Assessing geographical representativeness of crowdsourced urban mobility data: An empirical investigation of Australian bicycling

2019 ◽  
pp. 239980831989433 ◽  
Author(s):  
Scott N Lieske ◽  
Simone Z Leao ◽  
Lindsey Conrow ◽  
Chris Pettit
Keyword(s):  
Spatial Autocorrelation ◽  
Smart Cities ◽  
High Volume ◽  
Urban Mobility ◽  
Flow Data ◽  
Rapid Urbanization ◽  
Validity Assessment ◽  
Planning And Management ◽  
Crowdsourced Data ◽  
Interaction Matrices

In an era of data-driven smart cities, the possibility of using crowdsourced big data to support evidence-based planning and decision-making remains a challenge. Along with the increased availability and potential utility of crowdsourced data, there is a clear need to assess the validity of these data in order to determine their appropriate use for planning and management. Moreover, with growth and rapid urbanization in many cities, there are increasing challenges associated with urban mobility. The goal of this research is to develop an understanding of the geographical representativeness of crowdsourced data in the context of urban mobility through investigation of bicycling in Australian cities. In order to leverage both the geographic distribution and high volume of crowdsourced data for validity assessment, we present a two-stage statistical approach. First, we evaluate flow data through correlation between spatial interaction matrices in the presence of spatial autocorrelation. The second stage evaluates the quantity of information available within the interaction matrices. The approach is demonstrated with crowdsourced bicycling commuting routes recorded by the RiderLog app from 2010 to 2014 that are then correlated with census bicycling journey to work data. Data are from four of Australia’s state capital cities: Adelaide, Brisbane, Melbourne and Perth. These methods assess the representativeness of individual bicycle routes that address the full pattern of flows within multiorigin multidestination systems and incorporate spatial autocorrelation. Results indicate that these crowdsourced data are geographically representative of regional travel where there are higher data volumes, generally in central business districts and occasionally in outlying areas. This research provides insights into both methods for statistical comparison of flow data and the use of crowdsourced bicycling routes for urban planning and management.

scholarly journals FORECASTING URBAN POPULATION DISTRIBUTION OF ILOILO CITY USING GIS AND SPATIAL AUTOCORRELATION MODELS

2021 ◽  
Vol XLVI-4/W6-2021 ◽  
pp. 185-192
Author(s):  
L. Hilario ◽  
J. A. Duka ◽  
M. I. Mabalot ◽  
J. Domingo ◽  
K. A. Vergara ◽  
...  
Keyword(s):  
Spatial Autocorrelation ◽  
Population Distribution ◽  
Smart Cities ◽  
Spatial Interaction ◽  
Rapid Urbanization ◽  
Urban And Regional Planning ◽  
Factors Affecting ◽  
Explanatory Variables ◽  
Goods And Services ◽  
Population Forecasts

Abstract. Rapid urbanization in localities offers a lot of opportunities but also imposes a lot of challenges due to its direct relationship to population growth. This leads to an increase in the demand for essential goods and services such as food, energy, water among others. Hence, small-area population forecasts have long been an important element in urban and regional planning to aid in the decision-making processes in a locality. The promise of smart cities, through the use of advanced technologies, is to make cities livable and sustainable, preparing more opportunities and addressing challenges on urbanization. This study aims to forecast population distribution in Iloilo city by incorporating GIS techniques and highlighting the use of spatial autocorrelation models. The spatial interaction effects between neighboring barangays are taken into consideration to identify a set of factors affecting the population. The results identified a set of significant explanatory variables and whether it will result in an increase or decrease in population. The study also illustrates the resulting population forecast comparing it to the actual total population of the city.

Spatial Autocorrelation in the Study of Neighbourhoods towards Smart Cities: Empirical Evidence from Kerman, Iran

2012 ◽  
Vol 9 (2) ◽  
pp. 1
Author(s):  
Asra Hosseini
Keyword(s):  
Spatial Autocorrelation ◽  
Urban Areas ◽  
Smart Cities ◽  
Moran’S I ◽  
Moran's I ◽  
Urban Planner ◽  
Universal Feature ◽  
Autocorrelation Method ◽  
Selection Of

