Flood Area Estimation Using Personal Location Data - Case Study of Japan Floods in 2018

2019 ◽  
Author(s):  
Kei Hiroi ◽  
Takahiro Yoshida ◽  
Yoshiki Yamagata ◽  
Nobuo Kawaguchi
Keyword(s):  
Area Estimation ◽  
Location Data

scholarly journals Job Accessibility from a Multiple Commuting Circles Perspective Using Baidu Location Data: A Case Study of Wuhan, China

2019 ◽  
Vol 11 (23) ◽  
pp. 6696 ◽  
Author(s):  
Cai ◽  
Liu ◽  
Luo ◽  
Xing ◽  
Liu
Keyword(s):  
Urban Areas ◽  
Short Distance ◽  
Long Distance ◽  
Job Accessibility ◽  
Location Data ◽  
Job Opportunities ◽  
Transit Services ◽  
Service Jobs ◽  
Wuhan Metropolitan Area

Jobs–housing imbalance is a hot topic in urban study and has obtained many results. However, little research has overcome the limits of administrative boundaries in job accessibility measurement and considered differences in job accessibility within multiple commuting circles. Using Baidu location data, this research proposes a new method to measure job accessibility within multiple commuting circles at the grids’ level. Taking the Wuhan metropolitan area as a case study, the results are as follows: (1) Housing and service jobs are concentrated in the central urban areas along the Yangtze River, whereas industrial jobs are scattered throughout suburbs with double centers. The potential competition for job opportunities is fiercer in the city center than in the suburbs. (2) Job accessibility with different levels shows significant circle-like distribution. People with long- or short-distance potential commutes demand to live close to the groups with the same demand. Residents with long-distance commutes demand to live outside of where those with short-distance commutes demand to reside, regardless of whether their commuting demand is for service or industrial jobs. (3) There are three optimization patterns for transit services to increase job accessibility in various areas. These patterns involve areas with inadequate job opportunities, poor transit services to service jobs, and poor transit services to industrial jobs. Developing current transit facilities or new transit alternatives as well as adding extra jobs near housing could improve jobs–housing imbalance in these areas. Findings from this study could guide the allocation of jobs and housing as well as the development of transport to reduce residents’ commuting burdens and promote transportation equity. The method used in this study can be applied to evaluate jobs–housing imbalance from the perspective of the supply in other metropolises.

scholarly journals Urban anomalies in response to rainstorms based on smartphone location data: a case study of eight cities in China

2019 ◽  
Author(s):  
Jiawei Yi ◽  
Yunyan Du ◽  
Fuyuan Liang ◽  
Tao Pei ◽  
Ting Ma ◽  
...  
Keyword(s):  
Time Series ◽  
Rainfall Intensity ◽  
Mainland China ◽  
Food Delivery ◽  
Location Based Services ◽  
Location Data ◽  
City Population ◽  
The City ◽  
Method Analysis

Abstract. This study explored city residents’ collective geo-tagged behaviors in response to rainstorms using the number of location request (NLR) data generated by smartphone users. We examined the rainstorms, flooding, NLR anomalies, as well as the associations among them in eight selected cities across the mainland China. The time series NLR clearly reflects cities’ general diurnal rhythm and the total NLR is moderately correlated with the total city population. Anomalies of NLR were identified at both the city and grid scale using the S-H-ESD method. Analysis results manifested that the NLR anomalies at the city and grid levels are well associated with rainstorms, indicating city residents request more location-based services (e.g. map navigation, car hailing, food delivery, etc.) when there is a rainstorm. However, sensitivity of the city residents’ collective geo-tagged behaviors in response to rainstorms varies in different cities as shown by different peak rainfall intensity thresholds. Significant high peak rainfall intensity tends to trigger city flooding, which lead to increased location-based requests as shown by positive anomalies on the time series NLR.

scholarly journals Case Study: Using Facebook Data to Monitor Adherence to Stay-at-home Orders in Colorado and Utah

2020 ◽  
Author(s):  
Ryan M. Layer ◽  
Bailey Fosdick ◽  
Michael Bradshaw ◽  
Daniel B. Larremore ◽  
Paul Doherty
Keyword(s):  
Limited Resources ◽  
Location Data ◽  
Mobility Data ◽  
Health Measure ◽  
The Us ◽  
User Data ◽  
Interactive Visualizations ◽  
Local Officials ◽  
At Home

ABSTRACTIn the absence of effective treatments or a vaccine, social distancing has been the only public health measure available to combat the COVID-19 pandemic to date. In the US, implementing this response has been left to state, county, and city officials, and many localities have issued some form of a stay-at-home order. Without existing tools and with limited resources, localities struggled to understand how their orders changed behavior. In response, several technology companies opened access to their users’ location data. As part of the COVID-19 Data Mobility Data Network [2], we obtained access to Facebook User data and developed four key metrics and visualizations to monitor various aspects of adherence to stay at home orders. These metrics were carefully incorporated into static and interactive visualizations for dissemination to local officials.All code is open source and freely available at https://github.com/ryanlayer/COvid19

scholarly journals An Analysis of Factors Influencing Disaster Mobility Using Location Data from Smartphones: Case Study of Western Japan Flooding

2019 ◽  
Vol 14 (6) ◽  
pp. 903-911 ◽  
Author(s):  
Soohyun Joo ◽  
Takehiro Kashiyama ◽  
Yoshihide Sekimoto ◽  
Toshikazu Seto ◽  
◽  
...  
Keyword(s):  
Cognitive Dissonance ◽  
Heavy Rain ◽  
Abnormal Behavior ◽  
Alert System ◽  
Location Data ◽  
Significant Damage ◽  
Western Japan ◽  
Irregular Behavior ◽  
Evacuation Behaviors

Western Japan was hit by heavy rain from June 8 to July 28, 2018. Record-breaking rain caused nearly all rivers to flood in Hiroshima and other areas. Over 200 people died following this disaster. Authorities attempted to understand why evacuation was not conducted swiftly enough to stop these deaths. They mentioned that normalcy bias and cognitive dissonance are two primary causes of significant damage [1]. Moreover, an effective alert system is necessary to ensure that evacuation behaviors and procedures are incited at the appropriate time. To understand the factors that influence people’s behavior, we estimated the probability of irregular behavior by unit changes in external condition. We chose 500 m mesh as a unit of analysis to consider individual singularity and classified 3 classes of mesh to identify abnormal behavior. We verified that as the number of residents in each mesh increases, the likelihood of a person in that region to exhibit normalcy bias increases as well. Owing to data, the accuracy of this method is somewhat low. However, several implications may still be drawn from our results, such as the demand for an adequate alert system. Using the results of people’s mobility and disaster risk information, approaches to dangerous situations such as the examined case may be improved in the future.

scholarly journals Discovering User’s Trends and Routines from Location Based Social Networks

Proceedings ◽  
2018 ◽  
Vol 2 (19) ◽  
pp. 1222
Author(s):  
Sergio Salomón ◽  
Rafael Duque ◽  
José Montaña
Keyword(s):  
Dynamic Bayesian Network ◽  
User Preferences ◽  
Bayesian Network Model ◽  
Real World Data ◽  
Location Data ◽  
User Context ◽  
Location Based Social Network ◽  
Location Based Social Networks ◽  
Source Of Information

Location data is a powerful source of information to discover user’s trends and routines. A suitable identification of the user context can be exploited to provide automatically services adapted to the user preferences. In this paper, we define a Dynamic Bayesian Network model and propose a method that processes location annotated data in order to train the model. Finally, our model enables us to predict future location contexts from the user patterns. A case study evaluates the proposal using real-world data of a location-based social network.

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