Spatial Big Data and Moving Objects: A Comprehensive Survey <s> C. SBD GENERATED THROUGH DISASTER SITUATIONS <s> Abstract Geographic data and tools are essential in all aspects of emergency management: preparedness, response, recovery, and mitigation. Geographic information created by amateur citizens, often known as volunteered geographic information, has recently provided an interesting alternative to traditional authoritative information from mapping agencies and corporations, and several recent papers have provided the beginnings of a literature on the more fundamental issues raised by this new source. Data quality is a major concern, since volunteered information is asserted and carries none of the assurances that lead to trust in officially created data. During emergencies time is the essence, and the risks associated with volunteered information are often outweighed by the benefits of its use. An example is discussed using the four wildfires that impacted the Santa Barbara area in 2007–2009, and lessons are drawn. <s> BIB001 </s> Spatial Big Data and Moving Objects: A Comprehensive Survey <s> C. SBD GENERATED THROUGH DISASTER SITUATIONS <s> In recent months there has been an explosion of interest in using the Web to create, assemble, and disseminate geographic information provided voluntarily by individuals. Sites such as Wikimapia and OpenStreetMap are empowering citizens to create a global patchwork of geographic information, while Google Earth and other virtual globes are encouraging volunteers to develop interesting applications using their own data. I review this phenomenon, and examine associated issues: what drives people to do this, how accurate are the results, will they threaten individual privacy, and how can they augment more conventional sources? I compare this new phenomenon to more traditional citizen science and the role of the amateur in geographic observation. <s> BIB002 </s> Spatial Big Data and Moving Objects: A Comprehensive Survey <s> C. SBD GENERATED THROUGH DISASTER SITUATIONS <s> Engineering reconnaissance following an extreme event is critical in identifying the causes of infrastructure failure and minimizing such consequences in similar future events. Typically, however, much of the data about infrastructure performance and the progression of geological phenomena are lost during the event or soon after as efforts move to the recovery phase. A better methodology for reliable and rapid collection of perishable hazards data will enhance scientific inquiry and accelerate the building of disaster-resilient cities. In this paper, we explore ways to support post-event reconnaissance through the strategic collection and reuse of social media data and other remote sources of information, in response to the September 2013 flooding in Colorado. We show how tweets, particularly with postings of visual data and references to location, may be used to directly support geotechnical experts by helping to digitally survey the affected region and to navigate optimal paths through the physical space in preparation for direct observation. <s> BIB003 </s> Spatial Big Data and Moving Objects: A Comprehensive Survey <s> C. SBD GENERATED THROUGH DISASTER SITUATIONS <s> In recent years, social media emerged as a potential resource to improve the management of crisis situations such as disasters triggered by natural hazards. Although there is a growing research body concerned with the analysis of the usage of social media during disasters, most previous work has concentrated on using social media as a stand-alone information source, whereas its combination with other information sources holds a still underexplored potential. This article presents an approach to enhance the identification of relevant messages from social media that relies upon the relations between georeferenced social media messages as Volunteered Geographic Information and geographic features of flood phenomena as derived from authoritative data sensor data, hydrological data and digital elevation models. We apply this approach to examine the micro-blogging text messages of the Twitter platform tweets produced during the River Elbe Flood of June 2013 in Germany. This is performed by means of a statistical analysis aimed at identifying general spatial patterns in the occurrence of flood-related tweets that may be associated with proximity to and severity of flood events. The results show that messages near up to 10 km to severely flooded areas have a much higher probability of being related to floods. In this manner, we conclude that the geographic approach proposed here provides a reliable quantitative indicator of the usefulness of messages from social media by leveraging the existing knowledge about natural hazards such as floods, thus being valuable for disaster management in both crisis response and preventive monitoring. <s> BIB004 </s> Spatial Big Data and Moving Objects: A Comprehensive Survey <s> C. SBD GENERATED THROUGH DISASTER SITUATIONS <s> A new methodology is introduced that leverages data harvested from social media for tasking the collection of remote-sensing imagery during disasters or emergencies. The images are then fused with multiple sources of contributed data for the damage assessment of transportation infrastructure. The capability is valuable in situations where environmental hazards such as hurricanes or severe weather affect very large areas. During these types of disasters it is paramount to ‘cue’ the collection of remote-sensing images to assess the impact of fast-moving and potentially life-threatening events. The methodology consists of two steps. First, real-time data from Twitter are monitored to prioritize the collection of remote-sensing images for evolving disasters. Commercial satellites are then tasked to collect high-resolution images of these areas. Second, a damage assessment of transportation infrastructure is carried out by fusing the tasked images with contributed data harvested from social media such as Flickr and Twitter, and any additional available data. To demonstrate its feasibility, the proposed methodology is applied and tested on the 2013 Colorado floods with a special emphasis in Boulder County and the cities of Boulder and Longmont. <s> BIB005 </s> Spatial Big Data and Moving Objects: A Comprehensive Survey <s> C. SBD GENERATED THROUGH DISASTER SITUATIONS <s> Crowdsourcing is a process of acquisition, integration, and analysis of big and heterogeneous data generated by a diversity of sources in urban spaces, such as sensors, devices, vehicles, buildings, and human. Especially, nowadays, no countries, no communities, and no person are immune to urban emergency events. Detection about urban emergency events, e.g., fires, storms, traffic jams is of great importance to protect the security of humans. Recently, social media feeds are rapidly emerging as a novel platform for providing and dissemination of information that is often geographic. The content from social media usually includes references to urban emergency events occurring at, or affecting specific locations. In this paper, in order to detect and describe the real time urban emergency event, the 5W (What, Where, When, Who, and Why) model is proposed. Firstly, users of social media are set as the target of crowd sourcing. Secondly, the spatial and temporal information from the social media are extracted to detect the real time event. Thirdly, a GIS based annotation of the detected urban emergency event is shown. The proposed method is evaluated with extensive case studies based on real urban emergency events. The results show the accuracy and efficiency of the proposed method. <s> BIB006
Recent natural environmental disasters have drawn focus to the vulnerability of our society and infrastructure BIB001 . Novel information streams, such as social media-contributed videos, photographs, and text as well as other open sources are redefining situation awareness during emergencies BIB005 . Social media messages combined with geographic reference containing spatial and temporal information are termed as volunteered geographical information (VGI) BIB002 that unfolds the power of 'citizens as sensors' to provide realtime updated data. According to Dashti et al. BIB003 , VGI can be used in disaster response, timely damage assessment, and to efficiently carry out rescue and relief operations. Howe  argues that VGI is similar to crowdsourcing primarily because of two reasons. First, it makes use of the wisdom of crowd and can solve a problem more effectively than an expert, irrespective of the group's knowledge and expertise pertinent to the problem. Second, the information acquired via collaborative VGI is possibly closer to reality than the information obtained from a single source. A number of examples where volunteered data has been used to retrieve critical information have been cited in the recent literature, e.g. during hurricane Katrina BIB005 , fire emergency event in Shanghai BIB006 , River Elbe flood in Germany BIB004 and so forth.