Improving disaster management and recovery techniques is one of national priorities given the huge toll caused by man-made and nature calamities. Data-driven disaster management aims at applying advanced data collection and analysis technologies to achieve more effective and responsive disaster management, and has undergone considerable progress in the last decade. However, to the best of our knowledge, there is currently no work that both summarizes recent progress and suggests future directions for this emerging research area. To remedy this situation, we provide a systematic treatment of the recent developments in data-driven disaster management. Specifically, we first present a general overview of the requirements and system architectures of disaster management systems and then summarize state-of-the-art data-driven techniques that have been applied on improving situation awareness as well as in addressing users’ information needs in disaster management. We also discuss and categorize general data-mining and machine-learning techniques in disaster management. Finally, we recommend several research directions for further investigations.

https://dl.acm.org/doi/pdf/10.1145/3017678

Data-Driven Techniques in Disaster Information Management

Predicting when and where crimes are likely to occur is crucial for prioritizing police resources. Prior victimization is an excellent predictor of risk. Repeat victimization, when it occurs, tends to occur swiftly after an initial incident. The predictive power of prior victimization is greater than that of other analysed variables (see Budd 1999). Self-evidently, prior victimization yields no prediction about properties as yet unvictimized. This article, using data about the crime of domestic burglary, contends that research should seek to realize the predictive potential to be gained from both pre-and post-victimization factors. One of the advantages of crime reduction through the prevention of repeats is that it offers a constant (and, it is hoped, declining) rate of events that trigger preventive action, and hence a natural pace for preventive work. In that spirit, postvictimization prevention should, as well as targeting the victimized home, also protect other properties that are similar with respect to the dimensions used by burglars in target selection. The central purpose of the research here reported is to identify the ways in which it is prudent to allocate crime reduction resources in the wake of an offence and across time and location relative to the burgled home. We analysed police-recorded crime burglary data for the county of Merseyside. Using statistical techniques developed to study the transmission of disease, we first confirmed that burglaries do cluster in space and time. The operational payoff of this result is that a residential burglary flags the elevated risk of further residential burglaries in the near future (1-2 months) and in close proximity (up to 300-400 metres) to the victimized home. Put simply, the burglary event should trigger preventive action that is not restricted to the burgled home. The data enable prospective burglary hot-spotting.

The Burglary as Clue to the Future: The Beginnings of Prospective Hot-Spotting

On the Relation between Education and Crime

A range of applications, from predicting the spread of human and electronic viruses to city planning and resource management in mobile communications, depend on our ability to foresee the whereabouts and mobility of individuals, raising a fundamental question: To what degree is human behavior predictable? Here we explore the limits of predictability in human dynamics by studying the mobility patterns of anonymized mobile phone users. By measuring the entropy of each individual's trajectory, we find a 93% potential predictability in user mobility across the whole user base. Despite the significant differences in the travel patterns, we find a remarkable lack of variability in predictability, which is largely independent of the distance users cover on a regular basis.

http://absimage.aps.org/image/MAR10/MWS_MAR10-2009-007155.pdf

Limits of predictability in human mobility

The COVID-19 pandemic poses unprecedented challenges around the world. Many studies have applied mobility data to explore spatiotemporal trends over time, investigate associations with other variables, and predict or simulate the spread of COVID-19. Our objective was to provide a comprehensive overview of human mobility open data to guide researchers and policymakers in conducting data-driven evaluations and decision-making for the COVID-19 pandemic and other infectious disease outbreaks. We summarized the mobility data usage in COVID-19 studies by reviewing recent publications on COVID-19 and human mobility from a data-oriented perspective. We identified three major sources of mobility data: public transit systems, mobile operators, and mobile phone applications. Four approaches have been commonly used to estimate human mobility: public transit-based flow, social activity patterns, index-based mobility data, and social media-derived mobility data. We compared mobility datasets' characteristics by assessing data privacy, quality, space-time coverage, high-performance data storage and processing, and accessibility. We also present challenges and future directions of using mobility data. This review makes a pivotal contribution to understanding the use of and access to human mobility data in the COVID-19 pandemic and future disease outbreaks.

