An urban informatics approach to understanding residential mobility in Metro Chicago

Pan, Haozhi; Chen, Si; Gao, Yizhao; Deal, Brian; Liu, Jinfang

doi:10.1177/2399808320924437

Cited by 25 publications

(15 citation statements)

References 63 publications

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“…Availability and accessibility to the data at the time of the flood and right after that (with high spatial resolution and accuracy) might be considered the limitation for such a dynamic hazard for better analysis and precise prediction. The final result of our research implied a form of new knowledge such as urban informatics [10] in the technique, visualization, and representation of flood hazards for efficient interpretation in the urban area. This flow of information from big data and successful prediction of potential hazardous zones might improve land use planning and economic futures.…”

Section: Discussionmentioning

confidence: 95%

“…Today, remote sensors provide big data from the entire globe in no time. Such data prove their effectiveness in sustainable urban and environment management and the creation of urban informatics for better data representation, visualization, and interpretation of new information [10,11]. Along with big data collection, fast and accurate analysis and visualization are vital [10].…”

Section: Introductionmentioning

confidence: 99%

“…Such data prove their effectiveness in sustainable urban and environment management and the creation of urban informatics for better data representation, visualization, and interpretation of new information [10,11]. Along with big data collection, fast and accurate analysis and visualization are vital [10]. Hence, data mining, modeling, and developing robust and accurate algorithms obtain more attention, especially for natural disaster management and urban planning [12,13].…”

Section: Introductionmentioning

confidence: 99%

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Deep Neural Network Utilizing Remote Sensing Datasets for Flood Hazard Susceptibility Mapping in Brisbane, Australia

Kalantar

Ueda

Saeidi³

et al. 2021

Remote Sensing

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Large damages and losses resulting from floods are widely reported across the globe. Thus, the identification of the flood-prone zones on a flood susceptibility map is very essential. To do so, 13 conditioning factors influencing the flood occurrence in Brisbane river catchment in Australia (i.e., topographic, water-related, geological, and land use factors) were acquired for further processing and modeling. In this study, artificial neural networks (ANN), deep learning neural networks (DLNN), and optimized DLNN using particle swarm optimization (PSO) were exploited to predict and estimate the susceptible areas to the future floods. The significance of the conditioning factors analysis for the region highlighted that altitude, distance from river, STI, and slope played the most important roles, whereas SPI did not contribute to the hazardous situation. The performance of the models was evaluated against the statistical tests such as sensitivity, specificity, the area under curve (AUC), and true skill statistic (TSS). DLNN and PSO-DLNN models obtained the highest values of sensitivity (0.99) for the training stage to compare with ANN. Moreover, the validations of specificity and TSS for PSO-DLNN recorded the highest values of 0.98 and 0.90, respectively, compared with those obtained by ANN and DLNN. The best accuracies by AUC were evaluated in PSO-DLNN (0.99 in training and 0.98 in testing datasets), followed by DLNN and ANN. Therefore, the optimized PSO-DLNN proved its robustness to compare with other methods.

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Section: Discussionmentioning

confidence: 95%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Deep Neural Network Utilizing Remote Sensing Datasets for Flood Hazard Susceptibility Mapping in Brisbane, Australia

Kalantar

Ueda

Saeidi³

et al. 2021

Remote Sensing

View full text Add to dashboard Cite

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“…With developments in modern communication technology, it has become easy to estimate public mobility, which can provide useful insights into urban areas [ 40 ]. The use of daily updated mobility data demonstrates a larger perspective of public reactions and is a good data source to understand potential risks based on public activities.…”

Section: Discussionmentioning

confidence: 99%

One-Year Lesson: Machine Learning Prediction of COVID-19 Positive Cases with Meteorological Data and Mobility Estimate in Japan

Rashed

Hirata

2021

IJERPH

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With the wide spread of COVID-19 and the corresponding negative impact on different life aspects, it becomes important to understand ways to deal with the pandemic as a part of daily routine. After a year of the COVID-19 pandemic, it has become obvious that different factors, including meteorological factors, influence the speed at which the disease is spread and the potential fatalities. However, the impact of each factor on the speed at which COVID-19 is spreading remains controversial. Accurate forecasting of potential positive cases may lead to better management of healthcare resources and provide guidelines for government policies in terms of the action required within an effective timeframe. Recently, Google Cloud has provided online COVID-19 forecasting data for the United States and Japan, which would help in predicting future situations on a state/prefecture scale and are updated on a day-by-day basis. In this study, we propose a deep learning architecture to predict the spread of COVID-19 considering various factors, such as meteorological data and public mobility estimates, and applied it to data collected in Japan to demonstrate its effectiveness. The proposed model was constructed using a neural network architecture based on a long short-term memory (LSTM) network. The model consists of multi-path LSTM layers that are trained using time-series meteorological data and public mobility data obtained from open-source data. The model was tested using different time frames, and the results were compared to Google Cloud forecasts. Public mobility is a dominant factor in estimating new positive cases, whereas meteorological data improve their accuracy. The average relative error of the proposed model ranged from 16.1% to 22.6% in major regions, which is a significant improvement compared with Google Cloud forecasting. This model can be used to provide public awareness regarding the morbidity risk of the COVID-19 pandemic in a feasible manner.

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“…Several researches have employed IoT networks to collect big data for analysis and complex decision making. In [20], the authors use a multidimensional spatial scan technique to discover household movement patterns, in order to help in the development and planning of public policies. The research in [21] reviews studies of IoT applied to smart cities and sustainable cities, including research aimed at conceptual, analytical, and overarching levels, as well as research on specific technologies and applications.…”

Section: Related Workmentioning

confidence: 99%

A Standard-Based Internet of Things Platform and Data Flow Modeling for Smart Environmental Monitoring

Filho¹,

Fernando

Rabelo

et al. 2021

Sensors

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The environment consists of the interaction between the physical, biotic, and anthropic means. As this interaction is dynamic, environmental characteristics tend to change naturally over time, requiring continuous monitoring. In this scenario, the internet of things (IoT), together with traditional sensor networks, allows for the monitoring of various environmental aspects such as air, water, atmospheric, and soil conditions, and sending data to different users and remote applications. This paper proposes a Standard-based Internet of Things Platform and Data Flow Modeling for Smart Environmental Monitoring. The platform consists of an IoT network based on the IEEE 1451 standard which has the network capable application processor (NCAP) node (coordinator) and multiple wireless transducers interface module (WTIM) nodes. A WTIM node consists of one or more transducers, a data transfer interface and a processing unit. Thus, with the developed network, it is possible to collect environmental data at different points within a city landscape, to perform analysis of the communication distance between the WTIM nodes, and monitor the number of bytes transferred according to each network node. In addition, a dynamic model of data flow is proposed where the performance of the NCAP and WTIM nodes are described through state variables, relating directly to the information exchange dynamics between the communicating nodes in the mesh network. The modeling results showed stability in the network. Such stability means that the network has capacity of preserve its flow of information, for a long period of time, without loss frames or packets due to congestion.

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An urban informatics approach to understanding residential mobility in Metro Chicago

Cited by 25 publications

References 63 publications

Deep Neural Network Utilizing Remote Sensing Datasets for Flood Hazard Susceptibility Mapping in Brisbane, Australia

Deep Neural Network Utilizing Remote Sensing Datasets for Flood Hazard Susceptibility Mapping in Brisbane, Australia

One-Year Lesson: Machine Learning Prediction of COVID-19 Positive Cases with Meteorological Data and Mobility Estimate in Japan

A Standard-Based Internet of Things Platform and Data Flow Modeling for Smart Environmental Monitoring

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