Publications by authors named "Qifan Nie"

3 Publications

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Spatio-temporal evolution and influencing mechanism of the COVID-19 epidemic in Shandong province, China.

Sci Rep 2021 04 9;11(1):7811. Epub 2021 Apr 9.

College of Urban and Environmental Sciences, Northwest University, Xi'an, 710127, China.

The novel coronavirus pneumonia (COVID-19) outbreak that emerged in late 2019 has posed a severe threat to human health and social and economic development, and thus has become a major public health crisis affecting the world. The spread of COVID-19 in population and regions is a typical geographical process, which is worth discussing from the geographical perspective. This paper focuses on Shandong province, which has a high incidence, though the first Chinese confirmed case was reported from Hubei province. Based on the data of reported confirmed cases and the detailed information of cases collected manually, we used text analysis, mathematical statistics and spatial analysis to reveal the demographic characteristics of confirmed cases and the spatio-temporal evolution process of the epidemic, and to explore the comprehensive mechanism of epidemic evolution and prevention and control. The results show that: (1) the incidence rate of COVID-19 in Shandong is 0.76/100,000. The majority of confirmed cases are old and middle-aged people who are infected by the intra-province diffusion, followed by young and middle-aged people who are infected outside the province. (2) Up to February 5, the number of daily confirmed cases shows a trend of "rapid increase before slowing down", among which, the changes of age and gender are closely related to population migration, epidemic characteristics and intervention measures. (3) Affected by the regional economy and population, the spatial distribution of the confirmed cases is obviously unbalanced, with the cluster pattern of "high-low" and "low-high". (4) The evolution of the migration pattern, affected by the geographical location of Wuhan and Chinese traditional culture, is dominated by "cross-provincial" and "intra-provincial" direct flow, and generally shows the trend of "southwest → northeast". Finally, combined with the targeted countermeasures of "source-flow-sink", the comprehensive mechanism of COVID-19 epidemic evolution and prevention and control in Shandong is revealed. External and internal prevention and control measures are also figured out.
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http://dx.doi.org/10.1038/s41598-021-86188-0DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8035406PMC
April 2021

Bicyclist injury severity in traffic crashes: A spatial approach for geo-referenced crash data to uncover non-stationary correlates.

J Safety Res 2020 06 28;73:25-35. Epub 2020 Feb 28.

Virginia Department of Transportation, Richmond, VA 23219, United States. Electronic address:

Introduction: Bicyclists are among vulnerable road users with their safety a key concern. This study generates new knowledge about their safety by applying a spatial modeling approach to uncover non-stationary correlates of bicyclist injury severity in traffic crashes.

Method: The approach is Geographically Weighted Ordinal Logistic Regression (GWOLR), extended from the regular Ordered Logistic Regression (OLR) by incorporating the spatial perspective of traffic crashes. The GWOLR modeling approach allows the relationships between injury severity and its contributing factors to vary across the spatial domain, to account for the spatial heterogeneity. This approach makes use of geo-referenced data. This study explored more than 7,000 geo-referenced bicycle--motor-vehicle crashes in North Carolina.

Results: This study performed a series of non-stationarity tests to identify local relationships that vary substantially across the spatial domain. These local relationships are related to the bicyclist (bicyclist age, bicyclist behavior, bicyclist intoxication, bicycle direction, bicycle position), motorist (driver age, driver intoxication, driver behavior, vehicle speed, vehicle type) and traffic (traffic volume).

Conclusions: Results from the regular OLR are in general consistent with previous findings. For example, an increased bicyclist injury severity is associated with older bicyclists, bicyclist being intoxicated, and higher motor-vehicle speeds. Results from the GWOLR show local (rather than global) relationships between contributing factors and bicyclist injury severity. Practical Applications: Researchers and practitioners may use GWOLR to prioritize cycling safety countermeasures for specific regions. For example, GWOLR modeling estimates in the study highlighted the west part (from Charlotte to Asheville) of North Carolina for increased bicyclist injury severity due to the intoxication of road users including both bicyclists and drivers. Therefore, if a countermeasure is concerned with the road user intoxication, there may be a priority for the region from Charlotte to Asheville (relative to other areas in North Carolina).
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http://dx.doi.org/10.1016/j.jsr.2020.02.006DOI Listing
June 2020

Pedestrian injury severity in motor vehicle crashes: An integrated spatio-temporal modeling approach.

Accid Anal Prev 2019 Nov 24;132:105272. Epub 2019 Aug 24.

Department of Civil, Construction and Environmental Engineering, The University of Alabama, Tuscaloosa, AL 35487, United States; Alabama Transportation Institute, The University of Alabama, Tuscaloosa, AL 35487, United States. Electronic address:

Traffic crashes are outcomes of human activities interacting with the diverse cultural, socio-economic and geographic contexts, presenting a spatial and temporal nature. This study employs an integrated spatio-temporal modeling approach to untangle the crashed injury correlates that may vary across the space and time domain. Specifically, this study employs Geographically and Temporally Weighted Ordinal Logistic Regression (GTWOLR) to examine the correlates of pedestrian injury severity in motor vehicle crashes. The method leverages the space- and time-referenced crash data and powerful computational tools. This study performed non-stationarity tests to verify whether the local correlates of pedestrian injury severity have a significant spatio-temporal variation. Results showed that some variables passed the tests, indicating they have a significantly varying spatio-temporal relationship with the pedestrian injury severity. These factors include the pedestrian age, pedestrian position, crash location, motorist age and gender, driving under the influence (DUI), motor vehicle type and crash time in a day. The spatio-temporally varying correlates of pedestrian injury severity are valuable for researchers and practitioners to localize pedestrian safety improvement solutions in North Carolina. For example, in near future, special attention may be paid to DUI crashes in the city of Charlotte and Asheville, because in such areas DUI-involved crashes are even more likely to cause severe pedestrian injuries that in other areas. More implications are discussed in the paper.
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http://dx.doi.org/10.1016/j.aap.2019.105272DOI Listing
November 2019