Time 20 minutes Introduction Here is an excellent game for the first day of class. This game helps you to introduce yourself to the class and gives you insight into your students' level of English. Procedure Before you start the game, cover the board with information about yourself. Next to each piece of information write a number. The type and amount of information you write will depend on the level of your class. Example: Who am I?

Where am I

Emergency management (EM) has always been a concern of people from all walks of life due to the devastating impacts emergencies can have. The global outbreak of COVID-19 in 2020 has pushed EM to the top topic. As mobile phones have become ubiquitous, many scholars have shown interest in using mobile phone data for EM. This paper presents a systematic literature review about the use of mobile phone data for EM that includes 65 related articles written between 2014 and 2019 from six electronic databases. Five themes in using mobile phone data for EM emerged from the reviewed articles, and a systematic framework is proposed to illustrate the current state of the research. This paper also discusses EM under COVID-19 pandemic and five future implications of the proposed framework to guide future work.

/pdf/using-mobile-phone-data-for-emergency-management-a-4qul94l6uf.pdf

Using Mobile Phone Data for Emergency Management: A Systematic Literature Review

Traditional data collection methods such as remote sensing and field surveying often fail to offer timely information during or immediately following disaster events. Social sensing enables all cit...

https://www.tandfonline.com/doi/pdf/10.1080/17538947.2019.1670951

Introduction to social sensing and big data computing for disaster management

• Research in urban flood modelling has received a major attention in last 4 years. • The topics cover flow process understanding and innovation in numerical modelling. • Many new experimental set-ups and numerical models were developed. • Innovative calculations aim at improving prediction quality or efficiency. • Challenges remain for an efficient and precise urban flood calculations. This review provides a synthesis of advances in our understanding of urban flood processes and their modelling over the last four years (2018–2021). Four aspects are covered: knowledge of urban flood flow and transport processes, stability of humans and objects within flooded streets, reliability of computational modelling and approaches for speeding-up computations of urban flood event. New laboratory setups have shed light on previously unexplored processes such as flow intrusion into buildings or contaminant exchanges between surface and underground drainage. The stability of a single pedestrians or object (e.g., vehicles, waste containers) under urban flooding was analysed, but not group effects such as clogging. Improvements in computations were achieved by new strategies for merging and processing various sources of high quality topographic and forcing data (e.g., precipitation), the incorporation of more and more details on the drainage systems (e.g., effect of gullies), and 3D instead of 2D simulations. Computational efficiency was enhanced based on massive parallelization, adaptive mesh, porosity models, surrogate models as well as machine learning. Finally crowd-sourced data are shown to offer an avenue for next generation model validation methods. Remaining knowledge gaps and guidance for future research are proposed and predict that additional research work will be performed in following years. 

Hydraulic modelling of inland urban flooding: recent advances

The COVID-19 pandemic is changing the world in unprecedented and unpredictable ways. Human mobility, being the greatest facilitator for the spread of the virus, is at the epicenter of this change. In order to study mobility under COVID-19, to evaluate the efficiency of mobility restriction policies, and to facilitate a better response to future crisis, we need to understand all possible mobility data sources at our disposal. Our work studies private mobility sources, gathered from mobile-phones and released by large technological companies. These data are of special interest because, unlike most public sources, it is focused on individuals rather than on transportation means. Furthermore, the sample of society they cover is large and representative. On the other hand, these data are not directly accessible for anonymity reasons. Thus, properly interpreting its patterns demands caution. Aware of that, we explore the behavior and inter-relations of private sources of mobility data in the context of Spain. This country represents a good experimental setting due to both its large and fast pandemic peak and its implementation of a sustained, generalized lockdown. Our work illustrates how a direct and naive comparison between sources can be misleading, as certain days (e.g., Sundays) exhibit a directly adverse behavior. After understanding their particularities, we find them to be partially correlated and, what is more important, complementary under a proper interpretation. Finally, we confirm that mobile-data can be used to evaluate the efficiency of implemented policies, detect changes in mobility trends, and provide insights into what new normality means in Spain.

https://www.mdpi.com/2220-9964/10/2/73/pdf

Comparative analysis of geolocation information through mobile-devices under different COVID-19 mobility restriction patterns in Spain

Population distribution modelling can benefit many different domains, for example, transportation, urban planning, ecology or emergency management. Information about the location and number of people in an affected area is crucial for decision-makers during emergencies and crises. Mobile phone data represents relatively reliable and time accurate information on real-time population distribution, movement and behaviour. In this study, we evaluate the spatio-temporal distribution of population derived from phone data of the selected pilot area (City of Brno, Czech Republic). Analysis is based on the dataset describing the estimated human presence (EHP) with two values – visitors and transiting persons. The temporal change of data is first analysed and further processed using two methodological approaches. First, the dasymetric method is used where the building geometry and technical attributes served as a target layer. Second, the results of building level analysis are transformed into a regular grid zone of both visitors and the general EHP. Resulting spatio-temporal patterns are compared to the census data. Results demonstrate how the proposed building level dasymetric approach can improve the spatial granularity of EHP. Potential use of proposed methodology within selected emergency situations is further discussed.