From earliest cities to the present, spatial division into residential zones and neighbourhoods is the universal feature of urban areas. This study explored issue of measuring neighbourhoods through spatial autocorrelation method based on Moran's I index in respect of achieving to best neighbourhoods' model for forming cities smarter. The research carried out by selection of 35 neighbourhoods only within central part of traditional city of Kerman in Iran. The results illustrate, 75% of neighbourhoods' area in the inner city of Kerman had clustered pattern, and it shows reduction in Moran's index is associated with disproportional distribution of density and increasing in Moran's I and Z-score have monotonic relation with more dense areas and clustered pattern. It may be more efficient for urban planner to focus on spatial autocorrelation to foster neighbourhood cohesion rather than emphasis on suburban area. It is recommended characteristics of historic neighbourhoods can be successfully linked to redevelopment plans toward making city smarter, and also people's quality of life can be related to the way that neighbourhoods' patterns are defined. 

Spatial Autocorrelation in the Study of Neighbourhoods towards Smart Cities: Empirical Evidence from Kerman, Iran

2012 ◽  
Vol 9 (2) ◽  
pp. 1
Author(s):  
Asra Hosseini
Keyword(s):  
Spatial Autocorrelation ◽  
Smart Cities ◽  
Moran’S I ◽  
Moran's I ◽  
Urban Planner ◽  
Universal Feature ◽  
Autocorrelation Method ◽  
Selection Of ◽  
Monotonic Relation

From earliest cities to the present, spatial division into residential zones and neighbourhoods is the universal feature ofurban areas. This study explored issue ofmeasuring neighbourhoods through spatial autocorrelation method based on Moran's I index in respect of achieving to best neighbourhoods' model for forming cities smarter. The research carried out by selection of 35 neighbourhoods only within central part of traditional city of Kerman in Iran. The results illustrate, 75% ofneighbourhoods, area in the inner city of Kerman had clustered pattern, and it shows reduction in Moran's index is associated with disproportional distribution of density and increasing in Moran's I and Z-score have monotonic relation with more dense areas and clustered pattern. It may be more efficient for urban planner to focus on spatial autocorrelation to foster neighbourhood cohesion rather than emphasis on suburban area. It is recommended characteristics of historic neighbourhoods can be successfully linked to redevelopment plans toward making city smarter, and also people's quality of life can be related to the way that neighbourhoods' patterns are defined.

scholarly journals Smart and Resilient Urban Futures for Sustainability in the Post COVID-19 Era: A Review of Policy Responses on Urban Mobility

2021 ◽  
Vol 13 (11) ◽  
pp. 6486
Author(s):  
Christina Kakderi ◽  
Eleni Oikonomaki ◽  
Ilektra Papadaki
Keyword(s):  
Operations Management ◽  
Smart Cities ◽  
Smart Growth ◽  
Urban Mobility ◽  
Sustainable Mobility ◽  
Health Crisis ◽  
Daily Routines ◽  
Policy Responses ◽  
Urban Futures ◽  
Made In

The COVID-19 pandemic has put lifestyles in question, changed daily routines, and limited citizen freedoms that seemed inalienable before. A human activity that has been greatly affected since the beginning of the health crisis is mobility. Focusing on mobility, we aim to discuss the transformational impact that the pandemic brought to this specific urban domain, especially with regards to the promotion of sustainability, the smart growth agenda, and the acceleration towards the smart city paradigm. We collect 60 initial policy responses related to urban mobility from cities around the world and analyze them based on the challenge they aim to address, the exact principles of smart growth and sustainable mobility that they encapsulate, as well as the level of ICT penetration. Our findings suggest that emerging strategies, although mainly temporary, are transformational, in line with the principles of smart growth and sustainable development. Most policy responses adopted during the first months of the pandemic, however, fail to leverage advancements made in the field of smart cities, and to adopt off-the-shelf solutions such as monitoring, alerting, and operations management.

scholarly journals Real Time Multipurpose Smart Waste Classification Model for Efficient Recycling in Smart Cities Using Multilayer Convolutional Neural Network and Perceptron

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4916
Author(s):  
Ali Usman Gondal ◽  
Muhammad Imran Sadiq ◽  
Tariq Ali ◽  
Muhammad Irfan ◽  
Ahmad Shaf ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Real Time ◽  
Multilayer Perceptron ◽  
Smart Cities ◽  
Classification Model ◽  
Rapid Urbanization ◽  
Metal Waste ◽  
Waste Classification ◽  
The City

