Applied Geography最新文献

Housing price appreciation is an important socioeconomic phenomenon that captures the complex socioeconomic dynamics of a city. Variation in housing price appreciation across neighborhoods reflects localized housing demand and supply-side factors. This study develops quality-adjusted, census tract-level housing price indices using a fine-grained big dataset containing a total of 140,289 housing transactions in the County of Los Angeles. We employ the SHapley Additive exPlanations (SHAP) technique, an explainable artificial intelligence framework, to examine the underlying demographic and socioeconomic factors that help in explaining the variance in tract-level housing price appreciation from 2012 through 2018 in the County of Los Angeles. The novelty of the methodology lies in the local interpretation of spatial patterns it provides from big data in the urban context and in assessing how the factors influencing housing price appreciation vary geographically. The modeling framework could help planners in making informed decisions about local geographic contexts that contribute to variability in housing price appreciation in cities.

The cities within city clusters have strong trade connections, resulting in spatial heterogeneity and correlation of carbon balances. Previous studies have not deeply explored the characteristics of the spatial association network (SAN) of carbon balance in urban agglomerations, particularly regarding network structure, node effects, and spatial-temporal inhomogeneities. This study investigated the spatial network characteristics of carbon balance in the Beijing-Tianjin-Hebei (BTH) region from 2000 to 2019, employing a modified gravity model and social network analysis (SNA). The findings revealed the following: 1) Carbon emissions increased by 106.42%, and carbon sinks increased by 31.06%, displaying spatial-temporal heterogeneity and forming a multi-level, multi-nodal SAN of carbon balance centered around Beijing. 2) The carbon balance was mainly influenced by spatial spillovers occurring at different nodes, typically moving from lower-tier to higher-tier nodes. 3) Cities assumed four roles: bidirectional spillover, net spillover, primary beneficiary, and agent, with these roles being dynamic. 4) The spatial correlation of carbon balance was primarily influenced by economic development (max 0.602), spatial distance (max 0.331), and per capita ecological land (max 0.445). This approach would refine carbon management policies and deepen the understanding of the SAN and its influencing factors, providing insights for optimizing carbon emission reduction management.

While evolutionary economy geographers have paid increasing attention to the new industrial path creation in emerging economies, less has been done to compare the path creation between core and peripheral areas at the sub-regional level in the Global South. By taking the digital economy industry in Guangzhou City as a case, this study reveals that given the different initial conditions, innovative entrepreneurship, institutional entrepreneurship and place-based leadership have paid distinct and dynamic roles in different specific contexts and phases. In the well-developed core district (Tianhe) gathered universities and research institutes, innovative entrepreneurs have acted as initial drivers for the development of the digital economy industry at the early path creation phase, while institutional entrepreneurs also provided support to enhance the industrial agglomeration in the following periods. In the late-developing peripheral district (Huangpu) with the lack of innovative resources, institutional entrepreneurship and place-based leadership are crucial to nurturing the new digital industry in the early phase, while conducive industrial assets and innovative entrepreneurs are also needed to cultivate the development of new industries in the future. Therefore, a longitude and multi-actor approach is needed to understand regional industrial path creation in different geographical contexts.

The implicit development of consumption space in buildings relying on the O2O business mode is a new form of urban renewal under experience economy and e-commerce. The concept of implicit consumption space emerged as a result. There is a lack of research on implicit consumption space and this study aims to fill this gap. Using POI data and spatial analysis methods, the paper studies the clustering characteristics and influencing factors of the explicit and implicit consumption space in the Old City of Nanjing to provide practical implications for urban renewal, and finds that the implicit consumption space grows fast and has strong clustering, and its layout basically follows the traditional commercial location theory. Commercial districts, office buildings and traffic accessibility are the common influencing factors for the distribution of explicit and implicit consumption spaces. Explicit and implicit consumption space influence each other in layout, while implicit consumption space has a stronger dependence on explicit consumption space. The influencing factors for the agglomeration of explicit and implicit consumption space have spatial variations. The implicit consumption space is an important path for the bottom-up renewal of the old city, and also provides an opportunity for urban function optimization, innovation, and entrepreneurship.

