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Smart city article details
| Title | A Territorial Intelligence-Based Approach For Smart Emergency Planning |
|---|---|
| ID_Doc | 5551 |
| Authors | Sebillo M.; Vitiello G.; Grimaldi M.; Chiara D.D. |
| Year | 2021 |
| Published | Data Science and Big Data Analytics in Smart Environments |
| DOI | http://dx.doi.org/10.1201/9780367814397-7 |
| Abstract | Geospatial data has been always “big data” [315]. It is estimated that nowadays “80% of data is geographic” [250] and much of the data in the world can be georeferenced. Through the use of advanced big data sources, such as Global Positioning Systems (GPS), smartphones, Internet of Things (IoT) [138] and Web 2.0 [154], Copernicus program [76], massive volumes of this data are collected that contain implicit spatial and temporal components, and semantic interrelations among them, which should be uncovered and made explicit to benefit from them. More- over, numerous applications, ranging from smart cities to autonomous driving, from indoor/outdoor navigation to digital agriculture and social activism, are investigated to create knowledge from such volumes of data by appropriate algorithms, some of them still in infancy [391],[166],[253]. This goal, namely translating data into insight, represents the preliminary phase of the challenge that researchers and analysts have set to obtain value from territorial knowledge. Indeed, this challenge has become even more difficult when researchers became aware that advances in positioning and navigation technologies have facilitated an unprecedented growth in the collection of spatially and temporally referenced data, thus increasing its complexity in terms of volume, variety and dynamics. Moreover, a concern of the recent advances made in this context is the watertight approach adopted. An example is given by the Spatial Decision Support Systems (SDSS) [394], which are typically finalized to face a specific issue by integrating features from a given theme, thus keeping the various research domains isolated and making the intersection among them difficult to be exploited. Spatial Data Science and Territorial Intelligence may be a solution. © 2021 Taylor & Francis Group, LLC. |
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