Home Measuring spatiotemporal accessibility to healthcare with multimodal transport modes in the dynamic traffic environment
Article Open Access

Measuring spatiotemporal accessibility to healthcare with multimodal transport modes in the dynamic traffic environment

  • Xinxin Zhou , LinWang Yuan , Changbin Wu , Zhaoyuan Yu EMAIL logo and Wang Lei
Published/Copyright: March 9, 2023
Download Article (PDF)
Become an author with De Gruyter Brill
Submit Manuscript Author Information
Open Geosciences
From the journal Open Geosciences Volume 15 Issue 1

Abstract

Research on healthcare accessibility is developing with a focus on multimodal transport modes (MTMs) and multiple time-series variations. A dynamic traffic environment leads travelers to make distinct decisions at different time slots, which impacts spatiotemporal accessibility markedly. Our article proposes a methodological framework to measure spatiotemporal accessibility with multimodal transportation modes and its variation at multiple time series, while accounting for traffic congestion and the probability of residential transport mode choices in a dynamic traffic environment. We selected Nanjing, China, as the study area and pediatric clinic services (PCS) as specific healthcare services to estimate the spatiotemporal accessibility at four representative time slots. The results indicate that the weight estimation of travel time based on MTMs is more propitious than the travel time of single-mode to present real-world travel characteristics and reflects the spatiotemporal distribution and variation of services synthetically. Furthermore, the spatiotemporal accessibility variation of PCS in peripheral suburbs is more pronounced than that in urban centers and rural areas. This work holds pragmatic implications for policymakers in terms of services planning and allocation optimization to improve the equity of resource supply.

Keywords: spatiotemporal accessibility; online route planners; healthcare services; multimodal transport modes; residential transport mode choices; dynamic traffic environment

1 Introduction

As potential opportunities for the spatial interaction of geographical entities, the accessibility of services, including healthcare services [1], social amenities [2], emergency services [3], and so on, is widely used in Geography, Urban Studies, and Transportation Research [4,5]. Spatiotemporal accessibility, as one of the foremost directions of accessibility research, considers spatial location and time indicators to evaluate the effectiveness of services. In spatial interaction measurement, the travel impedance factors, commonly including the travel time, distance, and expense of transport modes, are among the fundamental indicators of accessibility, accompanied by the quality/quantity of opportunities [6].

Traditionally, the travel impedance is based on the measurement of single-mode travel time or distance and is commonly calculated by the network analysis in the Geographic Information Systems platform [7,8,9]. There is an underlying assumption of those acquisition methods that residents travel to services through uniform transport mode, which is unrealistic and inevitably introduces errors in accessibility estimations [10]. Meanwhile, the theoretically limited speed [11] neglects travelers’ multimodal preferences between different supply (destination) locations and the demand (origin) locations, preventing accessibility estimations for service planning from offering practical value.

As transportation systems become more diversified, accessibility research based on residents’ mobility becomes more complicated [1,12,13]. To date, some accessibility studies have paid attention to integrating multimodal transport modes (MTMs) into accessibility measures, and this has proven that MTMs are more beneficial for realistic accessibility estimations than single models [14]. However, empirical studies have not been conducted that simultaneously consider the influence of dynamic traffic conditions on travelers’ multimodal choices at different time slots. Dynamic traffic conditions, including traffic congestion [15], public transportation (PT) timetables, intersection delays, searching for parking spaces, time-limited exclusive bus lanes, multijunction crossroads, pedestrian systems separated from vehicle systems, and the required walk from/to one’s car, are also non-negligible factors for implementing sophisticated spatiotemporal accessibility, which is urgently needed for quantitative identification and analysis [15,16,17,18,19]. Meanwhile, when residents travel, they will choose different modes of transportation according to the time of travel, the distance to the destination, the economic cost, and the traffic congestion. Residents’ transportation mode generally has the characteristics of probability selection, non-uniformity, and fluctuation at different moments. However, the influence of travelers’ multimodal choice preferences based on the dynamic traffic environment was ignored in predecessors’ research. It has been difficult for accessibility research to measure the travel potential opportunity of services with residential MTMs at different time slots in the dynamic traffic environment. This research gap poses a considerable challenge to obtaining a comprehensive understanding of spatiotemporal accessibility.

In light of the above limitation, this article will fill the gap in investigating the effects of travelers’ multimodal choices and improve travel impedance’s rationalization. It is one of the primary methodology contributions in our article that evaluates the probability of choosing transportation mode at different time slots quantitatively, namely, the weight estimation of travel time with MTM (WETT-MTM) model, according to the travel time, the distance, the economic cost, and the traffic congestion coefficient. Furthermore, it reveals the dynamic variation of spatiotemporal accessibility of specific healthcare services when selecting pediatric clinic services (PCS) in Nanjing, China. This article is organized as follows: we provide a study area and data overview in Section 2 and the methodology in Section 3; the results are as shown in Section 4; and we end with a brief conclusion and discussion in Section 5.

1.1 Related literature review

1.1.1 Spatiotemporal accessibility of healthcare services

The research progress in the spatiotemporal accessibility of healthcare can be summarized from two perspectives: the business field and the methodology field.

In the accessibility business field, the accessibility of healthcare is a multifaceted field that involves primary healthcare [20], access to healthcare in rural areas [21,22], the cross-border spatial accessibility of healthcare [23], the spatial equity of multilevel healthcare [24], hospital care and emergency medical services [1,3], and mental health in childhood and adolescence [25]. Because primary healthcare is closely related to life, relatively inexpensive, and efficiently delivered, primary care has received extensive attention [26]. The other burgeoning thread of research has shifted the focus to accessibility measures for different age groups, highlighting age-level differences, like those between children and older people [8]. PCS resources face high scarcity due to pediatrics’ particular features, especially in developing countries [27,28]. In 2016, the State Health and Family Planning Commission of China issued guidelines for strengthening the reform and development of children’s medical and healthcare services[1]. PCS spatial accessibility is one of the typical and meaningful issues within research on the accessibility of healthcare services [29,30,31], which prompts us to choose PCS as the research object.

In the accessibility methodology field, we can categorize the accessibility models into two categories: place-based (i.e., residential community) and individual-based (i.e., residential individual) accessibility models classified by the number of residents studied [32]. The place-based accessibility measures mainly include cumulative opportunity models and gravity models [33] and the two-step floating catchment area method (2SFCA) [34] and evaluate the opportunities from demand locations to surrounding supply considering the travel impedance [35]. Luo and Wang [34] first proposed 2SFCA, one of the most popular accessibility estimation methods, to measure healthcare’s spatial accessibility. A new wave of development based on the 2SFCA model was widely developed until Wang unified the various accessibility models into the generalized 2SFCA and five types of the expanded form [36]. Individual-based accessibility measures, relying on the construct of the space–time prism in time geography [5,37], have been proposed to represent an individual’s ability to reach opportunities given their motility constraints [35]. More comprehensive accessibility measures, including integrating time and transport modes from open data[38], multitemporal transport network models [39], and a multimodal relative spatial access assessment approach [12], are becoming a critical development branch of dynamic environment changes in spatiotemporal accessibility.

1.1.2 Residential transport mode choices with MTMs

Traditionally, accessibility studies look towards subjectively choosing specific transportation modes [36], but an increasing number of scholars have begun to improve accessibility models by considering the influence of different transport modes [40]. Travel impedance data can be accessed from open data sources [41,42], including individual trip survey data [10], web mapping services (Google Maps [40], Baidu Maps [43], and Amap Maps [44]), and location-based social media data [14], which enable advancements in revealing the characteristics of human activities [45,46,47]. Open data sources provide fine-scale and dynamic spatiotemporal big data for accessibility research [48]. Web mapping services provide a more accurate approach for obtaining travel impedance data between origin and destination [17]. In addition, web mapping platforms have integrated multiple traffic conditions, including fundamental traffic flow principles and historical traffic data, through large-scale users’ behavior data based on mobile Apps [49] and record realistic multimodal transport information [50]. New accessibility models have been developed, such as multimodal 2SFCA [10], a variable-width floating catchment area model [40], multimodal 2SFCA incorporating the spatial access ratio [12], and multimodal accessibility-based equity assessment [11]. However, the influence of travelers’ multimodal choice preferences based on different traffic conditions is ignored in multimodal accessibility research. For example, people usually prefer to bicycle or walk to a hospital if it is less than 5 km away in the case of heavy traffic congestion during commuting time. Inversely, people are more likely to take PT or drive to the hospital, which is far away. Land use mix, population density, and employment density have been proven to influence the multimodal choices for life trips [51].

A multitude of factors, including travel experience [52], travel purpose [53], real-time traffic information [53,54], departure time urgency [55], travel time, and travel distance [56], would all impact residents’ multimodal choice preferences for different transport modes. It appears that there have been fewer experiments on accessibility that consider the influence of residential transport mode choices. One of the key tasks of our article will focus on calculating the probability distribution and combination value of residential transport mode choices and establishing a probability to obtain WETT.

2 Study area and data

2.1 Study area

With an area of 6,587 km2 and a population of 8,335,000, Nanjing is one of the most significant cities in China. Nanjing, the capital of Jiangsu Province, and the national gateway city for the Yangtze River Delta’s central and western regions (Figure 1), is a world-famous historical and cultural city. In terms of population, as of 2016, the urbanization rate of Nanjing was 82.29%. In terms of transportation, various transport modes coexist, and they are high in number: there are 8,395 buses and trolleybuses and 14,239 taxis. The total length of the Nanjing metro was 381 km at the end of 2015. In addition, there were 2,540,000 personal vehicles in Nanjing and as many as 650,000 and 3,000,000 shared bicycles and e-bikes, respectively, at the end of 2018.[2] The Nanjing traffic road network’s improvement level is at the forefront for China, with a per capita road area of 21.81 m2, far exceeding the national average of 15.6 m2 [57]. In terms of medical services, Nanjing’s medical and health system is flourishing, comprehensive medical resources are relatively abundant, and the overall health services rank behind only Shanghai and Beijing in China. There are 241 public hospitals in Nanjing, of which 22 are top-tier hospitals (3A-hospitals). However, pediatric medical resources in Nanjing are still scarce (Section 2.2.3). Therefore, Nanjing is selected as a metropolitan research area considering its regional representativeness and prominent contradiction. We used the whole administrative scope of Nanjing to generate basic research grids (the total number being 6,936 and cell size being 1 km × 1 km) to facilitate research.

Figure 1 The study area.
Figure 1

The study area.

2.2 Data

Three types data were collected through an open-source approach, including online route planners’ data, spatial distribution data of the population, and PCS data. According to the 2021Q1 Traffic Analysis report of Major Cities in China[3], we selected four typical time slots in the daytime, including 8:00 (morning rush peak), 13:00 (off-peak in noontime), 18:00 (evening rush peak), and 22:00 (off-peak in nighttime) in Nanjing, which is in UTC+8. These four time slots can effectively manifest the dynamic performance of the traffic environment.

