scholarly journals A Spatio-Temporal Enhanced Metadata Model for Interdisciplinary Instant Point Observations in Smart Cities

2017 ◽  
Vol 6 (2) ◽  
pp. 50 ◽  
Author(s):  
Nengcheng Chen ◽  
Yingbing Liu ◽  
Jia Li ◽  
Zeqiang Chen
Keyword(s):  
Smart Cities ◽  
Metadata Model ◽  
Spatio Temporal

scholarly journals Analyzing Temporal Changes in an Urbanized Area Using Densely Staked Image Classification and Multinomial Logistic Regression (MLR) Technique

2020 ◽  
Author(s):  
Abhinav Wadhwa ◽  
Pavan Kumar Kummamuru
Keyword(s):  
Road Network ◽  
Smart Cities ◽  
Multinomial Logistic Regression ◽  
Land Use Land Cover ◽  
Multinomial Regression ◽  
Causative Factors ◽  
Major Transition ◽  
Spatio Temporal ◽  
Urban Extent ◽  
Urbanization Pattern

Abstract Monitoring transformation of non-built-up area to urban spread via densely-stacked Land-Use-Land-Cover (LULC) classification offers a catalogue of spatio-temporal statistics to evaluate discrepancies instigated by transition factors. Impacts of major transition apparatuses in an area persuading the haphazard urbanization pattern are evaluated for Vellore acts a major contribution to Smart city project. Implications of causative factors: i) Population density; ii) proximity from rail-road-network; and iii) commercial areas are scrutinized with respect to urbanization upsurge. Multi-variate correlation is established using trend analysis and Multinomial Regression (MLR) technique for individual and homogeneous amalgamation of the aforementioned factors. Resulting equations obtained is formally used to detect closeness of urban extent from several landscapes. Research outcomes exhibited that the built-up straggling occurs from 30 to 232 m along the landscapes with a maximum of 336 m. Illustration of this study can also be assessed for various social and economic causative factors against urbanization for other smart cities.

scholarly journals EDISON: An Edge-Native Method and Architecture for Distributed Interpolation

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2279
Author(s):  
Lauri Lovén ◽  
Tero Lähderanta ◽  
Leena Ruha ◽  
Ella Peltonen ◽  
Ilkka Launonen ◽  
...  
Keyword(s):  
Smart Cities ◽  
Sensor Data ◽  
Fine Grained ◽  
Distributed Approach ◽  
Computing Paradigm ◽  
Temporal Interpolation ◽  
Data Points ◽  
Alternative Approaches ◽  
Spatio Temporal

Spatio-temporal interpolation provides estimates of observations in unobserved locations and time slots. In smart cities, interpolation helps to provide a fine-grained contextual and situational understanding of the urban environment, in terms of both short-term (e.g., weather, air quality, traffic) or long term (e.g., crime, demographics) spatio-temporal phenomena. Various initiatives improve spatio-temporal interpolation results by including additional data sources such as vehicle-fitted sensors, mobile phones, or micro weather stations of, for example, smart homes. However, the underlying computing paradigm in such initiatives is predominantly centralized, with all data collected and analyzed in the cloud. This solution is not scalable, as when the spatial and temporal density of sensor data grows, the required transmission bandwidth and computational capacity become unfeasible. To address the scaling problem, we propose EDISON: algorithms for distributed learning and inference, and an edge-native architecture for distributing spatio-temporal interpolation models, their computations, and the observed data vertically and horizontally between device, edge and cloud layers. We demonstrate EDISON functionality in a controlled, simulated spatio-temporal setup with 1 M artificial data points. While the main motivation of EDISON is the distribution of the heavy computations, the results show that EDISON also provides an improvement over alternative approaches, reaching at best a 10% smaller RMSE than a global interpolation and 6% smaller RMSE than a baseline distributed approach.

scholarly journals Spatio-Temporal Crime Predictions by Leveraging Artificial Intelligence for Citizens Security in Smart Cities

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Umair Muneer Butt ◽  
Sukumar Letchmunan ◽  
Fadratul Hafinaz Hassan ◽  
Mubashir Ali ◽  
Anees Baqir ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Smart Cities ◽  
Spatio Temporal

scholarly journals SPATIO-TEMPORAL MODELLING & THE NEW URBAN AGENDA IN POST-APARTHEID SOUTH AFRICA