An age-inclusive built environment is essential for promoting an accessible food landscape for the elderly population. However, previous research has focused on least-distance/time travel metrics in a 2D environment, potentially overlooking travelers' physical constraints and underestimating actual walking distances. In contrast, this study employs advanced geocomputational methods that leverage 3D building models, 3D pedestrian networks, and elevation data to appraise the fine-scale spatial accessibility to healthy food in Hong Kong. Guided by the principles of least effort, our findings suggest that 95% of older adults can access healthy food within 913.3m due to Hong Kong's compact and transit-oriented built environment. However, nearly half (47%) of older adults may encounter difficult pedestrian paths even with the least-effort route. Subsequently, the Aggregated Accessibility Index (AAI) is devised to identify communities that require improvement in promoting active living for older people. Site visits were also conducted to validate the AAI and present real-world situations to better articulate the mobility challenges imposed on older adults. Our study underscores the instrumental role of advanced spatial data computation in shaping age-friendly communities that prioritize and enhance spatial accessibility to healthy food, advocating for nuanced urban planning approaches that address the diverse needs of aging populations.

Green space is an important adaptation strategy against urban thermal environmental change. However, the impact of the spatiotemporal changes in green space on urban mesoscale meteorology and human thermal comfort has been understudied. Here, we incorporated gridded urban green fraction (GF) corresponding to 2016 and 2020 in central Wuhan into the Weather Research and Forecasting model to investigate the thermal environmental effects of changes in GF. Results show that a 10% increase in GF produced a nighttime cooling of 0.167 °C and a daytime cooling of 0.075 °C. Meanwhile, it increased atmospheric moisture content by 0.131 g/kg in the daytime and 0.042 g/kg at night. Consequently, the nighttime heat index (HI) was reduced by 0.258 °C but the daytime HI was raised by 0.056 °C. Addition of GF induced a greater magnitude of change in 2-m air temperature and the HI when the GF in a grid cell exceeded 0.6. The reduction in ground heat flux due to the conversion of urban fraction to green fraction, in conjunction with the weak turbulence, contributed to the greater magnitude of cooling in the nighttime than in the daytime. To enhance green space connectivity should be an effective pathway to thermal adaptation in high-density built environments like Wuhan.

The integration of multi-source and diverse spatio-temporal travel data provides a comprehensive insight into urban mobility. Using data from Shenzhen's public transportation system, this study presents an analytical framework based on multiplex networks to examine variations in multi-mode public transportation usage (metro, bus, taxi, and shared bike) and their correlation with the built environment. This framework encompasses the analysis of network topological characteristics, centrality, and communities. The examination of network topological characteristics reveals that the multiplex transportation network exhibits high global accessibility and local connectivity. Network centrality analysis, focusing on weighted outdegree centrality, captures the patterns of public transportation ridership. Centrality modeling, employing the light gradient boosting machine, demonstrates a nonlinear relationship between ridership and the built environment. Factors including population density, residential land use percentage, entertainment service density, restaurant density, and metro station density consistently exhibit positive correlations with ridership across different times of the day. The community structure analysis, using consensus community detection, indicates that distinct urban areas exhibit clustering behavior based on public transportation demand patterns, forming distinct communities that closely align with the functional zoning of urban planning. These findings could provide valuable insights for the strategic planning of transportation services and the built environment.

The ongoing erosion of beaches due to anthropic pressure, together with the effects of sea level rise are expected to have devastating effects on sandy coastlines. This paper identifies the Italian beaches under threat and their regional distribution, examining the implications for beach-dependent activities and coastal management, based on the integration of various projections and datasets. As a result, by the year 2050 erosion is expected to affect 70% of Italian beaches. One-fifth of the current beaches' surface is likely to be almost completely submerged by 2050 and 45% by the end of the century, with the regions of Sardinia, Campania, Lazio, and Apulia likely to lose more than half of their equipped beaches. Although beaches naturally tend to migrate landward when subjected to erosion, and such shift can be favoured by managed retreat measures, these solutions are severely limited by the increasing urbanisation of back beaches. Adaptation in Italy is almost exclusively based on coastal protections, which exacerbate overall erosion and undermine beaches’ natural resilience. A transformative and nature-based rethinking of the coastal management regime is needed to avoid the artificialization of a huge portion of the sandy coastline and increasing threats to coastal settlements and economies.