2.2.1 Route planning data for MTMs

The route planning API is feasible for travel impedance calculation [17]. The travel impedance requested from the web mapping platform is a historical average; thus, it offers valuable and credible predictions for research purposes and is more accurate in considering the traffic conditions and congestion time loss for actual location data [41]. We selected Amap Maps (www.amap.com) as a data source, as it is one of the most popular web mapping platforms in mainland China. The API returned results for the prescribed transportation modes are the most recommended route path considering time and distance.

To visualize the travel time variation of multimodal at different time slots, we select Nanjing Children Hospital, Guangzhou Branch, as a destination example to show the variation in travel times from full grids at different time slots in 1 day (Figure 2). The travel time variability of different travel modes has apparent differences. The most variable is driving, followed by PT, while walking and bicycling are not affected by different time slots. The travel time of PT has little effect on the morning and evening peak hours. The travel time at 22:00 is more prolonged. The travel time at night is significantly lower than the travel time during the daytime. The above exploratory analysis of online route planning data to Nanjing Children Hospital, Guangzhou Branch, indicates the diversity of the traffic environment.

Figure 2 The travel duration from different origins to Nanjing Children Hospital, Guangzhou Branch, based on four transport modes at four time slots. (a1) PT 08:00, (a2) walking 08:00, (a3) bicycling 08:00, (a4) driving 08:00, (b1) PT 13:00, (b2) walking 13:00, (b3) bicycling 13:00, (b4) driving 13:00, (c1) PT 18:00, (c2) walking 18:00, (c3) bicycling 18:00, (c4) driving 18:00, (d1) PT 22:00, (d2) walking 22:00, (d3) bicycling 22:00, and (d4) driving 22:00.
Figure 2

The travel duration from different origins to Nanjing Children Hospital, Guangzhou Branch, based on four transport modes at four time slots. (a1) PT 08:00, (a2) walking 08:00, (a3) bicycling 08:00, (a4) driving 08:00, (b1) PT 13:00, (b2) walking 13:00, (b3) bicycling 13:00, (b4) driving 13:00, (c1) PT 18:00, (c2) walking 18:00, (c3) bicycling 18:00, (c4) driving 18:00, (d1) PT 22:00, (d2) walking 22:00, (d3) bicycling 22:00, and (d4) driving 22:00.

2.2.2 Spatial distribution data of the population

The spatial distribution data of the population is one of the essential indicators for realizing spatiotemporal accessibility [44]. Similarly, the spatial distribution data of children is the key to evaluate the spatiotemporal accessibility of PCS. In past studies, each administrative unit’s demographic data was commonly used to represent the population directly. The shortcomings of this approach are high data granularity, discrete spatial distribution, and low accuracy. We obtained population spatial distribution data from Tencent Suitable for Travel Platform (TSTP), based on Tencent apps’ user density data. The Tencent apps’ user density data are one of the most popular population data sources in social media data [58,59,60]. They are provided by Tencent (http://www.qq.com), one of the largest internet companies both in China and globally [61]. Seven million seven hundred sixty-nine thousand population data points were requested from TSTP, slightly lower than the 8,335,000 total population found in the statistical yearbook [62]. Because some older adults and young children do not use smartphones and Tencent apps, this error can be explained and accepted. Therefore, requesting and counting TSTP data can reasonably indicate the regional population distribution. We chose the Sixth National Population Census tabulation in China [63] to obtain the proportion in each district and convert proportionally to obtain the percentage of children in each district in 2017 (Figure 3). The spatial population of children is concentrated in the main urban area, with high values distributed together and uneven spatial distributions. The hotspots are centralized in the regions along with the arterial networks of the main urban area.

Figure 3 The spatial distribution map of children in Nanjing.
Figure 3

The spatial distribution map of children in Nanjing.

2.2.3 Healthcare services data

Twenty-six hospitals in Nanjing have established pediatric services, but these services have significant differences in their treatment ability. To effectively measure the pediatric scale of various hospitals considering the availability of data, we estimate the hospitals’ pediatrics level by the number of pediatricians. The number of pediatricians is taken from the Good Doctor website (https://haoping.haodf.com/keshi/3030000/faculty_jiangsu_nanjing.htm). The statistical results are expressed in the form of spatialized drawings (Figure 1(d)). The results show that the total number of pediatric doctors in Nanjing was 603. Compared with 904,000 children [62], the average number of pediatricians per 1,000 people was approximately 0.67. According to the 2015 China Health Statistics Yearbook, the average is 0.43 pediatricians per 1,000 children in China in the past 5 years. Although the average number of pediatricians per 1,000 people in Nanjing is higher than the average for China, it is still far below that in the principal developed countries, which have a ratio reaching 0.85–1.3 pediatricians per 1,000 children.

For the number of pediatricians in each hospital, the number of pediatricians in Nanjing Children Hospital, which consists of Nanjing Children Hospital, Guangzhou Branch (Figure 1(d) “GZ” annotation), and Nanjing Children Hospital, Hexi Branch (Figure 1(d) “HX” annotation), is the largest, including 392 pediatricians and accounting for 65% of the total number of pediatricians in Nanjing. The maximum number of outpatient visits in Nanjing Children Hospital exceeds 11,000, and even the number of outpatient clinics in the evening is over 1,000. From the spatial distribution, the hospitals containing PCS are mainly concentrated in the Gulou District, which is the core urban area of Nanjing and has many essential departments, educational resources, and commercial centers.

3 Methods

The proposed methodological framework for spatiotemporal accessibility is shown in Figure 4.

Figure 4 The overall technology roadmap.
Figure 4

The overall technology roadmap.

Step 1: Basic data preprocessing (Figure 4I): Through the spider programs for Amap Maps, TSTP, and the Good Doctor website, we obtain the primary data and use basic data processing methods to obtain spatial grids, population data, and hospital data.

Step 2: Online route planning data requesting (Figure 4II): The input data are each origin–destination flow with four transport modes (PT, driving [including taking a taxi], bicycling, and walking). The online route planning data are obtained from the Amap route planning API program at four time slots (Section 2.2.1).

Step 3: The WETT-MTM model designing (Figure 4III): The WETT-MTM model calculates the WETT based on MTMs and the spatiotemporal variation in the WETT will is analyzed with an empirical destination.

Step 4: Spatial distribution data of population measurement (Figure 4IV): The spatial population distribution data of children are generated from the users’ density data from TSTP (Section 2.2.2).

Step 5: Accessibility model calculation (Figure 4V): We adopt the gravity model to calculate the accessibility values at four time slots.

Step 6: Results in analysis (Figure 4VI): The spatiotemporal accessibility characteristics were analyzed and compared to explore the spatial distribution and temporal variation of accessibility.

3.1 WETT-MTM model

As we all know, an individual tends to use specific transportation modes when traveling. However, considering individual and traffic environment differences, resident groups present different travel mode choices at the macro level. When individual differences are superimposed as group characteristics, they are characterized as superposition benefits of polymorphism, which sociological research has widely recognized. So the WETT-MTM model is an innovative estimation method of the probability weight method that considers affecting factors and calculates travel time’s weight estimation in the dynamic traffic environment (Figure 5). The theoretical premise of the WETT-MTM model is that MTMs widely exist in each origin–destination trip, while the difference lies in the probability of selection. The probability of choosing between different transport modes is mainly affected by travel time, distance, and dynamic traffic factors. It is a complex problem to determine the weight of each factor in the multi-factor superimposed benefit, according to the factor differences of each origin–destination trip, such as with the highest utility due to the difference of high urgency, short travel time, road traffic density, and economic cost during travel. The detailed design of the WETT-MTM model explicitly includes four steps. The input data is online route planning data, and the output result is the WETT.

Figure 5 The WETT-MTM model design roadmap.
Figure 5

The WETT-MTM model design roadmap.

Step 1: Transport utility function definition (equation (1))

The traveler selection behavior is in the constraint space–time of finite conditions, so the utility function can be employed to establish the travel mode selection behavior model based on the space–time constraint. The basic premise that the travel utility function satisfies is that residents will choose the transportation mode with the highest utility due to the difference of high urgency, short travel time, road traffic density, and economic cost during travel. The travel utility function Z r t ( ij ) aims to quantitatively depict the travel utility between time t , transportation mode r , demand location i , and supply location j . The higher the utility value indicates, the greater the value of this mode of transportation between demand location i and supply location j , and the greater the selection probability will be calculated by the input into the Softmax function.

(1) Z r t ( ij ) = ε h r t ( ij ) * γ r t ( ij ) * β r ,

where, ε represents the degree of urgency. We are aware that the degree of urgency varies between different parts or activities of the city. Daytime trips for medical services are normalized outpatient clinics, and nighttime trips for medical treatment are mostly emergencies. Therefore, the degree of urgency for day trips is relatively lower than that of late-night trips. After stability testing, we decided to use two empirical values, ε = { 10,12 } , for daytime and nighttime, respectively. γ r t ( ij ) represents the traffic congestion coefficient of transport mode r at time slot t from demand location i to supply location j ; h r t ( ij ) represents the travel duration of transport mode r at time slot t from demand location i to supply location j ; β r represents the economic cost factor of transport mode r .

Step 1.1: Traffic congestion coefficient ( γ r t ( ij ) ) measurement (equations (2) and (3))

Researchers apply a value to measure the degree of spatiotemporal change to directly reflect the comprehensive spatiotemporal variation in geographical features [64]. The dynamic index is widely used in land use type change, as it can depict the general characteristics of the spatiotemporal variation in land use type over multiple years [65]. Based on this inspiration, we design γ r t ( ij ) (equation (2)), the traffic congestion coefficient of demand location i to supply point j , and γ r t ( i ) (equation (3)), traffic congestion coefficient of demand location i , to estimate the traffic congestion as follows:

(2) γ r t ( ij ) = 1 T · ∑ t = 1 T h r t ( ij ) − h r t 0 ( ij ) h r t 0 ( ij ) , h r t ( ij ) > h r t 0 ( ij ) 0 , & h r t ( ij ) ≤ h r t 0 ( ij ) ,

(3) γ r t ( i ) = ∑ i = 1 M γ r t ( ij ) ,

where h r t 0 ( ij ) denotes the benchmark of travel duration of transport mode r at time slot t 0 from demand location i to supply location j . Considering nighttime peak being the lowest traffic congestion, our article selects 22:00 as t 0 . T denotes the sum of time slots, being 4 in this article. M denotes the total number of supply points.

