Assessing the access to fire service at an urban scale involves accounting for geographical impedance, demand, and supply, thus both spatial and non-spatial dimensions must be taken into account. Therefore, in this paper, an optimized two-step floating catchment area (F-2SFCA) method is proposed for measuring urban fire service access, which incorporates the effects of both spatial and non-spatial factors into fire service access. The proposed model is conducted in a case study to assess the fire service accessibility of Nanjing City, China, and then compares its differences and strengths to the existing 2SFCA (two-step floating catchment area) methods. The experimental results demonstrate that the proposed method effectively quantifies the actual fire service needs and reflects a more realistic spatial pattern of accessibility (i.e., high accessibility level corresponded to a low fire service needs). In addition, we teste the relationship between service accessibility and the facility busyness using the inverted 2SFCA method. The empirical findings indicate that the weighted average accessibility obtained by F-2SFCA is reciprocal to facility busyness across the study area (based on a 5-min catchment), and fits an obvious nonlinear correlation with the high R-square values. The above results further prove the effectiveness and accuracy of the proposed method in characterizing the accessibility of fire services.

Pick-up and drop-off events of taxi trajectory data contain rich information about residents’ travel activities and road traffic. Such data have been widely applied in urban hotspot detection in recent years. However, few studies have attempted to delimitate the urban hotspot scope using taxi trajectory data. On this basis, the current study firstly introduces a network-based spatiotemporal field (NSF) clustering approach to discover and identify hotspots. Our proposed method expands the notion from spatial to space–time dimension and from Euclidean to network space by comparing with traditional spatial clustering analyses. In addition, a concentration index of hotspot areas is presented to refine the surface of centredness to delimitate the hotspot scope further. This index supports the quantitative depiction of hotspot areas by generating two standard deviation isolines. In the case study, we analyze the spatiotemporal dynamic patterns of hotspots at different days and times of day using the NSF method. Meanwhile, we also validate the effectiveness of the proposed method in identifying hotspots to evaluate the delimitating results. Experimental results reveal that the proposed approach can not only help detect detailed microscale characteristics of urban hotspots but also identify high-concentration patterns of pick-up incidents in specific places.

Windows, as key components of building facades, have received increasing attention in facade parsing. Convolutional neural networks have shown promising results in window extraction. Most existing methods segment a facade into semantic categories and subsequently employ regularization based on the structure of manmade architectures. These methods merely concern the optimization of individual windows, without considering the spatial areas or relationships of windows. This paper presents a novel windows instance segmentation method based on Mask R-CNN architecture. The method features a spatial attention region proposal network and a relation module-enhanced head network. First, an attention module is introduced in the region proposal network to generate a spatial attention map, then the attention map is multiplied with the objectness scores of the classification branch. Second, for the head network, relation modules are added to model the spatial relationships between proposals. Appearance and geometric features are combined for instance recognition. Furthermore, we constructed a new window instance segmentation dataset with 1200 annotated images. With our dataset, the average precisions of our method on detection and segmentation increased from 53.1% and 53.7% to 56.4% and 56.7% compared with Mask R-CNN. A comparison with state-of-the-art methods also proves the predominance of our proposed method.

In the age of rapid motorization, walking, as both a green travelling mode and the most common form of daily physical activity, has been given increasing attention in Chinese metropolises. Walkability describes the extent to which a neighborhood environment is walking-friendly and recently has been regarded as a potential impetus for inflated housing prices. In this paper, we develop a walkability measurement model considering residents’ usage characteristics of the amenity, which incorporates three key factors: (1) amenity usage frequency; (2) amenity selection diversity; (3) the distance decay effect. Accordingly, we employ the proposed method to the case of Nanjing City and identify a clear spatial pattern of spatial heterogeneity in walkability among the 4143 dwelling areas within it. The experimental results suggest that the distribution of the residential walkability score varied greatly within Nanjing. It can be seen that dwelling areas with a high walkability score were clustered in the urban central regions and most dwelling areas showed a low walkability. Then, we utilized the hedonic price model to explore the correlation between neighborhood walkability and housing prices. The results show that the effects of community walkability on housing prices were statistically significant in Nanjing. Thus, we can infer that high walkability communities generally have concentrated rich amenity resources, and consequently have high property values.

The optical and physical characteristics of the aerosol vertical layers over Northeastern China (NEC) are investigated using the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Level 2 layer products from 2007 to 2014. To better examine the spatial and temporal variations in the characteristics of aerosols over NEC, the region is divided into three parts (Heilongjiang province, Jilin province, and Liaoning province) to analyze the inter-annual and seasonal variations of nine selected aerosol parameters in each part during night and day times. The results reveal that the values of aerosol optical depth (AOD) increase year by year, over the whole NEC, being relatively high over the Liaoning (LN) province; this might be induced by higher levels of economic development and agricultural activity. The highest AOD values appear in summer, which is plausibly related to the temperate monsoon climate in NEC. Higher AOD values exist during the daytime than at night; this is intuitively the result of higher daytime anthropogenic activities. The base altitude of the lowest aerosol layer (BAL) and the top altitude of the highest aerosol layer (TAH) varied significantly due to the topography of NEC. The number of aerosol layers (N) is relatively large over LN, which might be caused by a relatively stronger atmospheric convection over this landscape. The thickness of the lowest aerosol layer (TLL) bore little relationship with the topography of NEC. The AOD proportion of the lowest aerosol layer (PAODL) is high (0.70 to 0.85 for the entire NEC), indicating that aerosols are mainly concentrated in the lowest layer of the atmosphere. The volume depolarization ratio of the lowest aerosol layer (VDRL) is large during spring and winter due to the presence of dust aerosols. The color ratio of the lowest aerosol layer (CRL) is large during the day due to relatively more human activities taking place than at night. Moreover, there is a significantly positive linear correlation between N and TAH, and a negative logarithm correlation between N and PAODL over NEC. The results of this study could provide researchers and the government departments with detailed and certain optical and physical information about aerosol layers over NEC, to help in the treatment of air pollution over NEC.

Different travel cost functions for modeling travelers’ departure time and route choice behavior under uncertainty are investigated; then the principal equivalency of the mean-variance and scheduling approaches in modeling departure time choice is proven analytically. Based on the equivalency, a general cost function is formulated for modeling travelers’ departure time and route choice behavior under uncertainty and applied to an analysis of the departure patterns under uncertainty and a reliability-based dynamic network design problem. The analytical investigation of the departure pattern considering travel time reliability in the individual's travel cost function is presented as an extension of Vickrey's deterministic bottleneck model toward stochastic capacities. The departure pattern is significantly different from that without reliability: travelers depart earlier to reduce travel time variability. This finding (corroborated by simulations) motivated the network design approach including travel time reliability in departure time and route choice behavior. A discrete dynamic network design problem is formulated subject to a reliability-based dynamic user equilibrium, with the cost function derived from the equivalency analysis among different cost functions. Total network costs–the costs of total travel (including travel time reliability) and construction–are minimized. The network design problem consists of optimally allocating link capacities by determining the optimal numbers of lanes for a subset or a full set of links of a potential network. A genetic algorithm with some intelligence is adopted to solve the discrete optimization problem. Use of a small hypothetical network indicates that different network designs may lead to diverse departure distributions among different origin-destination (O-D) pairs over time, directly affecting travel costs. Relatively dissimilar departure patterns among different O-D pairs over time give lower travel costs.