Entropy-Based Approach for the Analysis of Spatio-Temporal Urban Growth Dynamics

Relative Entropy (RE) is defined as the measure of the degree of randomness of any geographical variable (i.e., urban growth). It is an effective indicator to evaluate the patterns of urban growth, whether compact or dispersed. In the present study, RE has been used to evaluate the urban growth of Dehradun city. Dehradun, the capital of Uttarakhand, is situated in the foothills of the Himalayas and has undergone rapid urbanization. Landsat satellite data for the years 2000, 2010 and 2019 have been used in the study. Built-up cover outside municipal limits and within municipal limits was classified for the given time period. The road network and city center of the study area were also delineated using satellite data. RE was calculated for the periods 2000–2010 and 2010–2019 with respect to the road network and city center. High values of RE indicate higher levels of urban sprawl, whereas lower values indicate compactness. The urban growth pattern over a period of 19 years was examined with the help of RE.

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Urban Growth Modeling using Historical Landsat Satellite Data Archive on Google Earth Engine

2019 First International Conference on Smart Technology & Urban Development (STUD) ◽

10.1109/stud49732.2019.9018846 ◽

2019 ◽

Author(s):

Hiroyuki Miyazaki ◽

Himanshu Bhushan ◽

Kotone Wakiya

Keyword(s):

Urban Growth ◽

Satellite Data ◽

Growth Modeling ◽

Google Earth ◽

Data Archive ◽

Urban Growth Modeling ◽

Google Earth Engine ◽

Landsat Satellite

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Scattering in remote sensing in the visible and microwave spectral range and in traffic control

Advances in Radio Science ◽

10.5194/ars-1-309-2003 ◽

2003 ◽

Vol 1 ◽

pp. 309-311

Author(s):

U. Böttger ◽

R. Kühne ◽

K.-U. Thiessenhusene

Keyword(s):

Complex System ◽

Remote Sensing ◽

Spectral Range ◽

Satellite Data ◽

Traffic Control ◽

Road Network ◽

Traffic Simulation ◽

The Road ◽

Traffic Area ◽

Spectral Ranges

Abstract. The treatment of scattering processes in remote sensing for interpretation of satellite data is demonstrated in the visible and microwave spectral range comparing the two spectral ranges. Analogies and distinctions in the treatment of the scattering processes are shown. Based on this cognition an approach for traffic simulation is outlined. Simulating the traffic of a part of a city, a whole city or a larger area in an acceptable time is one of the tasks in recent traffic research. One possible approach is the areal treatment of the road network. That means that single streets are not resolved but are introduced into simulations only by parameters that correspond to a specific traffic area resistance. The aim of this work is to outline such a possibility using experiences obtained from the theory of radiative transport to simulate scattering processes and applying them to the very complex system of traffic simulation.

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Mining Topological Dependencies of Recurrent Congestion in Road Networks

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi10040248 ◽

2021 ◽

Vol 10 (4) ◽

pp. 248

Author(s):

Nicolas Tempelmeier ◽

Udo Feuerhake ◽

Oskar Wage ◽

Elena Demidova

Keyword(s):

Network Topology ◽

Real World ◽

Public Transportation ◽

Road Network ◽

Road Networks ◽

Traffic Load ◽

Data Mining Algorithm ◽

Urban Road ◽

The Road ◽

Spatio Temporal

The discovery of spatio-temporal dependencies within urban road networks that cause Recurrent Congestion (RC) patterns is crucial for numerous real-world applications, including urban planning and the scheduling of public transportation services. While most existing studies investigate temporal patterns of RC phenomena, the influence of the road network topology on RC is often overlooked. This article proposes the ST-Discovery algorithm, a novel unsupervised spatio-temporal data mining algorithm that facilitates effective data-driven discovery of RC dependencies induced by the road network topology using real-world traffic data. We factor out regularly reoccurring traffic phenomena, such as rush hours, mainly induced by the daytime, by modelling and systematically exploiting temporal traffic load outliers. We present an algorithm that first constructs connected subgraphs of the road network based on the traffic speed outliers. Second, the algorithm identifies pairs of subgraphs that indicate spatio-temporal correlations in their traffic load behaviour to identify topological dependencies within the road network. Finally, we rank the identified subgraph pairs based on the dependency score determined by our algorithm. Our experimental results demonstrate that ST-Discovery can effectively reveal topological dependencies in urban road networks.