Step 1.2: Economic cost factor conversion

The β r , whose unit is expenditure (dollars per km), indicates the economic cost factor for integrating different transport modes. The economic cost factor refers to the trade-offs between the uses of resources [66,67]. The design of β r has had a major adjustment impact on the calculation of WETT-MTM. We use fixed vehicle costs and variable vehicle costs as the users’ money costs. Therefore, the indicators are: average car $0.15, diesel bus: $0.08, bike: $0.03, and walk: $0.01 [66,67]. We establish β r to be {public transportation = 0.08, walking = 0.01, driving = 0.15, bicycling = 0.03}. β r is a negative indicator, and the larger β r is, the lower the residential transport mode choices probability value.

Step 2: Transport mode selection probability P ir t (equation (4))

The Softmax function, also known as the normalized exponential function, generalizes logic functions in probability theory and machine learning fields. It can contain one with any K dimension vector Z , “compress” to another R dimension vector σ ( Z ) (Figure 6). Therefore, every element has a range in ( 0 , 1 ) , and the sum of all elements is equal to 1. The Softmax function is a gradient log normalization of a finite item discrete probability distribution. Therefore, the Softmax function includes multiple logistic regressions, multiple linear discriminant analyses, and a naïve Bayes classifier with artificial neural networks. It has a wide range of applications in various probability-based multiclassification problem methods.

(4) P r t ( ij ) = P ( Z r t ( ij ) ) = e Z r t ( ij ) ∑ r = 1 R e Z r t ( ij ) ,

Figure 6 Softmax schematic diagram.
Figure 6

Softmax schematic diagram.

Among them, r = 1 , … , R .

Step 3: Calculate WETT t ( ij ) (equation (5))

We apply the weight P r t ( ij ) multiplied by the travel time h r t ( ij ) , then weightily sum by different transport modes to obtain WETT t ( ij ) .

(5) WETT t ( ij ) = ∑ r = 1 R h r t ( ij ) * P r t ( ij ) ,

3.2 Accessibility model

Our spatiotemporal accessibility research focuses on place-based spatiotemporal accessibility measurements based on residents’ mobility and time slot variations. Among place-based accessibility measurement models, the gravity model is the most widely used [11]. The gravity model, also known as the potential and potential energy models, is derived from Newton’s law of universal gravitation and was proposed by Hansen in 1959 [68]. Hansen introduced the concept of spatial accessibility when analyzing the population distribution of the urban population and the spatial accessibility indicators of residential land. He offered a calculation model of spatial accessibility that used the potential indicators to evaluate spatial accessibility [69]. As a particular form of generalized 2SFCA models, the gravity model belongs to the same theoretical framework as 2SFCA [34]. Both aim to evaluate the physical accessibility to services based on the spatial interaction between supply and demand locations. However, the gravity model adopts the continuous distance attenuation function, while the 2SFCA model adopts the dichotomy method to address the distance attenuation.

The model expression is as follows:

(6) A i = ∑ j = 1 M E j f ( d ij ) V j ,

where A i denotes the spatial accessibility of demand location i ; E j denotes the number of pediatricians indicates the service resource supply capacity of supply point j ; M denotes the total number of provider locations.

The spatial accessibility of urban services is determined by both supply and demand sides, while equation (6) only considers the supply points and does not consider the demand locations, which gives rise to the same traffic impedance d ij . Assuming that the attraction of the two supplies E j is equal, the difference in the population of the two supplies does not affect the size of the spatial accessibility at this time, which is not consistent with the facts. To solve this problem, V j , the impact factor of population size, was introduced and can be extended as follows:

(7) V j = ∑ k = 1 N Q k f ( d ij ) ,

In summary, the potential model is written as follows:

(8) A i = ∑ j = 1 M E j f ( d ij ) V j = ∑ j = 1 M E j f ( d ij ) ∑ k = 1 N Q k f ( d ij ) ,

where N is the total number of demand locations; Q k is population number at demand location k (unit: 1,000 people, consistent with the dimensions in the previous data description); d ij is the traffic impedance between demand location i and supply point j ; d kj is the traffic impedance between demand location k and supply location j .

(9) f ( d ij ) = d ij σ , d ij ≤ d 0 ∞ , d ij > 0 ,

where d 0 is the catchment size (i.e., threshold travel time); considering the WETT implying dynamic traffic conditions and reducing filter degree from catchment size, we define d 0 like 2 h, the median value as shown in Figure 9; σ is the coefficient of travel friction, selected as 1 [70].

4 Results

To clearly state the dynamic consequence of the WETT-MTM model, we will divide the results into two parts: Part I – base exploration results and Part II – spatiotemporal accessibility results (Figure 7). Wherein, base exploration includes traffic congestion analysis (Section 4.1) and an empirical example of WETT-MTM (Section 4.2). Spatiotemporal accessibility results organize into reference scenario comparison (Section 4.3) and variation scenario comparison (Section 4.4). The reference scenario compares the impact on accessibility between PT and MTM. Nevertheless, the variation scenario comparison compares the dynamic variation in temporal and spatial accessibility brought about by WETT-MTM at different time slots to reveal the changes brought about by the dynamic urban traffic environment.

Figure 7 The organization of results.
Figure 7

The organization of results.

4.1 Analysis of traffic congestion

First, we performed the calculation γ r t ( ij ) on each origin–destination trip by equation (2) at different time slots to estimate the essential characteristics of four transport modes (Figure 8). One interesting finding was that the traffic congestion of PT and driving is higher than bicycling and walking obviously, which is consistent with our common sense. So we only consider traffic congestion for driving and PT. But another unfamiliar finding was that while the traffic congestion of driving crowds together, the traffic congestion of PT appears to be partial extremum, which surprises us. To further explain this phenomenon, we adapt equation (3) to calculate the traffic congestion of driving and PT on the grid separately (Figure 9). The distribution area and driving range surpassed PT in traffic congestion. They showed a typical center-periphery pattern since it is possible to access more opportunities from central locations than from peripheral areas.

Figure 8 Traffic congestion coefficient of four transport modes.
Figure 8

Traffic congestion coefficient of four transport modes.

Figure 9 Traffic congestion spatial distribution maps of driving mode and PT mode. (a) Traffic congestion coefficient of Driving mode. (b) Traffic congestion coefficient of PT mode.
Figure 9

Traffic congestion spatial distribution maps of driving mode and PT mode. (a) Traffic congestion coefficient of Driving mode. (b) Traffic congestion coefficient of PT mode.

Conversely, PT congestion presented discrete islands of distribution, and some locations were unusually high, mainly in the peripheral suburbs. The driven reasons are that PT is diversified, including the subway, bus with bus-only lanes, light rail, among them, the dense urban subway, light rail distribution, both of which are not affected by road traffic congestion. However, PT in the peripheral areas mainly relies on the bus. Road congestion, combined with the route, is very long in the morning and evening, so the accumulative effect of traffic congestion causes the formation of extremum congestion areas.

4.2 An empirical example of WETT-MTM

We selected all grids as the origins and chose Nanjing Children Hospital, Guangzhou Branch, as a representative pediatrics hospital to visualize the spatiotemporal patterns and changes of WETT directly from all demand locations (Figure 10). The natural interval rules of the legend in Figure 10 are consistent with Figure 2 to facilitate the comparison of the difference between WETT and the travel time of each traffic mode at different time slots. In terms of timing sequence, the WETT at 22:00 is the smallest. The value of the WETT at other different time slots has significant volatility, and the weight of travel time will be higher in the morning and evening peak periods. When calculating the value of WETT for the whole research area, the degree of participation in walking and bicycling is relatively low.

Figure 10 The WETT from different origins to Nanjing Children Hospital, Guangzhou Branch, at four time slots. (a) WETT 08:00, (b) WETT 13:00, (c) WETT 18:00, and (d) WETT 22:00.
Figure 10

The WETT from different origins to Nanjing Children Hospital, Guangzhou Branch, at four time slots. (a) WETT 08:00, (b) WETT 13:00, (c) WETT 18:00, and (d) WETT 22:00.

In comparison, walking and bicycling mainly calculate the main urban area. The potential reason could be that urban travelers have more alternative transport modes based on the well-developed traffic network [71], longer public transport service schedule time [18], and relative proximity to Nanjing Children Hospital, Guangzhou Branch. However, due to being far from Nanjing Children Hospital, Guangzhou Branch, and the imperfect public transport system, the WETT in suburbs and rural areas is volatile and variable.

4.3 Reference scenario comparison

At 22:00 considered, we make a cartographic comparison and accumulation graph between accessibility based on WETT-MTM (Figure 11(a) and 13(c)) and accessibility based on PT (Figure 11(b) and 13(e)) in the reference scenario.

  1. The spatial distribution of the spatiotemporal accessibility of PCS values indicates that the main urban districts present a high value, form a continuous piece and spread out to have lower spatiotemporal accessibility of PCS. The peripheral districts are relatively low. The accessibility of PCS presents double kernels (the dual cores are Nanjing Children Hospital, Hexi Branch, and Nanjing Children Hospital, Guangzhou Branch, respectively) and gradually diffuses downward.

  2. The spatial obstructing effect of the Yangtze River is noticeable. The spatiotemporal accessibility of PCS in the northern part of the Yangtze River is lower. It is affected by the water space barrier and limited pediatric medical resources. The spatiotemporal accessibility values in the area north of the Yangtze River are distributed along with the arterial networks, indicating that traffic conditions are essential to spatiotemporal accessibility.

  3. We find that Figure 11(a) (the maximum and minimum values being 4.427 and 0.0049, respectively) is higher than Figure 11(b) (the maximum and minimum value being 1.676 and 0, respectively) in terms of global. A more interesting phenomenon is that the zero value based on PT at 22:00 is as high as 1,137 grids.

  4. From the maps’ distribution, it can be found that the accessibility based on WETT-MTM at 22:00 for medium-value and high-value radiation areas greater than 0.4 increased. In addition, the distribution effect of Figure 11(a) along the main traffic arteries is relatively weakened than Figure 11(b).

Figure 11 Reference scenario maps at 22:00. (a) 22:00 based on WETT and (b) 22:00 based on PT.
Figure 11

Reference scenario maps at 22:00. (a) 22:00 based on WETT and (b) 22:00 based on PT.

These distribution results show that the WETT-MTM model is more comprehensive than a single transportation mode. In urgent travel needs, travelers tend to choose more efficient and convenient transportation, which is limited in the available PT. The WETT-MTM model can schedule and select the optimal travel modes of different demand locations, which is also in line with the actual travel situation. Therefore, the WETT-MTM model is more suitable for estimating accessibility than considering a single transportation mode.