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 1327-1332
Author(s):  
B. T. Mokoena ◽  
T. Moyo ◽  
E. N. Makoni ◽  
W. Musakwa
Keyword(s):  
South Africa ◽  
Industrial Revolution ◽  
Smart Cities ◽  
Spatial Integration ◽  
Smart Transportation ◽  
Development Framework ◽  
Transportation Modes ◽  
Form Part ◽  
Temporal Modelling ◽  
Spatio Temporal

<p><strong>Abstract.</strong> This paper presents the potentialities of spatio-temporal modelling in transforming South Africa’s previously marginalised townships. Using the Katlehong township in Ekurhuleni as a case study, the paper argues that the hitherto marginalised townships can benefit from a localised implementation of smart-city concepts as articulated in the Integrated Urban Development Framework. Instead of viewing townships as spaces of perpetual despair and hopelessness, the paper appreciates these areas as having the potential to benefit from new smart innovative planning approaches that form part of the Fourth Industrial Revolution. So, the discussion identifies smart transportation modes such as bicycle-sharing, as well as Bus Rapid Transit Networks as critical in promoting mobility in and beyond townships, while contributing to spatial integration and transformation. Using geolocation data, the paper concludes that formerly marginalised townships such as Katlehong can and must form part of the emergent smart cities in South Africa.</p>

scholarly journals Air Pollution Prediction in Smart Cities by using Machine Learning Techniques

2020 ◽  
Vol 9 (5) ◽  
pp. 1272-1279
Keyword(s):  
Air Pollution ◽  
Smart Cities ◽  
Time Series Prediction ◽  
Arima Model ◽  
Urban Air Pollution ◽  
Handling Time ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Air Contamination ◽  
Spatio Temporal

The urban air pollution has an immediate effect on man health specifically in developing and mechanical countries. It can cause health issues such as cancer, cardiovascular diseases and high mortality rates. Continuous checking of contamination empowers the metropolitans to dissect the present traffic circumstance of the city and take their decision accordingly. Existing exploration has utilized diverse AI apparatuses for pollution forecast; notwithstanding, relative examination of these methods is regularly required to have a superior comprehension of their handling time for numerous datasets. In this work, we look at forecasting the air contamination by dealing with parameters of three different gases like SO2 ,NO2 ,O3 .This process involves to pre-processing the times series. However, pre-processing involves a similarity measure, we explore the use of Dynamic Time Warping (DTW),LSTM,ARIMA Model for time series prediction, Kmeans, Support Vector Regression is then used to classify the spatio-temporal pollution data of different areas over a period of 10 years.

scholarly journals Postcolonial urban futures: Imagining and governing India’s smart urban age

2018 ◽  
Vol 37 (3) ◽  
pp. 393-410 ◽  
Author(s):  
Ayona Datta
Keyword(s):  
Smart City ◽  
Power Relations ◽  
Social Power ◽  
Smart Cities ◽  
Future Time ◽  
The Past ◽  
Urban Transformations ◽  
Urban Futures ◽  
Spatio Temporal ◽  
The Rich

This paper examines the ‘future’ as a blueprint for social power relations in postcolonial urbanism. It addresses a crucial gap in the rich scholarship on postcolonial urbanism that has largely ignored the ‘centrality of time’ (Chakrabarty, 2000 ) in the politics and speed of urban transformations. This paper takes postcolonial urbanism as a ‘colonisation of/with time’ (Adam, 2004 ) that reaches across spaces, scales and times of the past, present and future to produce cities as spatio-temporal entities. Using the lens of ‘futuring’ (Urry, 2016 ) as a practice of imagining and governing cities through speed, this paper analyses India’s national 100 Smart Cities Mission through a set of popular myths that create a dialectic relation between past and future. It suggests that smart cities in India are marked by the deployment of two parallel mythologies of speed – nationhood and technology. While the former refers to a mythical moral state, the latter refers to transparent and accountable governance in order to produce smart cities in the image of the moral state. The paper concludes that while postcolonial future time is imagined at the scale of the smart city, there is a simultaneous recalibration of its governance at the scale of the nation.

Quantifying the Spatio-Temporal Potential of Drive-by Sensing in Smart Cities

2021 ◽  
pp. 195-212
Author(s):  
Amin Anjomshoaa ◽  
Paolo Santi ◽  
Fabio Duarte ◽  
Carlo Ratti
Keyword(s):  
Smart Cities ◽  
Spatio Temporal
Sign in / Sign up

Export Citation Format

Share Document