Population distribution modelling at fine spatio-temporal scale based on mobile phone data

Urban flooding, as one of the most serious natural disasters, has caused considerable personal injury and property damage throughout the world. To better cope with the problem of waterlogging, the experts have developed many waterlogging models that can accurately simulate the process of pipe network drainage and water accumulation. The study of urban waterlogging involves many data types. These data come from the departments of hydrology, meteorology, planning, surveying, and mapping, etc. The incoordination of space–time scale and format standard has brought huge obstacles to the study of urban waterlogging. This is not conducive to interpretation, transmission, and visualization in today’s network environment. In this paper, the entities and attributes related to waterlogging are defined. Based on the five modules of urban drainage network, sub basin, dynamic water body, time series, and meteorological data, the corresponding UML (Unified Modeling Language) model is designed and constructed. On this basis, the urban waterlogging application domain extension model city waterlogging application domain extension (CTWLADE) is established. According to the characteristics of different types of data, two different methods based on FME object and citygml4j are proposed to realize the corresponding data integration, and KML (Keyhole Markup Language) /glTF data organization form and the corresponding sharing method are proposed to solve the problem that the CTWLADE model data cannot be visualized directly on the web and cannot interact in three-dimensional format. To evaluate the CTWLADE, a prototype system was implemented, which can convert waterlogging-related multi-source data in extensible markup language (XML) files conform. The current CTWLADE can map the data required and provided by the hydraulic software tool storm water management model (SWMM) and is ready to be integrated into a Web 3D Service to provide the data for 3D dynamic visualization in interactive scenes.

https://www.mdpi.com/2220-9964/9/6/359/pdf

Constructing the CityGML ADE for the Multi-Source Data Integration of Urban Flooding

As an important part of spatial data, the point feature has always been an essential element in web maps and navigation maps. With the development of location-based services and the rapid increase in volunteered geographic information and social media data, the amount of point data is increasing day by day, resulting in inevitable problems of overlay and congestion during visualization. Map generalization provides multiple algorithms that can be used to select, aggregate and make typification of points or point clusters. For the generalization of point data, however, the traditional stand-alone computing environment has difficulty with real-time realization. Currently, the rapid development of cloud computing technology provides a powerful support for improving the efficiency of map generalization. However, compared with the stand-alone environment, the data decomposition and the real-time display of point generalization in the cloud platform imposes higher requirements on the point generalization constraints, which play an important role in point-generalized process control. Based on the computational characteristics of the cloud platform, this paper analyzes the changes in point generalization constraints. In addition, our work proposes the constraints of point generalization based on the cloud platform and its construction method, builds a prototype system based on the Hadoop cloud platform. Our prototype system is tested using typical experimental data. Its efficiency and the quality of its results is examined. The results show that the efficiency and quality of point selection can be significantly improved by controlling the point generalization process with the generalization constraints in the cloud computing environment proposed in this paper. This paper provides a possible way for the realization of map generalization in the cloud computing environment. Its usability with real data and with many users accessing it will be the focus of further research.

https://www.mdpi.com/2220-9964/7/7/235/pdf

Method of Constructing Point Generalization Constraints Based on the Cloud Platform

Navigation in a complex indoor environment can be difficult, and pedestrians may find themselves disoriented. As the featured objects of an environment, indoor landmarks play an important role in navigation. A review of the existing literature in outdoor landmark evaluation methods, however, shows that they cannot be fully applicable in any indoor environment. In this paper, an instance-based scoring system is proposed for analyzing the indicators that influence the salience of spatial objects from visual, semantic and structural aspects. An Analytic Hierarchy Process was applied to calculate landmark weights using these indicators. Two types of indoor scenes were employed as instances to verify the validity of this method, the Dongchenghui shopping mall (Nanjing, China) using a subjective questionnaire and interview method, and the headquarters of Masaryk University (Brno, Czechia) using an objective eyetracking method. Be result of the two instances showed that the proposed method was feasible.

/pdf/an-instance-based-scoring-system-for-indoor-landmark-22ow0is8cg.pdf

An instance-based scoring system for indoor landmark salience evaluation

The volunteered geographic information(VGI) becomes more and more popolar with the general public and scientific community. In this background, OpenStreetMap (OSM) has developed rapidly as one of the most popular VGI projects. As a kind of important elements on the web map, Points of Interest (POIs) play an important role in the navigation and other fields. However, the data quality of POI in OSM can vary strongly.In order to find out the spatial heterogeneity of the POI quality in OSM, in this paper,we analyse the data quality of POIs in OSM from three aspects: positional accuracy, data completeness and topological consistency at first. And then we explore the relationship between the data quality of POIs in OSM and the local characteristics based on geographically weighted regression(GWR), and to find out the main influence factors of POIs quality in OSM. The results show that the distribution of data contributors plays an important influence on the data quality of POIs in OSM. Besides, from the data completeness of POIs, it can be find that the OSM is still new thing in China and it may be need more contributors to enrich the OSM data in China.

Jie Shen

Papers

Population distribution modelling at fine spatio-temporal scale based on mobile phone data

Constructing the CityGML ADE for the Multi-Source Data Integration of Urban Flooding

Method of Constructing Point Generalization Constraints Based on the Cloud Platform

An instance-based scoring system for indoor landmark salience evaluation

Study on the spatial heterogeneity of the POI quality in OpenStreetMap