4.4 Variation scenario comparison

The explanation for temporal changes in spatiotemporal accessibility resides in the dynamic traffic environment. The maps of variation scenario (Figure 12) show that temporal variation exerted a very disparate effect on different demand locations, such as Y1, Y2, and Y3 areas in Figure 12(a). In the Y1, Y2, and Y3 areas, the accessibility value is the lowest in the morning peak period. With the arrival of the off-peak and evening peak, the accessibility value presents an earlier increase and later decrease trend. The evening peak accessibility is still better than that of the morning peak and primarily consists of return journeys home or trips for shopping and leisure. We can also find that the areas with dense variation mainly focus on the peripheral suburbs, but the main urban and rural areas do not have significant variation. The Y1, Y2, and Y3 areas are adequate proof. The low-value spatiotemporal accessibility and high-value variation are concentrated in the peripheral suburbs. The potential reasons for this are that pediatric clinical services are scarce, and PT is inadequate. Most of these travelers’ daily emergency medical travel by bus or driving. Therefore, the traffic congestion accumulation effect is more prominent and makes travelers more sensitive to spatiotemporal accessibility. Although the main urban areas are the prominent locations of traffic congestion, there are diversified alternative modes of transportation. For example, bicycling is a good and stable way to travel with less disturbance. So the main urban areas present a stable and high-value distribution of accessibility. Due to the importance of the WETT considering residential transport mode choices, this finding is just the opposite of traffic congestion in Figure 9.

Figure 12 Variation scenario maps at three time slots. (a) spatiotemporal accessibility at 8:00, (b) spatiotemporal accessibility at 18:00, and (c) spatiotemporal accessibility at 13:00.
Figure 12

Variation scenario maps at three time slots. (a) spatiotemporal accessibility at 8:00, (b) spatiotemporal accessibility at 18:00, and (c) spatiotemporal accessibility at 13:00.

To present the spatiotemporal accessibility of PCS cumulative distribution of each time point, we have drawn a hist graph (800 bins) and divided it into four intervals containing the lowest value interval (0–0.2), the median interval (0.2–0.4), the second-highest value interval (0.4–0.6), and the highest value interval (>0.6) (Figure 13). At different time slots, the variation is also concentrated in the lowest and median areas. The trend of the highest and second-highest value intervals tends to be stable.

Figure 13 The spatiotemporal accessibility histogram graph results from different origins to all pediatric hospitals at four time slots. (a) Spatiotemporal accessibility value at 08:00 with MTM, (b) Spatiotemporal accessibility value at 13:00 with MTM, (c) Spatiotemporal accessibility value at 18:00 with MTM, (d) Spatiotemporal accessibility value at 22:00 with MTM, and (e) Spatiotemporal accessibility value at 22:00 with public transportation mode.
Figure 13

The spatiotemporal accessibility histogram graph results from different origins to all pediatric hospitals at four time slots. (a) Spatiotemporal accessibility value at 08:00 with MTM, (b) Spatiotemporal accessibility value at 13:00 with MTM, (c) Spatiotemporal accessibility value at 18:00 with MTM, (d) Spatiotemporal accessibility value at 22:00 with MTM, and (e) Spatiotemporal accessibility value at 22:00 with public transportation mode.

5 Discussion and conclusion

In this article, we have designed a methodological framework of spatiotemporal accessibility based on online route planning data, which can effectively estimate the spatiotemporal variation characteristics of PCS at different time slots. The dynamic traffic environment would have an overall disturbance effect on the accessibility of services and spatiotemporal heterogeneity. We found three exciting patterns. (1) The traffic congestion of driving crowds together and showed a typical center-periphery pattern, nevertheless the traffic congestion of PT appeared partial extremum and presented discrete islands distribution. (2) The WETT-MTM model is more comprehensive than a single transportation mode in the reference scenario. (3) The low-value spatiotemporal accessibility and high-value variation are concentrated in the peripheral suburbs. The methodological framework of spatiotemporal accessibility of PCS can offer policymakers and planners implications regarding the dynamic accessibility of healthcare services. Typical application directions include optimizing the spatial relocation of hospitals to reduce urban traffic congestion [72], optimizing healthcare services location–allocation problems [73,74], reducing the spatial inequity of multilevel healthcare services [75], and improving the spatial equity of multilevel healthcare in the metropolis [24], which will facilitate movement to optimize the allocation and equity of healthcare services.

The WETT-MTM model proposed in our article has the characteristics of probability selection, non-uniform, and fluctuation at different moments. This model, which sufficiently considers residential transportation mode choices based on dynamic traffic conditions, is a vital component of spatiotemporal accessibility research. This work has filled the gap in modeling travelers’ choices on MTMs integrated with dynamic traffic conditions and improved the incorporation of travel impedance in the spatiotemporal accessibility model. The WETT-MTM model overcomes the singleness or absoluteness of transport mode selection in traditional accessibility research. The disturbance of the WETT value at different time slots has shown a dynamic change, and the peripheral disturbance is evident further away. The WETT-MTM model transforms the equally weighted transport mode into a probabilistic combination of MTMs, analogous to the development of classical physical space into quantum physical space [76]. This methodological framework, wholly based on the open-source data of the internet, can be applied to other cities and regions to evaluate the spatiotemporal accessibility of services, such as medical facilities, parks, and commercial and science facilities. The framework holds pragmatic implications for policymakers on the spatiotemporal accessibility regarding the plan and allocation optimization of special healthcare services to improve the equity of medical resources supply.

Several aspects of our further qualitative research work can still be improved despite the above implications. First, the analysis scale, the catchment size, and the distance decay function significantly influence the accessibility model [32]. A fine-scale or alterable scale cell size of the grid (e.g., community level and statewide) can be found with increasing research to avoid the impact of the modifiable area unit problem. Second, the proposed research framework suitable for the current situation evaluation will be further applied to location optimization of the new PCS plan with a heuristic algorithm and multi-objective location allocation. Third, all open-source research data caters to the real-time trend of Urban Big Data research based on social media data. We can quantitatively find more different spatiotemporal accessibility for PCS. We will also attempt to calibrate or validate the WETT-MTM model and explore commuter-based spatiotemporal accessibility based on dynamic commuting data and dynamic flow data from individuals simultaneously. In brief, travelers’ choice of MTMs with integrated dynamic traffic conditions should be worth considering to reveal the spatiotemporal accessibility of services.

Acknowledgments

The authors would like to thank the anonymous referees and editor for their valuable comments, which significantly improved this article.

  1. Funding information: This research was supported by the Fundamental science (Natural Science) research Project of higher education institutions in Jiangsu Province, grant number 22KJB420004 and the Open Foundation of Key Lab of Virtual Geographic Environment of Ministry of Education, grant number 2021VGE02.

  2. Conflict of interest: The authors declare that they have no competing interest.

  3. Ethics approval and consent to participate: All the data that are collected from the internet are open source, ethically free, and privacy-free. Also, the openness of data acquisition brings advantages for our research method to be extended to other cities.

  4. Consent for publication: This manuscript does not contain any individual person’s data in any form (including any individual details, images, or videos), consent for publication.

  5. Data availability statement: The dataset of spatiotemporal accessibility for pediatric clinical services can be available through https://figshare.com/s/f6e86f1367e44bd123b2.

References

[1] Xia T, Song X, Zhang H, Song X, Kanasugi H, Shibasaki R. Measuring spatio-temporal accessibility to emergency medical services through big GPS data. Health Place. 2019;56:53–62.10.1016/j.healthplace.2019.01.012Search in Google Scholar PubMed

[2] Widener MJ, Farber S, Neutens T, Horner M. Spatiotemporal accessibility to supermarkets using public transit: an interaction potential approach in Cincinnati, Ohio. J Transp Geogr. 2015;42:72–83.10.1016/j.jtrangeo.2014.11.004Search in Google Scholar

[3] Hu W, Tan J, Li M, Wang J, Wang F. Impact of traffic on the spatiotemporal variations of spatial accessibility of emergency medical services in inner-city Shanghai. Environ Plan B Urban Anal City Sci. 2018;47(5):841–54.10.1177/2399808318809711Search in Google Scholar

[4] Batty M. The new science of cities. Cambridge, Massachusetts: MIT Press; 2013.10.7551/mitpress/9399.001.0001Search in Google Scholar

[5] Kwan M-P, Richardson D, Wang D, Zhou C. Space–time integration in geography and GIScience. Netherlands: Springer; 2015.10.1007/978-94-017-9205-9Search in Google Scholar

[6] Páez A, Scott DM, Morency C. Measuring accessibility: positive and normative implementations of various accessibility indicators. J Transp Geogr. 2012;25:141–53.10.1016/j.jtrangeo.2012.03.016Search in Google Scholar

[7] Fuller D, Cummins S, Matthews SA. Does transportation mode modify associations between distance to food store, fruit and vegetable consumption, and BMI in low-income neighborhoods? Am J Clin Nutr. 2012;97(1):167–72.10.3945/ajcn.112.036392Search in Google Scholar PubMed PubMed Central

[8] Neutens T. Accessibility, equity and health care: review and research directions for transport geographers. J Transp Geogr. 2015;43:14–27.10.1016/j.jtrangeo.2014.12.006Search in Google Scholar

[9] Lee J, Miller HJ. Measuring the impacts of new public transit services on space–time accessibility: an analysis of transit system redesign and new bus rapid transit in Columbus, Ohio, USA. Appl Geogr. 2018;93:47–63.10.1016/j.apgeog.2018.02.012Search in Google Scholar

[10] Mao L, Nekorchuk D. Measuring spatial accessibility to healthcare for populations with multiple transportation modes. Health Place. 2013;24:115–22.10.1016/j.healthplace.2013.08.008Search in Google Scholar PubMed

[11] Tahmasbi B, Mansourianfar MH, Haghshenas H, Kim I. Multimodal accessibility-based equity assessment of urban public facilities distribution. Sustain Cities Soc. 2019;49:101633.10.1016/j.scs.2019.101633Search in Google Scholar

[12] Lin Y, Wan N, Sheets S, Gong X, Davies A. A multi-modal relative spatial access assessment approach to measure spatial accessibility to primary care providers. Int J Health Geographics. 2018;17(1):33.10.1186/s12942-018-0153-9Search in Google Scholar PubMed PubMed Central

[13] Pan X, Kwan M-P, Yang L, Zhou S, Zuo Z, Wan B. Evaluating the accessibility of healthcare facilities using an integrated catchment area approach. Int J Environ Res Public Health. 2018;15(9):2051.10.3390/ijerph15092051Search in Google Scholar PubMed PubMed Central

[14] Zhang T, Dong S, Zeng Z, Li J. Quantifying multi-modal public transit accessibility for large metropolitan areas: a time-dependent reliability modeling approach. Int J Geogr Inf Sci. 2018;32(8):1649–76.10.1080/13658816.2018.1459113Search in Google Scholar

[15] Yiannakoulias N, Bland W, Svenson LW. Estimating the effect of turn penalties and traffic congestion on measuring spatial accessibility to primary health care. Appl Geogr. 2013;39:172–82.10.1016/j.apgeog.2012.12.003Search in Google Scholar

[16] Tenkanen H, Saarsalmi P, Jarv O, Salonen M, Toivonen T. Health research needs more comprehensive accessibility measures: integrating time and transport modes from open data. Int J Health Geographics. 2016;15(1):1–12.10.1186/s12942-016-0052-xSearch in Google Scholar PubMed PubMed Central

