Transforming Smart Cities with Spatial Computing

Xie, Yiqun; Gupta, Jayant; Li, Yan; Shekhar, Shashi

doi:10.1109/isc2.2018.8656800

Cited by 24 publications

(11 citation statements)

References 59 publications

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“…The optimum value in this context is data on natural hazard. The calculated hazard value is passed to the second module and processed into Geo-ICT value (Xie et al, 2018;He et al, 2018;Souza et al, 2019). The proposed method is implemented in Windows 7 professional environment.…”

Section: Data Pre-processmentioning

confidence: 99%

Managing natural hazards in smart cities in Kingdom of Saudi Arabia using a technique based on interior search algorithm

Dutta

2020

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A smart city is a modern habitat that uses internet of things (IOT) to collect data and manage resources to provide high level of services to people. Information and communication technology (ICT) is a technique to enhance the quality of a smart city. Semantic web and cloud servers are the resources to provide data for smart city management tools. Riyadh is one of the smart cities in Kingdom of Saudi Arabia. Dust storm and floods are the most common hazards in the city. Existing smart city management could not provide an effective solution for the natural hazards. There is a necessity for smart city applications to optimise data and provide an optimum accuracy in output. The objective of the paper is to provide a solution for natural hazards and provide effective management of Riyadh city. A machine learning technique, interior search algorithm is used in the proposed study. It is used in the research for the extraction of knowledge from complex data. The efficiency of proposed method is compared with state of the art algorithms. The proposed method has achieved an accuracy of 87% in the management of natural hazards.

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Section: Data Pre-processmentioning

confidence: 99%

Managing natural hazards in smart cities in Kingdom of Saudi Arabia using a technique based on interior search algorithm

Dutta

2020

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“…In general, tree species classification is considered one of the most challenging problems in remote sensing. In satellite imagery with visual bands, trees of different species often appear as very similar green blobs and have very little distinction [14]. In related work, most promising results are achieved with hyperspectral imagery.…”

Section: Determining Species Of Individual Treesmentioning

confidence: 99%

Revolutionizing Tree Management via Intelligent Spatial Techniques

Xie

Shekhar

Feiock

et al. 2019

Proceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

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Tree management is becoming a big issue in a variety of societal domains. In recent years, historic wildfires and blackouts caused by failures in tree management have increased in both quantity and severity, resulting in many deaths and financial loses in the tens of billions of dollars. Many communities are also suffering from massive tree loss (e.g., in the millions) that affects the health and well-being of citizens. These problems are likely to worsen due to climate change, aging infrastructure and population growth. Tree management needs a revolution to deal with these urgent problems. This opens up new challenges and opportunities for the spatial community. This paper presents some of the open research problems from the perspectives of individual tree mapping and characterization as well as decision making and in-field intervention. CCS CONCEPTS• Information systems → Spatial-temporal systems; Data mining; • Computing methodologies → Artificial intelligence.

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“…Hotspot detection is a critical topic in spatial data mining [4,37,38,46], and has wide and important applications in smart cities and communities (e.g., [1,37,47]), including public safety, public health, urban planning, equity, and economics, among others. In public safety, regions with significantly high concentration of crime activities can signal the need for administrative inspection and intervention [23,26].…”

Section: Introductionmentioning

confidence: 99%

“…Such hotspots can also help police officers locate the places of residence of serial criminals (e.g., arsonists) [8]. In public health, hotspots of infections indicate outbreaks of disease [18,20,36,47], which can help officials allocate medical resources and research efforts. In environmental science, hotspots of pollution (e.g., Escherichia coli contamination) help researchers identify the cause and improve urban water quality [16,17].…”

Section: Introductionmentioning

confidence: 99%

A Unified Framework for Robust and Efficient Hotspot Detection in Smart Cities

Xie

Shekhar

2020

ACM/IMS Trans. Data Sci.

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Given N geo-located point instances (e.g., crime or disease cases) in a spatial domain, we aim to detect subregions (i.e., hotspots) that have a higher probability density of generating such instances than the others. Hotspot detection has been widely used in a variety of important urban applications, including public safety, public health, urban planning, and equity, among others. The problem is challenging because its societal applications often have low tolerance for false positives and require significance testing that is computationally intensive. In related work, the spatial scan statistic introduced a likelihood ratio-based framework for hotspot evaluation and significance testing. However, it fails to consider the effect of spatial non-determinism, causing many missing detections. Our previous work introduced a non-deterministic normalization-based scan statistic to mitigate this issue. However, its robustness against false positives is not stably controlled. To address these limitations, we propose a unified framework that can improve the completeness of results without incurring more false positives. We also propose a reduction algorithm to improve the computational efficiency. Experiment results confirm that the unified framework can greatly improve the recall of hotspot detection without increasing the number of false positives, and the reduction algorithm can greatly reduce execution time.

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Transforming Smart Cities with Spatial Computing

Cited by 24 publications

References 59 publications

Managing natural hazards in smart cities in Kingdom of Saudi Arabia using a technique based on interior search algorithm

Managing natural hazards in smart cities in Kingdom of Saudi Arabia using a technique based on interior search algorithm

Revolutionizing Tree Management via Intelligent Spatial Techniques

A Unified Framework for Robust and Efficient Hotspot Detection in Smart Cities

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