scholarly journals Taking one step further – Advancing the measurement of green and blue area accessibility using spatial network analysis

2021 ◽  
Vol 126 ◽  
pp. 107665
Author(s):  
Manuel Wolff
Keyword(s):  
Network Analysis ◽  
Spatial Network ◽  
One Step ◽  
Spatial Network Analysis

scholarly journals A Hungarian and Ukrainian Competitors’ Network: A Spatial Network Analysis Perspective

Urban Science ◽  
2018 ◽  
Vol 2 (4) ◽  
pp. 99 ◽  
Author(s):  
György Jóna
Keyword(s):  
Network Analysis ◽  
Urban Economics ◽  
Small World ◽  
The Other ◽  
Spatial Density ◽  
Spatial Network ◽  
Other Hand ◽  
New Findings ◽  
New Type ◽  
Spatial Network Analysis

In this paper, the spatial dimensions of a transboundary, coopetitive (coopetition: cooperation of rivals) network, established by restaurant owners, are scrutinized empirically by applying advanced toolkits of spatial network analysis (SpNA). The paper emphasizes that the coopetitive network has geographical extensions, and on the other hand, interactions between vertices generate network space. The new type of economic network could thus be analyzed by SpNA to understand the spatial characteristics of a rivals’ network at transboundary level. The paper may be referred to as cutting-edge research, because on one hand, it dissects a new type of economic network (coopetitive networks) and on the other hand, a new method is utilized (SpNA) to study the geographical parameters of inter-firm relationships. This approach emerges as a novel method. As a result, the paper provides significant, fruitful and new findings in both network science and urban economics as well. By employing metrics of SpNA, the main spatial traits of the coopetitive network can be mapped, such as the circumference, spatial structure, diameter, spatial density, spatial small world phenomenon, and global connectivity of the network. The results show that the coopetitive network possesses hub-and spoke spatial framework, in which the hub is localized far from the cluster of players. Moreover, the coopetitive interaction does not require face-to-face nexus, because the focal firm communicates with them via IT devices. The coopetitive activities contribute significantly to the urban economic growth. The main agent (the hub) ought to be supported by the regional development policy at the local and inter-urban geographical scale as well.

scholarly journals sDNA: 3-d spatial network analysis for GIS, CAD, Command Line & Python

SoftwareX ◽  
2020 ◽  
Vol 12 ◽  
pp. 100525
Author(s):  
Crispin H.V. Cooper ◽  
Alain J.F. Chiaradia
Keyword(s):  
Network Analysis ◽  
Command Line ◽  
Spatial Network ◽  
Spatial Network Analysis

Detecting Foodborne Disease Outbreaks in Florida through Consumer Complaints

2020 ◽  
Vol 83 (11) ◽  
pp. 1877-1888
Author(s):  
XIAOLONG LI ◽  
AMANDA C. SAPP ◽  
NITYA SINGH ◽  
LAURA MATTHIAS ◽  
CHAD BAILEY ◽  
...  
Keyword(s):  
Network Analysis ◽  
Vibrio Vulnificus ◽  
Seasonal Pattern ◽  
Large Population ◽  
Disease Outbreaks ◽  
Incidence Rates ◽  
South Florida ◽  
Spatial Network ◽  
Foodborne Disease ◽  
Spatial Network Analysis

ABSTRACT The Florida Complaint and Outbreak Reporting System (FL-CORS) database is used by the Florida Department of Health's Food and Waterborne Disease Program as one of the tools to detect foodborne disease outbreaks (FBOs). We present a descriptive and spatial network analysis of FL-CORS data collected during 2015 to 2018. We also quantified FBOs that were investigated and confirmed because of a filed complaint and the etiological agents involved in these outbreaks. An increasing number of unique complaints filed in FL-CORS was observed during 2015 to 2018, with a sharp increase during 2017 to 2018 and a different seasonal pattern in 2018. The preferred mechanism of reporting varied by age group, with younger people more frequently filing complaints online and older people preferring reporting in person or by phone. Spatial network analysis revealed that 87% of complaints had the same county of residence and county of presumed exposure. Frequency of complaints was negatively associated with linear distance between place of residence and place of exposure at the zip code level. Counties located in North and Central Florida, as well as some coastal areas in South Florida, had higher incidence rates of complaints. Those counties tend to have a large population density, and some are popular vacation destinations. On average, 96 FBOs were reported in Florida annually, of which 60% were confirmed with successful identification of the causative agent. The 56% of the confirmed FBOs were triggered by a complaint. Throughout the years, 2.4 to 2.8 FBOs and 1.4 confirmed FBOs were identified per 100 complaints. Ciguatera toxin was the cause of 40% of all FBOs in Florida, and only 28% of outbreaks were detected through complaints. In contrast, complaints were the main source of identifying outbreaks of norovirus, nontyphoidal Salmonella enterica, and scombroid food poisoning, as well as rare outbreaks of Clostridium perfringens, Cryptosporidium spp., Shigella spp., and Vibrio vulnificus. HIGHLIGHTS

Spatial Network Analysis

2019 ◽  
pp. 1-24
Author(s):  
Clio Andris ◽  
David O’Sullivan
Keyword(s):  
Network Analysis ◽  
Spatial Network ◽  
Spatial Network Analysis

scholarly journals Using road class as a replacement for predicted motorized traffic flow in spatial network models of cycling

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Eric Yin Cheung Chan ◽  
Crispin H. V. Cooper
Keyword(s):  
Network Analysis ◽  
Traffic Flow ◽  
Motor Vehicle ◽  
Network Models ◽  
Spatial Network ◽  
Step Model ◽  
Mode Share ◽  
Negative Impacts ◽  
Spatial Network Analysis ◽  
Modelling Effort

AbstractRecent years have seen renewed policy interest in urban cycling due to the negative impacts of motorized traffic, obesity and emissions. Simulating bicycle mode share and flows can help decide where to build new infrastructure for maximum impact, though modelling budgets are limited. The four step model used for vehicles is not typically used for this task as, aside from the expense of use, it is designed around too-large zone sizes and a simplified network. Alternative approaches are based on aggregate statistics or spatial network analysis, the latter being necessary to create a model sufficiently sensitive to infrastructure location, although still requiring considerable modelling effort due to the need to simulate motor vehicle flows in order to account for the effect of motorized traffic in disincentivising cycling. The model presented uses an existing spatial network analysis methodology on an unsimplified network, but simplifies the analysis by substituting explicit prediction of motorized traffic flow with an alternative based on road classification. The method offers a large reduction in modelling effort, but nonetheless gives model correlation with actual cycling flows (R2 = 0.85) broadly comparable to a previous model with motorized traffic fully simulated (R2 = 0.78).

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