[17] Xia N, Cheng L, Chen S, Wei X, Zong W, Li M. Accessibility based on Gravity-Radiation model and Google Maps API: a case study in Australia. J Transp Geogr. 2018;72:178–90.10.1016/j.jtrangeo.2018.09.009Search in Google Scholar

[18] Ding Y, Zhou J, Li Y. Transit accessibility measures incorporating the temporal dimension. Cities. 2015;46:55–66.10.1016/j.cities.2015.05.002Search in Google Scholar

[19] Lang W, Chen T, Chan EH, Yung EH, Lee TC. Understanding livable dense urban form for shaping the landscape of community facilities in Hong Kong using fine-scale measurements. Cities. 2019;84:34–45.10.1016/j.cities.2018.07.003Search in Google Scholar

[20] Tanser F, Gijsbertsen B, Herbst K. Modelling and understanding primary health care accessibility and utilization in rural South Africa: an exploration using a geographical information system. Soc Sci & Med. 2006;63(3):691–705.10.1016/j.socscimed.2006.01.015Search in Google Scholar PubMed

[21] Schoeps A, Gabrysch S, Niamba L, Sié A, Becher H. The effect of distance to health-care facilities on childhood mortality in rural Burkina Faso. Am J Epidemiol. 2011;173(5):492–8.10.1093/aje/kwq386Search in Google Scholar PubMed

[22] Kanuganti S, Sarkar AK, Singh AP. Evaluation of access to health care in rural areas using enhanced two-step floating catchment area (E2SFCA) method. J Transp Geogr. 2016;56:45–52.10.1016/j.jtrangeo.2016.08.011Search in Google Scholar

[23] Mathon D, Apparicio P, Lachapelle U. Cross-border spatial accessibility of health care in the North-East Department of Haiti. Int J Health Geographics. 2018;17:1–15.10.1186/s12942-018-0156-6Search in Google Scholar PubMed PubMed Central

[24] Zhang S, Song X, Wei Y, Deng W. Spatial equity of multilevel healthcare in the metropolis of Chengdu, China: a new assessment approach. Int J Environ Res Public Health. 2019;16(3):493.10.3390/ijerph16030493Search in Google Scholar PubMed PubMed Central

[25] Nordbø ECA, Nordh H, Raanaas RK, Aamodt G. GIS-derived measures of the built environment determinants of mental health and activity participation in childhood and adolescence: a systematic review. Landsc Urban Plan. 2018;177:19–37.10.1016/j.landurbplan.2018.04.009Search in Google Scholar

[26] Wen H, Xiao Y, Hui EC, Zhang L. Education quality, accessibility, and housing price: does spatial heterogeneity exist in education capitalization? Habitat Int. 2018;78:68–82.10.1016/j.habitatint.2018.05.012Search in Google Scholar

[27] Cohen E, Kuo DZ, Agrawal R, Berry JG, Bhagat SK, Simon TD, et al. Children with medical complexity: an emerging population for clinical and research initiatives. Pediatrics. 2011;127(3):529–38.10.1542/peds.2010-0910Search in Google Scholar PubMed PubMed Central

[28] Glader L, Plews-Ogan J, Agrawal R. Children with medical complexity: creating a framework for care based on the International Classification of Functioning, Disability and Health. Dev Med Child Neurol. 2016;58(11):1116–23.10.1111/dmcn.13201Search in Google Scholar PubMed

[29] Guagliardo MF, Ronzio CR, Cheung I, Chacko E, Joseph JG. Physician accessibility: an urban case study of pediatric providers. Health Place. 2004;10(3):273–83.10.1016/j.healthplace.2003.01.001Search in Google Scholar PubMed

[30] Nieves JJ. Combining transportation network models with kernel density methods to measure the relative spatial accessibility of pediatric primary care services in Jefferson County, Kentucky. Int J Appl Geospatial Res (IJAGR). 2015;6(3):39–57.10.4018/ijagr.2015070103Search in Google Scholar

[31] Nobles M, Serban N, Swann J. Spatial accessibility of pediatric primary healthcare: measurement and inference. Ann Appl Stat. 2014;8(4):1922–46.10.1214/14-AOAS728Search in Google Scholar

[32] Chen X, Jia P. A comparative analysis of accessibility measures by the two-step floating catchment area (2SFCA) method. Int J Geogr Inf Sci. 2019;33(9):1–20.10.1080/13658816.2019.1591415Search in Google Scholar

[33] Boschmann EE, Kwan M-P. Metropolitan area job accessibility and the working poor: exploring local spatial variations of geographic context. Urban Geogr. 2010;31(4):498–522.10.2747/0272-3638.31.4.498Search in Google Scholar

[34] Luo W, Wang F. Measures of spatial accessibility to health care in a GIS environment: synthesis and a case study in the Chicago region. Environ Plan B: Plan Des. 2003;30(6):865–84.10.1068/b29120Search in Google Scholar PubMed PubMed Central

[35] Neutens T, Schwanen T, Witlox F, De Maeyer P. Equity of urban service delivery: a comparison of different accessibility measures. Environ Plan A. 2010;42(7):1613–35.10.1068/a4230Search in Google Scholar

[36] Wang F. Measurement, optimization, and impact of health care accessibility: a methodological review. Ann Assoc Am Geographers. 2012;102(5):1104–12.10.1080/00045608.2012.657146Search in Google Scholar PubMed PubMed Central

[37] Ilägcrstrand T, editor. What about people in regional science? Papers of the Regional Science Association; 1970.10.1007/BF01936872Search in Google Scholar

[38] Tenkanen H, Saarsalmi P, Järv O, Salonen M, Toivonen T. Health research needs more comprehensive accessibility measures: integrating time and transport modes from open data. Int J Health Geographics. 2016;15(1):1–12.10.1186/s12942-016-0052-xSearch in Google Scholar PubMed PubMed Central

[39] Tomasiello DB, Giannotti M, Arbex R, Davis C. Multi-temporal transport network models for accessibility studies. Trans GIS. 2019;23(2):203–3.10.1111/tgis.12513Search in Google Scholar

[40] Dony CC, Delmelle EM, Delmelle EC. Re-conceptualizing accessibility to parks in multi-modal cities: a variable-width floating catchment area (VFCA) method. Landsc Urban Plan. 2015;143:90–9.10.1016/j.landurbplan.2015.06.011Search in Google Scholar

[41] García-Albertos P, Picornell M, Salas-Olmedo MH, Gutiérrez J. Exploring the potential of mobile phone records and online route planners for dynamic accessibility analysis. Transp Res Part A Policy Pract. 2018;125:294–307.10.1016/j.tra.2018.02.008Search in Google Scholar

[42] Weiss DJ, Nelson A, Gibson H, Temperley W, Peedell S, Lieber A, et al. A global map of travel time to cities to assess inequalities in accessibility in 2015. Nature. 2018;553(7688):333–6.10.1038/nature25181Search in Google Scholar PubMed

[43] Tao Z, Yao Z, Kong H, Duan F, Li G. Spatial accessibility to healthcare services in Shenzhen, China: improving the multi-modal two-step floating catchment area method by estimating travel time via online map APIs. BMC Health Services Research. 2018;18(1):1–10.10.1186/s12913-018-3132-8Search in Google Scholar PubMed PubMed Central

[44] Zhou X, Ding Y, Wu C, Huang J, Hu C. Measuring the spatial allocation rationality of service facilities of residential areas based on internet map and location-based service data. Sustainability. 2019;11(5):1337.10.3390/su11051337Search in Google Scholar

[45] Huang J, Levinson D, Wang J, Zhou J, Wang ZJ. Tracking job and housing dynamics with smartcard data. Proc Natl Acad Sci. 2018;115(50):12710–5.10.1073/pnas.1815928115Search in Google Scholar PubMed PubMed Central

[46] Xu Y, Shaw S-L, Zhao Z, Yin L, Lu F, Chen J, et al. Another tale of two cities: understanding human activity space using actively tracked cellphone location data. Ann Am Assoc Geographers. 2016;106(2):489–502.10.4324/9781315266336-27Search in Google Scholar

[47] Liu Y, Liu X, Gao S, Gong L, Kang C, Zhi Y, et al. Social sensing: a new approach to understanding our socioeconomic environments. Ann Assoc Am Geographers. 2015;105(3):512–30.10.1080/00045608.2015.1018773Search in Google Scholar

[48] Yang C, Clarke K, Shekhar S, Tao CV. Big Spatiotemporal Data Analytics: a research and innovation frontier. Int J Geogr Inf Sci. 2020;34(6):1075–88.10.1080/13658816.2019.1698743Search in Google Scholar

[49] Pilkington H, Prunet C, Blondel B, Charreire H, Combier E, Le Vaillant M, et al. Travel time to hospital for childbirth: comparing calculated versus reported travel times in France. Matern Child Health J. 2018;22(1):101–10.10.1007/s10995-017-2359-zSearch in Google Scholar PubMed

[50] Järv O, Tenkanen H, Salonen M, Ahas R, Toivonen T. Dynamic cities: location-based accessibility modelling as a function of time. Appl Geogr. 2018;95:101–10.10.1016/j.apgeog.2018.04.009Search in Google Scholar

[51] Frank LD, Pivo G. Impacts of mixed use and density on utilization of three modes of travel: single-occupant vehicle, transit, and walking. Transp Res Rec. 1994;1466:44–52.Search in Google Scholar

[52] Hutchinson TP, editor. The customer experience when using public transport: a review. Proceedings of the Institution of Civil Engineers-Municipal Engineer. London: Thomas Telford Ltd; 2009.10.1680/muen.2009.162.3.149Search in Google Scholar

[53] Feng G, Mingzhe W. Route choice behavior model with guidance information. J Transp Syst Eng Inf Technol. 2010;10(6):64–9.10.1016/S1570-6672(09)60072-6Search in Google Scholar

[54] Tseng Y-Y, Knockaert J, Verhoef ET. A revealed-preference study of behavioural impacts of real-time traffic information. Transp Res Part C Emerg Technol. 2013;30:196–209.10.1016/j.trc.2011.11.006Search in Google Scholar

[55] Chen C. Task complexity and time pressure: Impacts on activity-travel choices. Phd thesis. Technische: Delft University of Technology; 2014.Search in Google Scholar

[56] Dastjerdi AM, Kaplan S, e Silva JD, Nielsen OA, Pereira FC. Participating in environmental loyalty program with a real-time multimodal travel app: user needs, environmental and privacy motivators. Transp Res Part D Transp Environ. 2019;67:223–43.10.1016/j.trd.2018.11.013Search in Google Scholar

[57] China. China Urban Construction Statistical Yearbook; 2016.Search in Google Scholar

[58] Zhuo L, Shi Q, Zhang C, Li Q, Tao H. Identifying building functions from the spatiotemporal population density and the interactions of people among buildings. ISPRS Int J Geo-Information. 2019;8(6):247.10.3390/ijgi8060247Search in Google Scholar