Human Mobility Data in the COVID-19 Pandemic: Characteristics, Applications, and Challenges

Sentiment affects every aspect of people's lives and has strong impact on their mental health. This paper explores local users' sentiments extracted from Geo-tweets data from January to December 2016, analyzed in the spatial and temporal perspective. Because of large amount of noisy data and complicated procedure of extracting local user, a workflow is created, facilitating more researchers to reproduce, replicate or extend the procedures using similar Geo-tweet dataset. The workflow is sharing at Harvard Dataverse (https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/6N9VUF). Using the processed data, each tweet's sentiment is classified according to the content. Then, the overall temporal variations of total number of positive, neural, and negative sentiments are analyzed on a monthly, daily and hourly level. From a spatial perspective, the Local Indicators of Spatial Association (LISA) statistical method is employed to discover the spatial clusters. In order to explore the content of positive sentiments, this paper applies the Latent Dirichlet Allocation (LDA) model to classify the Geo-tweets with positive sentiments into different topics. Combining the geospatial information with the topics, some patterns are found which demonstrate the associations between the nature of Twitter content and the characteristics of places and users. For example, weekend events and friend and family gatherings are the time that users prefer to post positive tweets. In the western part of US, users tend to post more photos to share the great moment on Twitter than other parts of the US.

/pdf/a-systematic-spatial-and-temporal-sentiment-analysis-on-geo-2usd28smr4.pdf

A Systematic Spatial and Temporal Sentiment Analysis on Geo-Tweets

Spatio-temporal Bayesian modeling, a method based on regional statistics, is widely used in epidemiological studies. Using Bayesian theory, this study builds a spatio-temporal Bayesian model specific to urban crime to analyze its spatio-temporal patterns and determine any developing trends. The associated covariates and their changes are also analyzed. The model is then used to analyze data regarding burglaries that occurred in Wuhan City in China from January to August 2013. Of the diverse socio-economic variables associated with crime rate, including population, the number of local internet bars, hotels, shopping centers, unemployment rate, and residential zones, this study finds that the burglary crime rate is significantly correlated with the average resident population per community and number of local internet bars. This finding provides a scientific reference for urban safety protection.

/pdf/urban-crime-prediction-based-on-spatio-temporal-bayesian-4tfxv5dven.pdf

Urban crime prediction based on spatio-temporal Bayesian model.

Depth sensor is widely used today and has great impact in object pose estimation, camera tracking, human actions, and scene reconstruction This paper presents a novel method for human interaction recognition based on 3D skeleton data captured by Kinect sensor using hierarchical spatial-temporal saliency-based representation method Hierarchical saliency can be conceptualized as Salient Actions at the highest level, determined by the initial movement in an interaction; Salient Points at middle level, determined by a single time point uniquely identified for all instances of Salient Action; Salient Joints at the lowest level, determined by the greatest positional changes of human joints in a Salient Action sequence Given the interaction saliency at different levels, several types of features, such as spatial displacement, direction relations, and etc, are introduced based on action characteristics Since there are few publicly accessible test datasets, we created a new dataset with eight types of interactions named K3HI, using the Microsoft Kinect The method was tested based on Support Vector Machine (SVM) multi-class classifier In the experiment, the results demonstrate that the average recognition accuracy of hierarchical saliency-based representation is 9029%, outperforming methods using other features

Human interaction recognition using spatial-temporal salient feature

The prediction of suspect location enables proactive experiences for crime investigations and offers essential intelligence for crime prevention. However, existing studies have failed to capture the complex social location transition patterns of suspects and lack the capacity to address the issue of data sparsity. This paper proposes a novel location prediction model called CMoB (Crime Multi-order Bayes model) based on the spatiotemporal semantics to enhance the prediction performance. In particular, the model groups suspects with similar spatiotemporal semantics as one target suspect. Then, their mobility data are applied to estimate Markov transition probabilities of unobserved locations based on a KDE (kernel density estimating) smoothing method. Finally, by integrating the total transition probabilities, which are derived from the multi-order property of the Markov transition matrix, into a Bayesian-based formula, it is able to realize multi-step location prediction for the individual suspect. Experiments with the mobility dataset covering 210 suspects and their 18,754 location records from January to June 2012 in Wuhan City show that the proposed CMoB model significantly outperforms state-of-the-art algorithms for suspect location prediction in the context of data sparsity.

Prediction of Suspect Location Based on Spatiotemporal Semantics

In the big data era, robust solutions are obliged to be proposed to integrate and represent data from different formats and with different contents to assist the decision-making. Current ca...

https://www.tandfonline.com/doi/pdf/10.1080/10095020.2019.1649192

Lian Duan

Papers

A Systematic Spatial and Temporal Sentiment Analysis on Geo-Tweets

Urban crime prediction based on spatio-temporal Bayesian model.

Human interaction recognition using spatial-temporal salient feature

Prediction of Suspect Location Based on Spatiotemporal Semantics

Development and implementation of a dynamic and 4D GIS based on semantic location model