Evaluation of spatial analysis application for urban emergency management

Abdalla, Rifaat

doi:10.1186/s40064-016-3723-y

Cited by 14 publications

(5 citation statements)

References 37 publications

(37 reference statements)

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“…These actions include to rescue and alert peoples, to maintain the supply chains (foods, medical supplies, and other resources), to provide medical services, and other necessary steps. Currently, GIS tools and solutions [1,39,40,217] are used by emergency teams around the world to analyze data collected from aerial drones, satellites, smartphones, social networks, and other sources to take immediate measures. Future geospatial analytics will be more advanced and accurate for emergency management and response due to the integration of AI and machine learning.…”

Section: Importance and Applications Of Spatio-temporal Data Analyticsmentioning

confidence: 99%

A Survey on Spatio-temporal Data Analytics Systems

2022

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Due to the surge of spatio-temporal data volume, the popularity of location-based services and applications, and the importance of extracted knowledge from spatio-temporal data to solve a wide range of real-world problems, a plethora of research and development work has been done in the area of spatial and spatio-temporal data analytics in the past decade. The main goal of existing works was to develop algorithms and technologies to capture, store, manage, analyze, and visualize spatial or spatio-temporal data. The researchers have contributed either by adding spatio-temporal support with existing systems, by developing a new system from scratch, or by implementing algorithms for processing spatio-temporal data. The existing ecosystem of spatial and spatio-temporal data analytics systems can be categorized into three groups, (1) spatial databases (SQL and NoSQL), (2) big spatial data processing infrastructures, and (3) programming languages and GIS software. Since existing surveys mostly investigated infrastructures for processing big spatial data, this survey has explored the whole ecosystem of spatial and spatio-temporal analytics. This survey also portrays the importance and future of spatial and spatio-temporal data analytics.

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Section: Importance and Applications Of Spatio-temporal Data Analyticsmentioning

confidence: 99%

A Survey on Spatio-temporal Data Analytics Systems

2022

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“…Deelesh Mandloi et al [30] present an object-oriented data model to represent a multi-modal, indoor/outdoor transportation network in an urban area; this model is particularly suitable for network analyses regarding route planning and navigation in the network. As reported by [6], [35], GIS applications in disaster management are progressively becoming very useful tools that help process the related emergency activities and reduce the extremely critical duration of emergency management operations.…”

Section: Related Workmentioning

confidence: 99%

“…• Constraints Check: Constraints verifications are performed when the agent selects a new node v and generates a reconstruction plan. Constraints functions validate input parameters ('Budget' and 'T e ') in every cycle 6 , which defines duration of the plan. If the conditions are satisfied, then the time and cost required to construct specific damage units are deducted from the total input parameters.…”

Section: Figure 3: Shapefiles Of the Considered Areamentioning

confidence: 99%

Toward Effective Response to Natural Disasters: A Data Science Approach

et al. 2021

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Natural disasters can cause widespread damage to buildings and infrastructures and kill thousands of living beings. These events are difficult to be overcome both by the populations and by government authorities. Two challenging issues require in particular to be addressed: find an effective way to evacuate people first, and later to rebuild houses and other infrastructures. An adequate recovery strategy to evacuate people and start reconstructing damaged areas on a priority basis can then be a game changer allowing to overcome effectively those terrible circumstances. In this perspective, we here present DiReCT, an approach based on i) a dynamic optimization model designed to timely formulate an evacuation plan of an area struck by an earthquake, and ii) a decision support system, based on double deep Q Network, able to guide efficiently the reconstruction the affected areas. The latter works by considering both the resources available and the needs of the various stakeholders involved (e.g., residents social benefits and political priorities). The ground on which both the above solutions stand was a dedicated geographical data extraction algorithm, called "GisToGraph", especially developed for this purpose. To check applicability of the whole approach, we dovetailed it on the real use-case of the historical city center of L'Aquila (Italy) using detailed GIS data and information on urban land structure and buildings vulnerability. Several simulations were run on the underlining network generated. First, we ran experiments to safely evacuate in the shortest possible time as many people as possible from an endangered area towards a set of safe places. Then, using DDQN, we generated different reconstruction plans and selected the best ones considering both social benefits and political priorities of the building units. The described approaches are comprised in a more general data science framework delved to produce an effective response to natural disasters.

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“…These actions include to rescue and alert peoples, to maintain the supply chains (foods, medical supplies, and other essential resources), to provide medical services, and other necessary steps. Currently, GIS tools and solutions [1,30,31,206] are used by emergency teams around the world to analyze data collected from aerial drones, satellites, smartphones, social networks, and other sources to take immediate measures. Future geospatial analytics will be more advanced and accurate for emergency management and response due to the integration of AI and machine learning.…”

Section: Importance and Applications Of Spatio-temporal Data Analyticsmentioning

confidence: 99%

“…1 Collections -MultiPoint, MultiLineString, MultiPolygon, GeometryCollection 2 Support functions compliant with OGC SFA-SQL[167] and ISO SQL/MM[219] standard…”

mentioning

confidence: 99%

A Survey on Spatio-temporal Data Analytics Systems

Alam¹,

Torgo²,

Bifet³

2021

Preprint

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Due to the surge of spatio-temporal data volume, the popularity of location-based services and applications, and the importance of extracted knowledge from spatio-temporal data to solve a wide range of real-world problems, a plethora of research and development work has been done in the area of spatial and spatiotemporal data analytics in the past decade. The main goal of existing works was to develop algorithms and technologies to capture, store, manage, analyze, and visualize spatial or spatio-temporal data. The researchers have contributed either by adding spatio-temporal support with existing systems, by developing a new system from scratch for processing spatio-temporal data, or by implementing algorithms for mining spatio-temporal data. The existing ecosystem of spatial and spatio-temporal data analytics can be categorized into three groups, (1) spatial databases (SQL and NoSQL), ( 2) big spatio-temporal data processing infrastructures, and (3) programming languages and software tools for processing spatio-temporal data. Since existing surveys mostly investigated big data infrastructures for processing spatial data, this survey has explored the whole ecosystem of spatial and spatio-temporal analytics along with an up-to-date review of big spatial data processing systems. This survey also portrays the importance and future of spatial and spatio-temporal data analytics. CCS Concepts: • General and reference → Surveys and overviews; • Information systems → Spatialtemporal systems; Parallel and distributed DBMSs.

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Evaluation of spatial analysis application for urban emergency management

Cited by 14 publications

References 37 publications

A Survey on Spatio-temporal Data Analytics Systems

A Survey on Spatio-temporal Data Analytics Systems

Toward Effective Response to Natural Disasters: A Data Science Approach

A Survey on Spatio-temporal Data Analytics Systems

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