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Research on Modeling and Indexing of Trajectories of Moving Objects in Road Networks

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.756-759.1234 ◽

2013 ◽

Vol 756-759 ◽

pp. 1234-1239

Author(s):

Yan Ling Zheng

Keyword(s):

Road Network ◽

Moving Objects ◽

Road Networks ◽

Experimental Results ◽

Index Structure ◽

Index Structures ◽

The Road ◽

The Past ◽

Spatio Temporal ◽

Temporal Trajectory

Proposed a new index structure, named MG2R*, can efficiently store and retrieve the past, present and future positions of network-constrained moving objects. It is a two-tier structure. The upper is a MultiGrid-R*-Tree (MGRT for short) that is used to index the road network. The lower is a group of independent R*-Tree. Each R*-Tree is relative to a route in the road network, can index the spatiotemporal trajectory of the moving objects in the road. Moreover, moving objects query is implemented based on this index structure. It compared to other index structures for road-network-based moving objects, such as MON-Tree, the experimental results shown that the MG2R* can effectively improve the query performance of the spatio-temporal trajectory of network-constrained moving objects.

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Spatio-temporal urban growth dynamics of Lagos Metropolitan Region of Nigeria based on Hybrid methods for LULC modeling and prediction

European Journal of Remote Sensing ◽

10.1080/22797254.2017.1419831 ◽

2018 ◽

Vol 51 (1) ◽

pp. 251-265 ◽

Cited By ~ 9

Author(s):

Jianzhu Wang ◽

Ikechukwu Nnamdi Maduako

Keyword(s):

Urban Growth ◽

Hybrid Methods ◽

Growth Dynamics ◽

Metropolitan Region ◽

Modeling And Prediction ◽

Spatio Temporal

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A quantitative analysis of urban growth and associated thermal characteristics using Landsat satellite data

10.1117/12.812777 ◽

2008 ◽

Author(s):

Yongnain Zeng ◽

Honghui Zhang ◽

Bin Zou ◽

Hua Li

Keyword(s):

Quantitative Analysis ◽

Urban Growth ◽

Satellite Data ◽

Thermal Characteristics ◽

Landsat Satellite

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Evaluation of urban growth and land use transformation in Riyadh using Landsat satellite data

Arabian Journal of Geosciences ◽

10.1007/s12517-018-3896-5 ◽

2018 ◽

Vol 11 (18) ◽

Cited By ~ 1

Author(s):

M. Shakir Khan ◽

Mohammad Suhail ◽

T. Alharbi

Keyword(s):

Land Use ◽

Urban Growth ◽

Satellite Data ◽

Land Use Transformation ◽

Landsat Satellite

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Bridge closure in the road network of Lombardy: a spatio-temporal analysis of the socio-economic impacts

Statistical Methods & Applications ◽

10.1007/s10260-021-00620-3 ◽

2022 ◽

Author(s):

Agostino Torti ◽

Marika Arena ◽

Giovanni Azzone ◽

Piercesare Secchi ◽

Simone Vantini

Keyword(s):

Network Theory ◽

Functional Data ◽

Road Network ◽

Economic Impacts ◽

Temporal Analysis ◽

Decision Makers ◽

Complex Network Theory ◽

The Road ◽

Spatio Temporal ◽

Different Levels

AbstractThis paper introduces a methodology to evaluate the socio-economic impacts of closure for maintenance of one or more infrastructures of a large and complex road network. Motivated by a collaboration with Regione Lombardia, we focus on a subset of bridges in the region, although we aim at developing a method scalable to all road infrastructures of the regional network, consisting of more than 10,000 tunnels, bridges and overpasses. The final aim of the endeavor is to help decision-makers in prioritizing their interventions for maintaining and repairing infrastructure segments. We develop two different levels of impact assessment, both providing a unique global score for each bridge closure and investigating its spatio-temporal effects on mobility. We take advantage of a functional data analysis approach enhanced by a complex network theory perspective, thus modelling the roads of Lombardy as a network in which weights attributed to the edges are functional data. Results reveal the most critical bridges of Lombardy; moreover, for each bridge closure, the most impactful hours of the day and the most impacted municipalities of the region are identified. The proposed approach develops a flexible and scalable method for monitoring infrastructures of large and complex road networks.

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