[59] Liu X, He J, Yao Y, Zhang J, Liang H, Wang H, et al. Classifying urban land use by integrating remote sensing and social media data. Int J Geogr Inf Sci. 2017;31(8):1675–96.10.1080/13658816.2017.1324976Search in Google Scholar

[60] Chen Y, Liu X, Li X, Liu X, Yao Y, Hu G, et al. Delineating urban functional areas with building-level social media data: a dynamic time warping (DTW) distance based k-medoids method. Landsc Urban Plan. 2017;160:48–60.10.1016/j.landurbplan.2016.12.001Search in Google Scholar

[61] Yao Y, Liu X, Li X, Zhang J, Liang Z, Mai K, et al. Mapping fine-scale population distributions at the building level by integrating multisource geospatial big data. Int J Geogr Inf Sci. 2017;31(6):1220–44.10.1080/13658816.2017.1290252Search in Google Scholar

[62] Nanjing SBo. A brief analysis of population data. Nanjing: Statistics Bureau; 2018.Search in Google Scholar

[63] Nanjing Pclgoo. Tabulation on the 2010 population census of Nanjing city. Jiangsu Education Press; 2012.Search in Google Scholar

[64] Wang J, Xu C, Tong S, Yang W. S-146: Spatial dynamic pattern of hand-foot-mouth disease in China. Epidemiology. 2012;23(5S):1.10.1097/01.ede.0000416999.32352.b8Search in Google Scholar

[65] Jiyuan L, Zengxiang Z, Xinliang XU, Wenhui K, Wancun Z, Shuwen Z, et al. Spatial patterns and driving forces of land use change in China in the early 21st century. Acta Geogr Sin. 2010;20(4):483–94.10.1007/s11442-010-0483-4Search in Google Scholar

[66] Litman T. Transportation cost and benefit analysis. Vic Transp Policy Inst. 2009;31:1–19.Search in Google Scholar

[67] Litman T. Full cost accounting of urban transportation: implications and tools. Cities. 1997;14(3):169–74.10.1016/S0264-2751(97)00057-7Search in Google Scholar

[68] Hansen WG. How accessibility shapes land use. J Am Inst Plan. 1959;25:73–6.10.1080/01944365908978307Search in Google Scholar

[69] Krueckeberg DA, Silvers AL. Urban planning analysis: methods and models. New York: John Wiley & Sons; 1974.Search in Google Scholar

[70] Yao J, Murray AT, Agadjanian V. A geographical perspective on access to sexual and reproductive health care for women in rural Africa. Soc Sci Med. 2013;96:60–8.10.1016/j.socscimed.2013.07.025Search in Google Scholar PubMed PubMed Central

[71] Carleton PR, Porter JD. A comparative analysis of the challenges in measuring transit equity: definitions, interpretations, and limitations. J Transp Geogr. 2018;72:64–75.10.1016/j.jtrangeo.2018.08.012Search in Google Scholar

[72] Wang Y, Tong D, Li W, Liu Y. Optimizing the spatial relocation of hospitals to reduce urban traffic congestion: a case study of Beijing. Trans GIS. 2019;23(2):365–86.10.1111/tgis.12524Search in Google Scholar

[73] Zhang W, Cao K, Liu S, Huang B. A multi-objective optimization approach for health-care facility location-allocation problems in highly developed cities such as Hong Kong. Compu Environ Urban Syst. 2016;59:220–30.10.1016/j.compenvurbsys.2016.07.001Search in Google Scholar

[74] Smith CM, Fry H, Anderson C, Maguire H, Hayward AC. Optimising spatial accessibility to inform rationalisation of specialist health services. Int J Health Geographics. 2017;16(1):15.10.1186/s12942-017-0088-6Search in Google Scholar PubMed PubMed Central

[75] Hu W, Li L, Su M. Spatial inequity of multi-level healthcare services in a rapid expanding immigrant city of China: a case study of Shenzhen. Int J Environ Res Public Health. 2019;16(18):3441.10.3390/ijerph16183441Search in Google Scholar PubMed PubMed Central

[76] Watts DJ. Should social science be more solution-oriented? Nat Hum Behav. 2017;1(1):1–5.10.1038/s41562-016-0015Search in Google Scholar
Received: 2021-08-14
Revised: 2022-04-22
Accepted: 2023-02-09
Published Online: 2023-03-09

© 2023 the author(s), published by De Gruyter

This work is licensed under the Creative Commons Attribution 4.0 International License.