Urbanization is a big concern for both developed and developing countries in recent years. People shift themselves and their families to urban areas for the sake of better education and a modern lifestyle. Due to rapid urbanization, cities are facing huge challenges, one of which is waste management, as the volume of waste is directly proportional to the people living in the city. The municipalities and the city administrations use the traditional wastage classification techniques which are manual, very slow, inefficient and costly. Therefore, automatic waste classification and management is essential for the cities that are being urbanized for the better recycling of waste. Better recycling of waste gives the opportunity to reduce the amount of waste sent to landfills by reducing the need to collect new raw material. In this paper, the idea of a real-time smart waste classification model is presented that uses a hybrid approach to classify waste into various classes. Two machine learning models, a multilayer perceptron and multilayer convolutional neural network (ML-CNN), are implemented. The multilayer perceptron is used to provide binary classification, i.e., metal or non-metal waste, and the CNN identifies the class of non-metal waste. A camera is placed in front of the waste conveyor belt, which takes a picture of the waste and classifies it. Upon successful classification, an automatic hand hammer is used to push the waste into the assigned labeled bucket. Experiments were carried out in a real-time environment with image segmentation. The training, testing, and validation accuracy of the purposed model was 0.99% under different training batches with different input features.

scholarly journals How Far Has China’s Urbanization Gone?

2018 ◽  
Vol 10 (8) ◽  
pp. 2953 ◽  
Author(s):  
Yiping Xiao ◽  
Yan Song ◽  
Xiaodong Wu
Keyword(s):  
Urban Development ◽  
Spatial Autocorrelation ◽  
Spatial Data ◽  
Spatial Association ◽  
Early Years ◽  
Spatial Data Analysis ◽  
Rapid Urbanization ◽  
Urban Rural ◽  
Global Spatial Autocorrelation ◽  
Over Time

China’s rapid urbanization has attracted wide international attention. However, it may not be sustainable. In order to assess it objectively and put forward recommendations for future development, this paper first develops a four-dimensional Urbanization Quality Index using weights calculated by the Deviation Maximization Method for a comprehensive assessment and then reveals the spatial association of China’s urbanization by Exploratory Spatial Data Analysis. The study leads to three major findings. First, the urbanization quality in China has gradually increased over time, but there have been significant differences between regions. Second, the four aspects of urbanization quality have shown the following trends: (i) the quality of urban development has steadily increased; (ii) the sustainability of urban development has shown a downward trend in recent years; (iii) the efficiency of urbanization improved before 2006 but then declined slightly due to capital, land use, and resource efficiency constraints; (IV) the urban–rural integration deteriorated in the early years but then improved over time. Third, although the urbanization quality has a significantly positive global spatial autocorrelation, the local spatial autocorrelation varies between eastern and western regions. Based on these findings, this paper concludes with policy recommendations for improving urbanization quality and its sustainability in China.

scholarly journals A New Wearable System for Sensing Outdoor Environmental Conditions for Monitoring Hyper-Microclimate

Sensors ◽  
2022 ◽  
Vol 22 (2) ◽  
pp. 502
Author(s):  
Roberta Jacoby Cureau ◽  
Ilaria Pigliautile ◽  
Anna Laura Pisello
Keyword(s):  
Air Quality ◽  
Environmental Monitoring ◽  
Smart Cities ◽  
Control Unit ◽  
Urban Environments ◽  
Physical Parameters ◽  
Negative Effects ◽  
Rapid Urbanization ◽  
Wearable System ◽  
New Device

The rapid urbanization process brings consequences to urban environments, such poor air quality and the urban heat island issues. Due to these effects, environmental monitoring is gaining attention with the aim of identifying local risks and improving cities’ liveability and resilience. However, these environments are very heterogeneous, and high-spatial-resolution data are needed to identify the intra-urban variations of physical parameters. Recently, wearable sensing techniques have been used to perform microscale monitoring, but they usually focus on one environmental physics domain. This paper presents a new wearable system developed to monitor key multidomain parameters related to the air quality, thermal, and visual domains, on a hyperlocal scale from a pedestrian’s perspective. The system consisted of a set of sensors connected to a control unit settled on a backpack and could be connected via Wi-Fi to any portable equipment. The device was prototyped to guarantee the easy sensors maintenance, and a user-friendly dashboard facilitated a real-time monitoring overview. Several tests were conducted to confirm the reliability of the sensors. The new device will allow comprehensive environmental monitoring and multidomain comfort investigations to be carried out, which can support urban planners to face the negative effects of urbanization and to crowd data sourcing in smart cities.

