Spatial equity of park green space using KD2SFCA and web map API: A case study of zhengzhou, China

Zheng, Zhicheng; Shen, Wei; Yang, Linzhang; Qin, Yaochen; Wang, Lu

doi:10.1016/j.apgeog.2020.102310

Cited by 68 publications

(39 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Some studies have confirmed that citizens' actual routes and time consumption were more meaningful than Euclidean distance, and high travel costs may diminish residents' desire to visit parks [39,45]. Interestingly, we found that residents' usage patterns of the different parks were relatively consistent from the perspective of travel time consumption.…”

Section: Spatial Relationship Between Park Service Area and Accessibility Based On Time Thresholdssupporting

confidence: 60%

“…However, these studies have paid little attention to the travel routes and time consumption of visitors for recreation, instead only looking at their origin-destination lines from the park to home as an actual supply-and-demand service flow. In fact, the likelihood of visiting urban parks for citizens is time sensitive, and varies depending on their travel mode, location, and traffic conditions [39,40].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Exploring Spatial Distribution of Urban Park Service Areas in Shanghai Based on Travel Time Estimation: A Method Combining Multi-Source Data

Hui

Yan

2021

IJGI

View full text Add to dashboard Cite

Due to a growing appreciation for the ecological and recreational benefits of public green spaces, the evaluation of urban parks’ service efficiency, as well as citizens’ behavioral preferences for daily recreation, have become an increasing academic focus. However, due to the lack of empirical approaches, existing research on exploring park service areas has been simplified by their use of Euclidean distance or buffer sets by simulation, ignoring the fact that the likelihood of citizens visiting urban parks is time sensitive. Utilizing mobile signaling data and web map services, this study proposes an approach to estimating the travel times of park visitors and analyzing the characteristics of park service areas from the perspective of actual time consumption. Taking Shanghai as a case study, this research firstly identified the time–cost decay of parks with different areas and locations. A comparison analysis was then used to examine the spatial relationship between park service areas and their accessibility defined by time consumption. The results show that (1) urban parks in Shanghai have larger mean service radii than existing planning guidelines, and park service areas were significantly influenced by park locations; (2) people have a great preference for urban parks whose travel times by public transit are under 40 min, and they have no desire to visit parks located within or outside the Middle Ring Road when the travel times reach 60 min and 75 min, respectively; (3) the shapes of park service areas are consistent with the high-accessibility districts defined by time thresholds, in spite of some differences caused by citizens’ choices. These findings provide an effective tool for evaluating the actual characteristics of park recreational services, along with direct implications for policymakers aiming to establish effective strategies for improving the accessibility and vitality of urban parks.

show abstract

Section: Spatial Relationship Between Park Service Area and Accessibility Based On Time Thresholdssupporting

confidence: 60%

Section: Introductionmentioning

confidence: 99%

Exploring Spatial Distribution of Urban Park Service Areas in Shanghai Based on Travel Time Estimation: A Method Combining Multi-Source Data

Hui

Yan

2021

IJGI

View full text Add to dashboard Cite

show abstract

“…Since the administrative boundaries of cities cannot accurately reflect the urban central areas, where human and socioeconomic activities are intensive, more scholars have recently used multi-source data fusion driven methods to study real urban systems [78][79][80][81][82][83]. In data driven research, urban central areas are identified and defined by multi-source data representing human activities and socioeconomic activities.…”

Section: Redefining Natural Citiesmentioning

confidence: 99%

Research on Large-Scale Urban Shrinkage and Expansion in the Yellow River Affected Area Using Night Light Data

Niu

Xia

Wang

et al. 2020

IJGI

Self Cite

View full text Add to dashboard Cite

As the land use issue, caused by urban shrinkage in China, is becoming more and more prominent, research on urban shrinkage and expansion has become particularly challenging and urgent. Based on the points of interest (POI) data, this paper redefines the scope, quantity, and area of natural cities by using threshold methods, which accurately identify the shrinkage and expansion of cities in the Yellow River affected area using night light data in 2013 and 2018. The results show that: (1) there are 3130 natural cities (48,118.75 km2) in the Yellow River affected area, including 604 shrinking cities (8407.50 km2) and 2165 expanding cities (32,972.75 km2). (2) The spatial distributions of shrinking and expanding cities are quite different. The shrinking cities are mainly located in the upper Yellow River affected area, except for the administrative cities of Lanzhou and Yinchuan; the expanding cities are mainly distributed in the middle and lower Yellow River affected area, and the administrative cities of Lanzhou and Yinchuan. (3) Shrinking and expanding cities are typically smaller cities. The research results provide a quick data supported approach for regional urban planning and land use management, for when regional and central governments formulate the outlines of urban development monitoring and regional planning.

show abstract

“…However, there are some shortcomings in these studies. Firstly, most previous studies have used vector data-based network analysis for road network modelling, but this approach is more demanding and varies significantly from the actual road network data (Li et al, 2021;Zheng et al, 2020). In addition, most previous studies have focused on urban or vulnerable groups, and few studies have focused on tourism enterprises (Shi et al, 2020).…”

Section: Spatial Accessibilitymentioning

confidence: 99%

The Emergency Accessibility Analysis based on Traffic Big Data and Flood Scenario Simulation in the context of Shanghai Hotel industry

Qian

Lin

Shi

et al. 2021

Preprint

View full text Add to dashboard Cite

Abstract. Underlying the impact of global warming and rapid growth of the tourism industry, the increasing frequency of flood post threats to the sustainable development of the coastal cities in China. The article proposes a methodological approach to evaluate the emergence response capability. This approach combines the flood simulation scenario method, traffic big data with the path navigation interface of the web. This article provides an empirical study to evaluate the emergency response from Fire & Rescue Service (FRS) to the tourist hotel in Shanghai from spatial accessibility perspective. The findings show that (1). The emergency response from FRS has significant relationships with the situation of transportation, the location of hotels, the intensity of flood inundation and the number, location of the urban FRS. (2). The emergency accessibility of a city caused by floods depends on the prevailing traffic conditions. The more severe traffic congestion has a significant impact on the spatial accessibility. (3) Flooding and real-time traffic conditions can change the fastest path from FRS to tourist hotels, resulting in delays in emergency response times, and the selection of the most appropriate travel routes is critical to improving the emergency response capability of cities. The results proved the validity of this proposed approach. Consequently, the approach contributes to the enhancement of the level of emergence response ability of urban tourism when they encounter disasters.

show abstract

Spatial equity of park green space using KD2SFCA and web map API: A case study of zhengzhou, China

Cited by 68 publications

References 48 publications

Exploring Spatial Distribution of Urban Park Service Areas in Shanghai Based on Travel Time Estimation: A Method Combining Multi-Source Data

Exploring Spatial Distribution of Urban Park Service Areas in Shanghai Based on Travel Time Estimation: A Method Combining Multi-Source Data

Research on Large-Scale Urban Shrinkage and Expansion in the Yellow River Affected Area Using Night Light Data

The Emergency Accessibility Analysis based on Traffic Big Data and Flood Scenario Simulation in the context of Shanghai Hotel industry

Contact Info

Product

Resources

About