Articles in the same Issue

  1. Regular Articles
  2. Diagenesis and evolution of deep tight reservoirs: A case study of the fourth member of Shahejie Formation (cg: 50.4-42 Ma) in Bozhong Sag
  3. Petrography and mineralogy of the Oligocene flysch in Ionian Zone, Albania: Implications for the evolution of sediment provenance and paleoenvironment
  4. Biostratigraphy of the Late Campanian–Maastrichtian of the Duwi Basin, Red Sea, Egypt
  5. Structural deformation and its implication for hydrocarbon accumulation in the Wuxia fault belt, northwestern Junggar basin, China
  6. Carbonate texture identification using multi-layer perceptron neural network
  7. Metallogenic model of the Hongqiling Cu–Ni sulfide intrusions, Central Asian Orogenic Belt: Insight from long-period magnetotellurics
  8. Assessments of recent Global Geopotential Models based on GPS/levelling and gravity data along coastal zones of Egypt
  9. Accuracy assessment and improvement of SRTM, ASTER, FABDEM, and MERIT DEMs by polynomial and optimization algorithm: A case study (Khuzestan Province, Iran)
  10. Uncertainty assessment of 3D geological models based on spatial diffusion and merging model
  11. Evaluation of dynamic behavior of varved clays from the Warsaw ice-dammed lake, Poland
  12. Impact of AMSU-A and MHS radiances assimilation on Typhoon Megi (2016) forecasting
  13. Contribution to the building of a weather information service for solar panel cleaning operations at Diass plant (Senegal, Western Sahel)
  14. Measuring spatiotemporal accessibility to healthcare with multimodal transport modes in the dynamic traffic environment
  15. Mathematical model for conversion of groundwater flow from confined to unconfined aquifers with power law processes
  16. NSP variation on SWAT with high-resolution data: A case study
  17. Reconstruction of paleoglacial equilibrium-line altitudes during the Last Glacial Maximum in the Diancang Massif, Northwest Yunnan Province, China
  18. A prediction model for Xiangyang Neolithic sites based on a random forest algorithm
  19. Determining the long-term impact area of coastal thermal discharge based on a harmonic model of sea surface temperature
  20. Origin of block accumulations based on the near-surface geophysics
  21. Investigating the limestone quarries as geoheritage sites: Case of Mardin ancient quarry
  22. Population genetics and pedigree geography of Trionychia japonica in the four mountains of Henan Province and the Taihang Mountains
  23. Performance audit evaluation of marine development projects based on SPA and BP neural network model
  24. Study on the Early Cretaceous fluvial-desert sedimentary paleogeography in the Northwest of Ordos Basin
  25. Detecting window line using an improved stacked hourglass network based on new real-world building façade dataset
  26. Automated identification and mapping of geological folds in cross sections
  27. Silicate and carbonate mixed shelf formation and its controlling factors, a case study from the Cambrian Canglangpu formation in Sichuan basin, China
  28. Ground penetrating radar and magnetic gradient distribution approach for subsurface investigation of solution pipes in post-glacial settings
  29. Research on pore structures of fine-grained carbonate reservoirs and their influence on waterflood development
  30. Risk assessment of rain-induced debris flow in the lower reaches of Yajiang River based on GIS and CF coupling models
  31. Multifractal analysis of temporal and spatial characteristics of earthquakes in Eurasian seismic belt
  32. Surface deformation and damage of 2022 (M 6.8) Luding earthquake in China and its tectonic implications
  33. Differential analysis of landscape patterns of land cover products in tropical marine climate zones – A case study in Malaysia
  34. DEM-based analysis of tectonic geomorphologic characteristics and tectonic activity intensity of the Dabanghe River Basin in South China Karst
  35. Distribution, pollution levels, and health risk assessment of heavy metals in groundwater in the main pepper production area of China
  36. Study on soil quality effect of reconstructing by Pisha sandstone and sand soil
  37. Understanding the characteristics of loess strata and quaternary climate changes in Luochuan, Shaanxi Province, China, through core analysis
  38. Dynamic variation of groundwater level and its influencing factors in typical oasis irrigated areas in Northwest China
  39. Creating digital maps for geotechnical characteristics of soil based on GIS technology and remote sensing
  40. Changes in the course of constant loading consolidation in soil with modeled granulometric composition contaminated with petroleum substances
  41. Correlation between the deformation of mineral crystal structures and fault activity: A case study of the Yingxiu-Beichuan fault and the Milin fault
  42. Cognitive characteristics of the Qiang religious culture and its influencing factors in Southwest China
  43. Spatiotemporal variation characteristics analysis of infrastructure iron stock in China based on nighttime light data
  44. Interpretation of aeromagnetic and remote sensing data of Auchi and Idah sheets of the Benin-arm Anambra basin: Implication of mineral resources
  45. Building element recognition with MTL-AINet considering view perspectives
  46. Characteristics of the present crustal deformation in the Tibetan Plateau and its relationship with strong earthquakes
  47. Influence of fractures in tight sandstone oil reservoir on hydrocarbon accumulation: A case study of Yanchang Formation in southeastern Ordos Basin
  48. Nutrient assessment and land reclamation in the Loess hills and Gulch region in the context of gully control
  49. Handling imbalanced data in supervised machine learning for lithological mapping using remote sensing and airborne geophysical data
  50. Spatial variation of soil nutrients and evaluation of cultivated land quality based on field scale
  51. Lignin analysis of sediments from around 2,000 to 1,000 years ago (Jiulong River estuary, southeast China)
  52. Assessing OpenStreetMap roads fitness-for-use for disaster risk assessment in developing countries: The case of Burundi
  53. Transforming text into knowledge graph: Extracting and structuring information from spatial development plans
  54. A symmetrical exponential model of soil temperature in temperate steppe regions of China
  55. A landslide susceptibility assessment method based on auto-encoder improved deep belief network
  56. Numerical simulation analysis of ecological monitoring of small reservoir dam based on maximum entropy algorithm
  57. Morphometry of the cold-climate Bory Stobrawskie Dune Field (SW Poland): Evidence for multi-phase Lateglacial aeolian activity within the European Sand Belt
  58. Adopting a new approach for finding missing people using GIS techniques: A case study in Saudi Arabia’s desert area
  59. Geological earthquake simulations generated by kinematic heterogeneous energy-based method: Self-arrested ruptures and asperity criterion
  60. Semi-automated classification of layered rock slopes using digital elevation model and geological map
  61. Geochemical characteristics of arc fractionated I-type granitoids of eastern Tak Batholith, Thailand
  62. Lithology classification of igneous rocks using C-band and L-band dual-polarization SAR data
  63. Analysis of artificial intelligence approaches to predict the wall deflection induced by deep excavation
  64. Evaluation of the current in situ stress in the middle Permian Maokou Formation in the Longnüsi area of the central Sichuan Basin, China
  65. Utilizing microresistivity image logs to recognize conglomeratic channel architectural elements of Baikouquan Formation in slope of Mahu Sag
  66. Resistivity cutoff of low-resistivity and low-contrast pays in sandstone reservoirs from conventional well logs: A case of Paleogene Enping Formation in A-Oilfield, Pearl River Mouth Basin, South China Sea
  67. Examining the evacuation routes of the sister village program by using the ant colony optimization algorithm
  68. Spatial objects classification using machine learning and spatial walk algorithm
  69. Study on the stabilization mechanism of aeolian sandy soil formation by adding a natural soft rock
  70. Bump feature detection of the road surface based on the Bi-LSTM
  71. The origin and evolution of the ore-forming fluids at the Manondo-Choma gold prospect, Kirk range, southern Malawi
  72. A retrieval model of surface geochemistry composition based on remotely sensed data
  73. Exploring the spatial dynamics of cultural facilities based on multi-source data: A case study of Nanjing’s art institutions
  74. Study of pore-throat structure characteristics and fluid mobility of Chang 7 tight sandstone reservoir in Jiyuan area, Ordos Basin
  75. Study of fracturing fluid re-discharge based on percolation experiments and sampling tests – An example of Fuling shale gas Jiangdong block, China
  76. Impacts of marine cloud brightening scheme on climatic extremes in the Tibetan Plateau
  77. Ecological protection on the West Coast of Taiwan Strait under economic zone construction: A case study of land use in Yueqing
  78. The time-dependent deformation and damage constitutive model of rock based on dynamic disturbance tests
  79. Evaluation of spatial form of rural ecological landscape and vulnerability of water ecological environment based on analytic hierarchy process
  80. Fingerprint of magma mixture in the leucogranites: Spectroscopic and petrochemical approach, Kalebalta-Central Anatolia, Türkiye
  81. Principles of self-calibration and visual effects for digital camera distortion
  82. UAV-based doline mapping in Brazilian karst: A cave heritage protection reconnaissance
  83. Evaluation and low carbon ecological urban–rural planning and construction based on energy planning mechanism
  84. Modified non-local means: A novel denoising approach to process gravity field data
  85. A novel travel route planning method based on an ant colony optimization algorithm
  86. Effect of time-variant NDVI on landside susceptibility: A case study in Quang Ngai province, Vietnam
  87. Regional tectonic uplift indicated by geomorphological parameters in the Bahe River Basin, central China
  88. Computer information technology-based green excavation of tunnels in complex strata and technical decision of deformation control
  89. Spatial evolution of coastal environmental enterprises: An exploration of driving factors in Jiangsu Province
  90. A comparative assessment and geospatial simulation of three hydrological models in urban basins
  91. Aquaculture industry under the blue transformation in Jiangsu, China: Structure evolution and spatial agglomeration
  92. Quantitative and qualitative interpretation of community partitions by map overlaying and calculating the distribution of related geographical features
  93. Numerical investigation of gravity-grouted soil-nail pullout capacity in sand
  94. Analysis of heavy pollution weather in Shenyang City and numerical simulation of main pollutants
  95. Road cut slope stability analysis for static and dynamic (pseudo-static analysis) loading conditions
  96. Forest biomass assessment combining field inventorying and remote sensing data
  97. Late Jurassic Haobugao granites from the southern Great Xing’an Range, NE China: Implications for postcollision extension of the Mongol–Okhotsk Ocean
  98. Petrogenesis of the Sukadana Basalt based on petrology and whole rock geochemistry, Lampung, Indonesia: Geodynamic significances
  99. Numerical study on the group wall effect of nodular diaphragm wall foundation in high-rise buildings
  100. Water resources utilization and tourism environment assessment based on water footprint
  101. Geochemical evaluation of the carbonaceous shale associated with the Permian Mikambeni Formation of the Tuli Basin for potential gas generation, South Africa
  102. Detection and characterization of lineaments using gravity data in the south-west Cameroon zone: Hydrogeological implications
  103. Study on spatial pattern of tourism landscape resources in county cities of Yangtze River Economic Belt
  104. The effect of weathering on drillability of dolomites
  105. Noise masking of near-surface scattering (heterogeneities) on subsurface seismic reflectivity
  106. Query optimization-oriented lateral expansion method of distributed geological borehole database
  107. Petrogenesis of the Morobe Granodiorite and their shoshonitic mafic microgranular enclaves in Maramuni arc, Papua New Guinea
  108. Environmental health risk assessment of urban water sources based on fuzzy set theory
  109. Spatial distribution of urban basic education resources in Shanghai: Accessibility and supply-demand matching evaluation
  110. Spatiotemporal changes in land use and residential satisfaction in the Huai River-Gaoyou Lake Rim area
  111. Walkaway vertical seismic profiling first-arrival traveltime tomography with velocity structure constraints
  112. Study on the evaluation system and risk factor traceability of receiving water body
  113. Predicting copper-polymetallic deposits in Kalatag using the weight of evidence model and novel data sources
  114. Temporal dynamics of green urban areas in Romania. A comparison between spatial and statistical data
  115. Passenger flow forecast of tourist attraction based on MACBL in LBS big data environment
  116. Varying particle size selectivity of soil erosion along a cultivated catena
  117. Relationship between annual soil erosion and surface runoff in Wadi Hanifa sub-basins
  118. Influence of nappe structure on the Carboniferous volcanic reservoir in the middle of the Hongche Fault Zone, Junggar Basin, China
  119. Dynamic analysis of MSE wall subjected to surface vibration loading
  120. Pre-collisional architecture of the European distal margin: Inferences from the high-pressure continental units of central Corsica (France)
  121. The interrelation of natural diversity with tourism in Kosovo
  122. Assessment of geosites as a basis for geotourism development: A case study of the Toplica District, Serbia
  123. IG-YOLOv5-based underwater biological recognition and detection for marine protection
  124. Monitoring drought dynamics using remote sensing-based combined drought index in Ergene Basin, Türkiye
  125. Review Articles
  126. The actual state of the geodetic and cartographic resources and legislation in Poland
  127. Evaluation studies of the new mining projects
  128. Comparison and significance of grain size parameters of the Menyuan loess calculated using different methods
  129. Scientometric analysis of flood forecasting for Asia region and discussion on machine learning methods
  130. Rainfall-induced transportation embankment failure: A review
  131. Rapid Communication
  132. Branch fault discovered in Tangshan fault zone on the Kaiping-Guye boundary, North China
  133. Technical Note
  134. Introducing an intelligent multi-level retrieval method for mineral resource potential evaluation result data
  135. Erratum
  136. Erratum to “Forest cover assessment using remote-sensing techniques in Crete Island, Greece”
  137. Addendum
  138. The relationship between heat flow and seismicity in global tectonically active zones
  139. Commentary
  140. Improved entropy weight methods and their comparisons in evaluating the high-quality development of Qinghai, China
  141. Special Issue: Geoethics 2022 - Part II
  142. Loess and geotourism potential of the Braničevo District (NE Serbia): From overexploitation to paleoclimate interpretation
https://doi.org/10.1515/geo-2022-0461
Keywords for this article
spatiotemporal accessibility; online route planners; healthcare services; multimodal transport modes; residential transport mode choices; dynamic traffic environment
Creative Commons
BY 4.0

