Research on Assessment of Impact of Big Data Attributes to Disaster Response Decision-Making Process

Min, Geum Young; Jeong, Duke Hoon

doi:10.7838/jsebs.2013.18.3.017

Cited by 11 publications

(5 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The time series of hourly population estimates can be used to group blocks based on similar patterns and then delineate urban functional areas. Dynamic population estimation can also be beneficial to studies of natural disaster relief and impact assessments, in which population dynamics at high temporal resolution are usually not considered (Bengtsson et al, ; Min & Jeong, ; Wilson et al, ).…”

Section: Resultsmentioning

confidence: 99%

“…Mapping population dynamics is of great significance to transport and city planning (Becker et al, ; De Nadai et al, ; Tao, Corcoran, Mateo‐Babiano, & Rohde, ), public safety warning (Li, Xu, Ma, & Chung, ; Traag, Browet, Calabrese, & Morlot, ; Zhou, Pei, & Wu, ), disaster impact assessments (Bengtsson, Lu, Thorson, Garfield, & Von Schreeb, ; Min & Jeong, ; Wilson et al, ), and epidemic modeling (Faria et al, ; Lopez, Gunasekaran, Murugan, Kaur, & Abbas, ; Vespignani, ). But the acquisition of attribute and location data of human activities is still a challenge for mapping population dynamics at fine temporal resolution.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Mapping hourly dynamics of urban population using trajectories reconstructed from mobile phone records

Liu

et al. 2018

Transactions in GIS

View full text Add to dashboard Cite

Understanding the spatiotemporal dynamics of urban population is crucial for addressing a wide range of urban planning and management issues. Aggregated geospatial big data have been widely used to quantitatively estimate population distribution at fine spatial scales over a given time period. However, it is still a challenge to estimate population density at a fine temporal resolution over a large geographical space, mainly due to the temporal asynchrony of population movement and the challenges to acquiring a complete individual movement record. In this article, we propose a method to estimate hourly population density by examining the time-series individual trajectories, which were reconstructed from call detail records using BP neural networks. We first used BP neural networks to predict the positions of mobile phone users at an hourly interval and then estimated the hourly population density using log-linear regression at the cell tower level. The estimated population density is linearly correlated with population census data at the sub-district level. Trajectory clustering results show five distinct diurnal dynamic patterns of population movement in the study area, revealing spatially explicit characteristics of the diurnal commuting flows, though the driving forces of the flows need further investigation.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Mapping hourly dynamics of urban population using trajectories reconstructed from mobile phone records

Liu

et al. 2018

Transactions in GIS

View full text Add to dashboard Cite

show abstract

“…The dynamic nature of temporal and spatial population distributions has a profound impact on urban and transportation planning [1][2][3], safety for human crowds [4][5][6], disaster impact assessment [7][8][9], human activity-travel behaviour [10,11] and epidemiological modelling [12][13][14]. However, when studying human activity on a city-wide scale, estimating and mapping more detailed population distributions at higher than a 12h temporal resolution is still a challenge [15].…”

Section: Introductionmentioning

confidence: 99%

A Method for the Estimation of Finely-Grained Temporal Spatial Human Population Density Distributions Based on Cell Phone Call Detail Records

et al. 2020

View full text Add to dashboard Cite

Estimating and mapping population distributions dynamically at a city-wide spatial scale, including those covering suburban areas, has profound, practical, applications such as urban and transportation planning, public safety warning, disaster impact assessment and epidemiological modelling, which benefits governments, merchants and citizens. More recently, call detail record (CDR) of mobile phone data has been used to estimate human population distributions. However, there is a key challenge that the accuracy of such a method is difficult to validate because there is no ground truth data for the dynamic population density distribution in time scales such as hourly. In this study, we present a simple and accurate method to generate more finely grained temporal-spatial population density distributions based upon CDR data. We designed an experiment to test our method based upon the use of a deep convolutional generative adversarial network (DCGAN). In this experiment, the highest spatial resolution of every grid cell is 125125 square metre, while the temporal resolution can vary from minutes to hours with varying accuracy. To demonstrate our method, we present an application of how to map the estimated population density distribution dynamically for CDR big data from Beijing, choosing a half hour as the temporal resolution. Finally, in order to cross-check previous studies that claim the population distribution at nighttime (from 8 p.m. to 8 a.m. on the next day) mapped by Beijing census data are similar to the ground truth data, we estimated the baseline distribution, first, based upon records in CDRs. Second, we estimate a baseline distribution based upon Global Navigation Satellite System (GNSS) data. The results also show the Root Mean Square Error (RMSE) is about 5000 while the two baseline distributions mentioned above have an RMSE of over 13,500. Our estimation method provides a fast and simple process to map people’s actual density distributions at a more finely grained, i.e., hourly, temporal resolution.

show abstract

“…Mapping population dynamics is of great significance for city and transport planning [1,2,3], public safety warning [4,5,6], disaster impact assessments [7,8,9], and epidemic modeling [10,11,12]. The analysis of mobile phone data is frequently used to map the spatial and temporal situation of users [13,14].…”

Section: Introductionmentioning

confidence: 99%

Large-Scale, Fine-Grained, Spatial, and Temporal Analysis, and Prediction of Mobile Phone Users’ Distributions Based upon a Convolution Long Short-Term Model

Zhang

Rui

Poslad

et al. 2019

Sensors

View full text Add to dashboard Cite

Accurate and timely estimations of large-scale population distributions are a valuable input for social geography and economic research and for policy-making. The most popular large-scale method to calculate such estimations uses mobile phone data. We propose a novel method, firstly based upon using a kernel density estimation (KDE) to estimate dynamic mobile phone users’ distributions at a two-hourly scale temporal resolution. Secondly, a convolutional long short-term memory (ConvLSTM) model was used in our study to predict mobile phone users’ spatial and temporal distributions for the first time at such a fine-grained temporal resolution. The evaluation results show that the predicted people’s mobility derived from the mobile phone users’ density correlates much better with the actual density, both temporally and spatially, as compared to traditional methods such as time-series prediction, autoregressive moving average model (ARMA), and LSTM.

show abstract

Research on Assessment of Impact of Big Data Attributes to Disaster Response Decision-Making Process

Cited by 11 publications

References 41 publications

Mapping hourly dynamics of urban population using trajectories reconstructed from mobile phone records

Mapping hourly dynamics of urban population using trajectories reconstructed from mobile phone records

A Method for the Estimation of Finely-Grained Temporal Spatial Human Population Density Distributions Based on Cell Phone Call Detail Records

Large-Scale, Fine-Grained, Spatial, and Temporal Analysis, and Prediction of Mobile Phone Users’ Distributions Based upon a Convolution Long Short-Term Model

Contact Info

Product

Resources

About