Using the City of Corvallis, Oregon, a small to medium sized American city, as a test-bed, this paper examines the City’s urban growth in relation to urban accessibility. This relationship is explored in an anatomic spatial-temporal fashion, taking account of: the number and size of developed land use parcels over time; urban accessibility from residential to non-residential land use areas; and the statistical relationships between urban form and urban accessibility. This investigation of land use is structured around use-classification and examined within a range of dimensional and demographic measurements over 5-year time periods from 1853 to 2014; concurrently, urban accessibility is measured by the least-cost path distance as calculated through the OD cost matrix analysis in GIS. The results indicate that the city grew spatially at different rates and its urban accessibility experienced both ups and downs over time. The city’s population growth corresponded closely with urban growth and its decreasing population density negatively impacted on the city’s urban accessibility to commerce, industry, and office for most time periods. Significantly, while the urban density increased steadily after 1950s concurrent with an increase in urban sprawl, in contrast to previous studies on the metropolitan condition, the urban density had no evident impact on urban accessibility in Corvallis. Instead, increasing the land-use mix was a more effective and feasible approach to reduce urban travel path distance and enhance accessibility than increasing population density or urban development density. Accordingly, this research provides evidence-based policy recommendations for planning sustainable urban mobility and urban form in small to medium-sized cities.

This research highlights that street layouts, including the vegetation ratio, street orientation, and aspect ratio, are key in diminishing urban heat islands (UHIs), building energy use, and carbon emissions. The optimal street layout for minimal building energy consumption, carbon emissions, and maximal outdoor thermal comfort in Tianjin was established via field measurements, ENVI-met 5.6.1, Energy Plus simulations, and correlation analysis. The findings indicate the following: (1) The carbon emissions of winter residential heating energy consumption are 2.9–3.2 times higher than those for summer cooling. Urban design should thus prioritize winter energy efficiency and summer thermal comfort outdoors. (2) The summer street PET (physiological equivalent temperature) inversely correlates with the vegetation ratio and aspect ratio. Winter heating energy use inversely correlates with the street orientation and directly correlates with the aspect ratio. Adequate vegetation and proper orientation can decrease energy and carbon output while enhancing summer outdoor comfort. (3) Streets with an NW–SE orientation, H/W = 0.9, and 50% trees in summer yield the best thermal comfort, while those with an NE–SW orientation, H/W = 0.3, and 50% vegetative trees in winter produce the lowest carbon emissions. These insights are instrumental in refining urban streets and building designs in cold zones.

Efficiently harnessing the urban cool island effect associated with large urban aquatic bodies holds significant importance in mitigating the urban heat island (UHI) effect and enhancing the quality of residential living. This study focuses on Tianjin’s Meijiang Lake and its surrounding 47 residential areas, combining Landsat 8 remote sensing satellite data with geographic information system (GIS) buffer analyses and multiple linear regression analyses to reveal the summer thermal characteristics of residential waterfront areas with diverse spatial layouts. The results indicate that: (1) Meijiang Lake’s effective cooling radius extends up to 130 m from the water’s edge, achieving a maximum temperature reduction of 14.44%. Beyond 810 m, the cooling effect diminishes significantly. (2) Waterfront distance (WD), building density (BD), building width (L) and normalized difference vegetation index (NDVI) emerge as the primary factors influencing changes in average land surface temperature (ΔLST) in residential areas. The degrees of influence are ordered as follows: BD > WD > NDVI > L. “Dispersed” pattern residential areas exhibit the most favorable thermal environments, which are primarily influenced by WD, while “parallel” pattern residential areas demonstrate the least favorable conditions, primarily due to WD and NDVI. (3) The direct adjacency of residential areas to large-scale aquatic bodies proves to be the most effective approach for temperature reduction, resulting in a 5.03% lower average temperature compared to non-adjacent areas. Consequently, this study derives strategies for improving the thermal environment via the regulation of spatial planning elements in residential areas, including waterfront patterns, vegetation coverage, WD, and BD.