Applications of Big Data and Green IoT-Enabling Technologies for Smart Cities

2019 ◽  
pp. 22-41
Author(s):  
Onur Dogan ◽  
Omer Faruk Gurcan
Keyword(s):  
Big Data ◽  
Traffic Congestion ◽  
Urban Areas ◽  
Smart Cities ◽  
Digital Data ◽  
Rapid Urbanization ◽  
Data Resource ◽  
Parallel Data ◽  
Information And Communication

In recent years, enormous amounts of digital data have been generated. In parallel, data collection, storage, and analysis technologies have developed. Recently, there has been an increasing trend of people moving towards urban areas. By 2030 more than 60% of the world's population will live in an urban environment. Urban areas are big data resource because they include millions of citizens, technological devices, and vehicles which generate data continuously. Besides, rapid urbanization brings many challenges, such as environmental pollution, traffic congestion, health problems, energy management, etc. Some policies for countries are required to cope with urbanization problems. One of these policies is to build smart cities. Smart cities integrate information and communication technology and various physical devices connected to the network (the internet of things or IoT) to both improve the quality of government services and citizen welfare. This chapter presents a literature review of big data, smart cities, IoT, green-IoT concepts, using technology and methods, and applications worldwide.

Smart Cities and Governance

2021 ◽  
pp. 464-483
Author(s):  
Özcan Sezer ◽  
Mehmet Avcı
Keyword(s):  
Smart City ◽  
Smart Cities ◽  
Action Plan ◽  
Public Agencies ◽  
Rapid Urbanization ◽  
Social Challenges ◽  
Public Resources ◽  
Strategic Goals ◽  
Smart Governance

Cities are futures' crucial elements, playing an important role in economics, social and environmental. As closer to individuals, cities face some challenges in terms of problems caused through the rapid urbanization process. Hence, governments and public agencies at all levels should use smart techniques including technology for sustainable development, better quality of life for citizens, and finally, an efficient use of scarce public resources. In this sense, Turkey plans to apply a smart city concept in Turkish cities as worldwide and published 2020-2023 National Smart Cities Strategy and Action Plan document with four strategic goals, nine targets, and 40 actions. This chapter aims to reveal the institutional, fiscal, and social challenges on smart governance, which is the most important dimension of smart city, for Turkey. In this respect, there are some challenges on smart governance in Turkey in terms of legislation, institutional, transparency and accountability, participation, e-democracy, and citizens.

scholarly journals Could a Smart City Ameliorate Urban Traffic Congestion? A Quasi-Natural Experiment Based on a Smart City Pilot Program in China

2020 ◽  
Vol 12 (6) ◽  
pp. 2291
Author(s):  
Yuhui Guo ◽  
Zhiwei Tang ◽  
Jie Guo
Keyword(s):  
Traffic Congestion ◽  
Smart City ◽  
Urban Governance ◽  
Smart Cities ◽  
Dynamic Effect ◽  
Urban Traffic ◽  
Marginal Effect ◽  
Positive Role ◽  
Rapid Urbanization ◽  
City Construction

More countries and regions are joining the bandwagon of smart city construction, which is an important strategy and innovative urban governance concept to solve the problem of rapid urbanization. This paper examines whether smart city innovation is able to ameliorate the traffic congestion faced by a large number of cities. Using panel data for 187 prefecture-level cities in China from 2008 to 2017, this paper tests the effect of implementation of a smart city on urban traffic congestion with the difference-in-difference method. The results show that, firstly, the construction of smart cities have significantly reduced the degree of urban traffic congestion and improved the quality and capacity of public transport. Secondly, information technology and urban innovation are the main mechanisms for smart city implementation to improve urban traffic problems. Thirdly, the improvement effect of smart city implementation on traffic management shows an increasing marginal effect over time. By overcoming the estimation bias in previous studies, this study accurately analyzes the positive role and dynamic effect of smart city construction on traffic improvement. It augments the literature of program evaluation and assessment of smart city implementation. By examining how to improve traffic congestion, it offers some insights that could inspire governments to build smarter cities with better traffic.

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