDC was most affected by built environment, travel characteristics, and travel-related attitude.

Population data is crucial for policy decisions, but fine-scale population numbers are often lacking due to the challenge of sharing sensitive data. Different approaches, such as the use of the Random Forest (RF) model, have been used to disaggregate census data from higher administrative units to small area scales. A major limitation of the RF model is its inability to quantify the uncertainties associated with the predicted populations, which can be important for policy decisions. In this study, we applied a Bayesian Additive Regression Tree (BART) model for population disaggregation and compared the result with a RF model using both simulated data and the 2021 census data for Ghana. The BART model consistently outperforms the RF model in out-of-sample predictions for all metrics, such as bias, mean squared error (MSE), and root mean squared error (RMSE). The BART model also addresses the limitations of the RF model by providing uncertainty estimates around the predicted population, which is often lacking with the RF model. Overall, the study demonstrates the superiority of the BART model over the RF model in disaggregating population data and highlights its potential for gridded population estimates.

The recent oil palm expansion has resulted in significant land losses for rural communities globally, raising concerns about food security, poverty, and the loss of common resources. This study investigates whether community-based tenure regimes in Mexico, particularly ejidos, prevent land grabbing and land concentration in oil palm producing regions. By mapping oil palm plantation types (smallholdings, mid-sized and large-scale plantations) across major land tenure regimes (ejido, communal and private property) using high spatial resolution imagery from Google Earth and ESRI/Maxar, we explore the relationship between tenure forms and land concentration. Our findings suggest that ejido lands largely prevents land grabbing by oil palm, although neoliberal reforms have favored land concentration, especially under private tenured land (southern Campeche) but also in some ejidos facing illegal land-based investments (Lacandon rainforest). This research contributes to broader debates on oil palm, tenure regimes and land grabbing, highlighting the need for land tenure policies that protect rural communities from industrial plantation encroachments.

Social media is a pervasive part of everyday life. Neighbourhood social media are important community orientated structures that serve as digital platforms where local residents can connect with neighbours, exchange information, and share resources. The current study details an analytic framework to systematically capture, measure and map neighbourhood social media (Facebook groups) presence for a large metropolitan region, Brisbane, Australia. Further through modelling we reveal how socially organised communities acquire a higher number of neighbourhood-based social media groups while socially disorganised communities tend to have social media groups associated with crime or crime watch. We also unveil important spatial patterns with more neighbourhood-based social media groups located in coastal areas that are associated with tourism, leisure activities and recreational pursuits. Our findings demonstrate that neighbourhood-based social media is an important component of community social infrastructure and can support collective capacity to respond to problems. Our hope is that our approach can be replicated in other situational and cultural contexts to assemble a growing set of comparative studies through which the spatial distribution of locality-based social media can be assessed.

This paper leverages store location data and analysis software from retail industry vendors to analyze the unique geographies of electronic commerce (e-commerce) offerings among major grocery chains in the United States. Join-Count statistics are calculated to identify patterns of clustering among offerings, and a spatial probit model is implemented to regress various trade area characteristics on a given grocery banner's decision to offer some form of e-commerce (pickup, delivery, or both) or no offering at a given store location. A regional case study of the Southeastern United States demonstrates how model results can shift based on scale, pointing to unique regional dynamics that deviate from broader national trends. This analysis carries implications for location planning and competitive defense strategies in the grocery industry, and also establishes avenues for future research.

Fine particulate matter (PM_2.5) is a major health and environmental concern, with significant spatiotemporal dynamics in urban areas. Low-cost air quality sensor (LCS) networks offer a paradigm-changing opportunity to acquire high spatiotemporal resolution data, revealing the urban pollution landscape with sufficient detail for effective policymaking and health assessment. This study advances geospatial air quality research by using classic and spatial Markov chains to analyze the seasonality and intra-daily variations of PM_2.5 using LCS data. Results highlight distinctive PM_2.5 seasonality, with the “Good” state predominating in summer and being least common in winter. Midday is the peak period for the “Good” state, while mornings and nights have poorer conditions, suggesting a need for stricter pollution control during evening traffic rush hours. Notably, the impact of temporal scale on spatial Markov analysis is substantial, showing a broader range of air pollution states, increased stability, and reduced variation between time intervals compared to daily assessments. Site-level analysis reveals that rural sites are more likely to maintain “Good” state and less likely to transition out of it. Overall, this study highlights the effectiveness of high spatiotemporal resolution data and demonstrates the capacity of Markov chains to reveal nuances in phenomena such as air pollution.