Articles in the same Issue

  1. Regular Articles
  2. Diagenesis and evolution of deep tight reservoirs: A case study of the fourth member of Shahejie Formation (cg: 50.4-42 Ma) in Bozhong Sag
  3. Petrography and mineralogy of the Oligocene flysch in Ionian Zone, Albania: Implications for the evolution of sediment provenance and paleoenvironment
  4. Biostratigraphy of the Late Campanian–Maastrichtian of the Duwi Basin, Red Sea, Egypt
  5. Structural deformation and its implication for hydrocarbon accumulation in the Wuxia fault belt, northwestern Junggar basin, China
  6. Carbonate texture identification using multi-layer perceptron neural network
  7. Metallogenic model of the Hongqiling Cu–Ni sulfide intrusions, Central Asian Orogenic Belt: Insight from long-period magnetotellurics
  8. Assessments of recent Global Geopotential Models based on GPS/levelling and gravity data along coastal zones of Egypt
  9. Accuracy assessment and improvement of SRTM, ASTER, FABDEM, and MERIT DEMs by polynomial and optimization algorithm: A case study (Khuzestan Province, Iran)
  10. Uncertainty assessment of 3D geological models based on spatial diffusion and merging model
  11. Evaluation of dynamic behavior of varved clays from the Warsaw ice-dammed lake, Poland
  12. Impact of AMSU-A and MHS radiances assimilation on Typhoon Megi (2016) forecasting
  13. Contribution to the building of a weather information service for solar panel cleaning operations at Diass plant (Senegal, Western Sahel)
  14. Measuring spatiotemporal accessibility to healthcare with multimodal transport modes in the dynamic traffic environment
  15. Mathematical model for conversion of groundwater flow from confined to unconfined aquifers with power law processes
  16. NSP variation on SWAT with high-resolution data: A case study
  17. Reconstruction of paleoglacial equilibrium-line altitudes during the Last Glacial Maximum in the Diancang Massif, Northwest Yunnan Province, China
  18. A prediction model for Xiangyang Neolithic sites based on a random forest algorithm
  19. Determining the long-term impact area of coastal thermal discharge based on a harmonic model of sea surface temperature
  20. Origin of block accumulations based on the near-surface geophysics
  21. Investigating the limestone quarries as geoheritage sites: Case of Mardin ancient quarry
  22. Population genetics and pedigree geography of Trionychia japonica in the four mountains of Henan Province and the Taihang Mountains
  23. Performance audit evaluation of marine development projects based on SPA and BP neural network model
  24. Study on the Early Cretaceous fluvial-desert sedimentary paleogeography in the Northwest of Ordos Basin
  25. Detecting window line using an improved stacked hourglass network based on new real-world building façade dataset
  26. Automated identification and mapping of geological folds in cross sections
  27. Silicate and carbonate mixed shelf formation and its controlling factors, a case study from the Cambrian Canglangpu formation in Sichuan basin, China
  28. Ground penetrating radar and magnetic gradient distribution approach for subsurface investigation of solution pipes in post-glacial settings
  29. Research on pore structures of fine-grained carbonate reservoirs and their influence on waterflood development
  30. Risk assessment of rain-induced debris flow in the lower reaches of Yajiang River based on GIS and CF coupling models
  31. Multifractal analysis of temporal and spatial characteristics of earthquakes in Eurasian seismic belt
  32. Surface deformation and damage of 2022 (M 6.8) Luding earthquake in China and its tectonic implications
  33. Differential analysis of landscape patterns of land cover products in tropical marine climate zones – A case study in Malaysia
  34. DEM-based analysis of tectonic geomorphologic characteristics and tectonic activity intensity of the Dabanghe River Basin in South China Karst
  35. Distribution, pollution levels, and health risk assessment of heavy metals in groundwater in the main pepper production area of China
  36. Study on soil quality effect of reconstructing by Pisha sandstone and sand soil
  37. Understanding the characteristics of loess strata and quaternary climate changes in Luochuan, Shaanxi Province, China, through core analysis
  38. Dynamic variation of groundwater level and its influencing factors in typical oasis irrigated areas in Northwest China
  39. Creating digital maps for geotechnical characteristics of soil based on GIS technology and remote sensing
  40. Changes in the course of constant loading consolidation in soil with modeled granulometric composition contaminated with petroleum substances
  41. Correlation between the deformation of mineral crystal structures and fault activity: A case study of the Yingxiu-Beichuan fault and the Milin fault
  42. Cognitive characteristics of the Qiang religious culture and its influencing factors in Southwest China
  43. Spatiotemporal variation characteristics analysis of infrastructure iron stock in China based on nighttime light data
  44. Interpretation of aeromagnetic and remote sensing data of Auchi and Idah sheets of the Benin-arm Anambra basin: Implication of mineral resources
  45. Building element recognition with MTL-AINet considering view perspectives
  46. Characteristics of the present crustal deformation in the Tibetan Plateau and its relationship with strong earthquakes
  47. Influence of fractures in tight sandstone oil reservoir on hydrocarbon accumulation: A case study of Yanchang Formation in southeastern Ordos Basin
  48. Nutrient assessment and land reclamation in the Loess hills and Gulch region in the context of gully control
  49. Handling imbalanced data in supervised machine learning for lithological mapping using remote sensing and airborne geophysical data
  50. Spatial variation of soil nutrients and evaluation of cultivated land quality based on field scale
  51. Lignin analysis of sediments from around 2,000 to 1,000 years ago (Jiulong River estuary, southeast China)
  52. Assessing OpenStreetMap roads fitness-for-use for disaster risk assessment in developing countries: The case of Burundi
  53. Transforming text into knowledge graph: Extracting and structuring information from spatial development plans
  54. A symmetrical exponential model of soil temperature in temperate steppe regions of China
  55. A landslide susceptibility assessment method based on auto-encoder improved deep belief network
  56. Numerical simulation analysis of ecological monitoring of small reservoir dam based on maximum entropy algorithm
  57. Morphometry of the cold-climate Bory Stobrawskie Dune Field (SW Poland): Evidence for multi-phase Lateglacial aeolian activity within the European Sand Belt
  58. Adopting a new approach for finding missing people using GIS techniques: A case study in Saudi Arabia’s desert area
  59. Geological earthquake simulations generated by kinematic heterogeneous energy-based method: Self-arrested ruptures and asperity criterion
  60. Semi-automated classification of layered rock slopes using digital elevation model and geological map
  61. Geochemical characteristics of arc fractionated I-type granitoids of eastern Tak Batholith, Thailand
  62. Lithology classification of igneous rocks using C-band and L-band dual-polarization SAR data
  63. Analysis of artificial intelligence approaches to predict the wall deflection induced by deep excavation
  64. Evaluation of the current in situ stress in the middle Permian Maokou Formation in the Longnüsi area of the central Sichuan Basin, China
  65. Utilizing microresistivity image logs to recognize conglomeratic channel architectural elements of Baikouquan Formation in slope of Mahu Sag
  66. Resistivity cutoff of low-resistivity and low-contrast pays in sandstone reservoirs from conventional well logs: A case of Paleogene Enping Formation in A-Oilfield, Pearl River Mouth Basin, South China Sea
  67. Examining the evacuation routes of the sister village program by using the ant colony optimization algorithm
  68. Spatial objects classification using machine learning and spatial walk algorithm
  69. Study on the stabilization mechanism of aeolian sandy soil formation by adding a natural soft rock
  70. Bump feature detection of the road surface based on the Bi-LSTM
  71. The origin and evolution of the ore-forming fluids at the Manondo-Choma gold prospect, Kirk range, southern Malawi
  72. A retrieval model of surface geochemistry composition based on remotely sensed data
  73. Exploring the spatial dynamics of cultural facilities based on multi-source data: A case study of Nanjing’s art institutions
  74. Study of pore-throat structure characteristics and fluid mobility of Chang 7 tight sandstone reservoir in Jiyuan area, Ordos Basin
  75. Study of fracturing fluid re-discharge based on percolation experiments and sampling tests – An example of Fuling shale gas Jiangdong block, China
  76. Impacts of marine cloud brightening scheme on climatic extremes in the Tibetan Plateau
  77. Ecological protection on the West Coast of Taiwan Strait under economic zone construction: A case study of land use in Yueqing
  78. The time-dependent deformation and damage constitutive model of rock based on dynamic disturbance tests
  79. Evaluation of spatial form of rural ecological landscape and vulnerability of water ecological environment based on analytic hierarchy process
  80. Fingerprint of magma mixture in the leucogranites: Spectroscopic and petrochemical approach, Kalebalta-Central Anatolia, Türkiye
  81. Principles of self-calibration and visual effects for digital camera distortion
  82. UAV-based doline mapping in Brazilian karst: A cave heritage protection reconnaissance
  83. Evaluation and low carbon ecological urban–rural planning and construction based on energy planning mechanism
  84. Modified non-local means: A novel denoising approach to process gravity field data
  85. A novel travel route planning method based on an ant colony optimization algorithm
  86. Effect of time-variant NDVI on landside susceptibility: A case study in Quang Ngai province, Vietnam
  87. Regional tectonic uplift indicated by geomorphological parameters in the Bahe River Basin, central China
  88. Computer information technology-based green excavation of tunnels in complex strata and technical decision of deformation control
  89. Spatial evolution of coastal environmental enterprises: An exploration of driving factors in Jiangsu Province
  90. A comparative assessment and geospatial simulation of three hydrological models in urban basins
  91. Aquaculture industry under the blue transformation in Jiangsu, China: Structure evolution and spatial agglomeration
  92. Quantitative and qualitative interpretation of community partitions by map overlaying and calculating the distribution of related geographical features
  93. Numerical investigation of gravity-grouted soil-nail pullout capacity in sand
  94. Analysis of heavy pollution weather in Shenyang City and numerical simulation of main pollutants
  95. Road cut slope stability analysis for static and dynamic (pseudo-static analysis) loading conditions
  96. Forest biomass assessment combining field inventorying and remote sensing data
  97. Late Jurassic Haobugao granites from the southern Great Xing’an Range, NE China: Implications for postcollision extension of the Mongol–Okhotsk Ocean
  98. Petrogenesis of the Sukadana Basalt based on petrology and whole rock geochemistry, Lampung, Indonesia: Geodynamic significances
  99. Numerical study on the group wall effect of nodular diaphragm wall foundation in high-rise buildings
  100. Water resources utilization and tourism environment assessment based on water footprint
  101. Geochemical evaluation of the carbonaceous shale associated with the Permian Mikambeni Formation of the Tuli Basin for potential gas generation, South Africa
  102. Detection and characterization of lineaments using gravity data in the south-west Cameroon zone: Hydrogeological implications
  103. Study on spatial pattern of tourism landscape resources in county cities of Yangtze River Economic Belt
  104. The effect of weathering on drillability of dolomites
  105. Noise masking of near-surface scattering (heterogeneities) on subsurface seismic reflectivity
  106. Query optimization-oriented lateral expansion method of distributed geological borehole database
  107. Petrogenesis of the Morobe Granodiorite and their shoshonitic mafic microgranular enclaves in Maramuni arc, Papua New Guinea
  108. Environmental health risk assessment of urban water sources based on fuzzy set theory
  109. Spatial distribution of urban basic education resources in Shanghai: Accessibility and supply-demand matching evaluation
  110. Spatiotemporal changes in land use and residential satisfaction in the Huai River-Gaoyou Lake Rim area
  111. Walkaway vertical seismic profiling first-arrival traveltime tomography with velocity structure constraints
  112. Study on the evaluation system and risk factor traceability of receiving water body
  113. Predicting copper-polymetallic deposits in Kalatag using the weight of evidence model and novel data sources
  114. Temporal dynamics of green urban areas in Romania. A comparison between spatial and statistical data
  115. Passenger flow forecast of tourist attraction based on MACBL in LBS big data environment
  116. Varying particle size selectivity of soil erosion along a cultivated catena
  117. Relationship between annual soil erosion and surface runoff in Wadi Hanifa sub-basins
  118. Influence of nappe structure on the Carboniferous volcanic reservoir in the middle of the Hongche Fault Zone, Junggar Basin, China
  119. Dynamic analysis of MSE wall subjected to surface vibration loading
  120. Pre-collisional architecture of the European distal margin: Inferences from the high-pressure continental units of central Corsica (France)
  121. The interrelation of natural diversity with tourism in Kosovo
  122. Assessment of geosites as a basis for geotourism development: A case study of the Toplica District, Serbia
  123. IG-YOLOv5-based underwater biological recognition and detection for marine protection
  124. Monitoring drought dynamics using remote sensing-based combined drought index in Ergene Basin, Türkiye
  125. Review Articles
  126. The actual state of the geodetic and cartographic resources and legislation in Poland
  127. Evaluation studies of the new mining projects
  128. Comparison and significance of grain size parameters of the Menyuan loess calculated using different methods
  129. Scientometric analysis of flood forecasting for Asia region and discussion on machine learning methods
  130. Rainfall-induced transportation embankment failure: A review
  131. Rapid Communication
  132. Branch fault discovered in Tangshan fault zone on the Kaiping-Guye boundary, North China
  133. Technical Note
  134. Introducing an intelligent multi-level retrieval method for mineral resource potential evaluation result data
  135. Erratum
  136. Erratum to “Forest cover assessment using remote-sensing techniques in Crete Island, Greece”
  137. Addendum
  138. The relationship between heat flow and seismicity in global tectonically active zones
  139. Commentary
  140. Improved entropy weight methods and their comparisons in evaluating the high-quality development of Qinghai, China
  141. Special Issue: Geoethics 2022 - Part II
  142. Loess and geotourism potential of the Braničevo District (NE Serbia): From overexploitation to paleoclimate interpretation
Sign up now to receive a 20% welcome discount
Institutional Access
How does access work?
Have an idea on how to improve our website?
Please write us.
© 2025 De Gruyter Brill
Downloaded on 13.9.2025 from https://www.degruyterbrill.com/document/doi/10.1515/geo-2022-0461/html
Scroll to top button