Accurately identifying the boundary of urban clusters is a crucial aspect of studying the development of urban agglomerations. This process is essential for comprehending and optimizing smart and compact urban development. Existing studies often rely on a single category of data, which can result in coarse identification boundaries, insufficient detail accuracy, and slight discrepancies between the coverage and the actual conditions. To accurately identify the extent of urban clusters, this study proposes and compares the results of three methods for identifying dense urban areas of three major agglomerations in China: Beijing–Tianjin–Hebei, the Yangtze River Delta, and the Guangdong–Hong Kong–Macao Greater Bay Area. The study then integrates the results of these methods to obtain a more effective identification approach. The social economic method involved extracting a density threshold based on the fused nuclear density of socio-economic vitality data, including population, GDP, and POI, while the remote sensing method evaluated feature indices based on remote sensing images, including the density index, continuity index, gradient index, and development index. The traffic network method utilizes land transportation networks and travelling speeds to identify the minimum cost path and delineate the boundary by 20–30 min isochronous circles. The results obtained from the three methods were combined, and hotspots were identified using GIS overlay analysis and spatial autocorrelation analysis. This method integrates the multi-layered information from the previous three methods, which more comprehensively reflects the characteristics and morphology of urban clusters. Finally, the accuracy of each identification result is verified and compared. The results reveal that the average overall accuracy (OA) of the three areas delineated by the first three methods are 57.49%, 30.88%, and 33.74%, respectively. Furthermore, the average Kappa coefficients of these areas are 0.4795, 0.2609, and 0.2770, respectively. After performing data fusion, the resulting average overall accuracy (OA) was 85.34%, and the average Kappa coefficient was 0.7394. These findings suggest that the data fusion method can effectively delineate dense urban areas with greater accuracy than the previous three methods. Additionally, this method can accurately reflect the scope of urban clusters by depicting their overall boundary contour and the distribution of internal details in a more scientific manner. The study proposes a feasible method and path for the identification of urban clusters. It can serve as a starting point for formulating spatial planning policies for urban agglomerations, aiding in precise and scientific control of boundary growth. This can promote the rational allocation of resources and optimization of spatial structure by providing a reliable reference for the optimization of urban agglomeration space and the development of regional spatial policies.

Nature-based solutions are some of the most effective strategies to promote sustainable city development; however, existing research on NbS is mostly comprised of single variable studies rather than multiple variables. The purpose of this study was to explore the possibility of extending the NbS of a single variable to two variables for the better development of sustainable cities. Both forestation and wetland restoration are regarded as NbS for sustainable city development. The research approach of “forest–wetland” NbS was proposed and centers on the process and core issues of traditional NbS. Taking Tianjin as an example, the spatial patterns of forests and wetlands, correlation between the spatial distribution of forests and wetlands, and spatial correlation between the areas of forest growth and wetland growth within a certain distance in different years were studied using a spatial distribution pattern analysis, geographic concentration analysis, kernel density estimation and spatial autocorrelation analysis. Based on the core issues of NbS and the above spatial analysis, a “forest–wetland” spatial planning strategy was formulated. The main conclusions are as follows: forest and wetland were negatively correlated in the whole area of Tianjin, forest resources w mainly located in north, while wetland resources were mainly located in south. Compared with forests, the spatial distribution of wetlands in Tianjin was more balanced. There exist synergy and trade-offs between forest and wetland area under certain circumstances. Growth of forests was positively correlated with the growth of wetlands, within a distance of 0–400 m from 2000 to 2010, and within a distance of 0–600 m from 2010 to 2020. An increase in forest area will lead to an increase in evaporation, which in turn will hinder the growth of wetlands in Tianjin. Forest–wetland ecological network could promote synergistic between forest and wetland, and grey infrastructure to reduce potential trade-off between forest and wetland.