Assessing Pattern of Spatio-temporal Change in NCT of Delhi and its Peri-urban Areas using Geospatial Techniques

2017 ◽  
pp. 145-160 ◽  
Author(s):  
Dipanwita Dutta ◽  
Atiqur Rahman
Keyword(s):  
Urban Areas ◽  
Temporal Change ◽  
Geospatial Techniques ◽  
Spatio Temporal

Cropland Spatio-temporal Change and Driving Force in Three Gorges Reservoir Area

2011 ◽  
Vol 13 (3) ◽  
pp. 297-304 ◽  
Author(s):  
Lei ZHANG ◽  
Bingfang WU ◽  
Liang ZHU ◽  
Peng WANG
Keyword(s):  
Driving Force ◽  
Three Gorges Reservoir ◽  
Temporal Change ◽  
Three Gorges Reservoir Area ◽  
Three Gorges ◽  
Reservoir Area ◽  
Spatio Temporal

scholarly journals Multi-Criteria, Co-Evolutionary Charging Behavior: An Agent-Based Simulation of Urban Electromobility

2021 ◽  
Vol 12 (1) ◽  
pp. 18
Author(s):  
Lennart Adenaw ◽  
Markus Lienkamp
Keyword(s):  
Urban Areas ◽  
City Planning ◽  
User Behavior ◽  
Transport Sector ◽  
Simulation Framework ◽  
Agent Based Simulation ◽  
Agent Based ◽  
Charging Station ◽  
Spatio Temporal

In order to electrify the transport sector, scores of charging stations are needed to incentivize people to buy electric vehicles. In urban areas with a high charging demand and little space, decision-makers are in need of planning tools that enable them to efficiently allocate financial and organizational resources to the promotion of electromobility. As with many other city planning tasks, simulations foster successful decision-making. This article presents a novel agent-based simulation framework for urban electromobility aimed at the analysis of charging station utilization and user behavior. The approach presented here employs a novel co-evolutionary learning model for adaptive charging behavior. The simulation framework is tested and verified by means of a case study conducted in the city of Munich. The case study shows that the presented approach realistically reproduces charging behavior and spatio-temporal charger utilization.

scholarly journals Measuring Accessibility Based on Improved Impedance and Attractive Functions Using Taxi Trajectory Data

2020 ◽  
Vol 13 (1) ◽  
pp. 112
Author(s):  
Helai Huang ◽  
Jialing Wu ◽  
Fang Liu ◽  
Yiwei Wang
Keyword(s):  
Waiting Time ◽  
Urban Areas ◽  
Transportation Systems ◽  
Trajectory Data ◽  
Peak Period ◽  
Important Indicator ◽  
Destination Attractiveness ◽  
Urban Accessibility ◽  
Spatio Temporal ◽  
Taxi Trajectory

Accessibility has attracted wide interest from urban planners and transportation engineers. It is an important indicator to support the development of sustainable policies for transportation systems in major events, such as the COVID-19 pandemic. Taxis are a vital travel mode in urban areas that provide door-to-door services for individuals to perform urban activities. This study, with taxi trajectory data, proposes an improved method to evaluate dynamic accessibility depending on traditional location-based measures. A new impedance function is introduced by taking characteristics of the taxi system into account, such as passenger waiting time and the taxi fare rule. An improved attraction function is formulated by considering dynamic availability intensity. Besides, we generate five accessibility scenarios containing different indicators to compare the variation of accessibility. A case study is conducted with the data from Shenzhen, China. The results show that the proposed method found reduced urban accessibility, but with a higher value in southern center areas during the evening peak period due to short passenger waiting time and high destination attractiveness. Each spatio-temporal indicator has an influence on the variation in accessibility.

Spatio-Temporal Modeling on Village Land Feature Change

2012 ◽  
Vol 204-208 ◽  
pp. 2721-2725
Author(s):  
Hua Ji Zhu ◽  
Hua Rui Wu
Keyword(s):  
Real World ◽  
Temporal Change ◽  
End Users ◽  
Temporal Modeling ◽  
The Real ◽  
Change Type ◽  
Feature Change ◽  
Spatio Temporal ◽  
The Village ◽  
Land Changes

Village land continually changes in the real world. In order to keep the data up-to-date, data producers need update the data frequently. When the village land data are updated, the update information must be dispensed to the end-users to keep their client-databases current. In the real world, village land changes in many forms. Identifying the change type of village land (i.e. captures the semantics of change) and representing them in the data world can help end-users understand the change commonly and be convenient for end-users to integrate these change information into their databases. This work focuses on the model of the spatio-temporal change. A three-tuple model CAR for representing the spatio-temporal change is proposed based on the village land feature set before change and the village land feature set after change, change type and rules. In this model, the C denotes the change type. A denotes the attribute set; R denotes the judging rules of change type. The rule is described by the IF－THEN expressions. By the operations between R and A, the C is distinguished. This model overcomes the limitations of current methods. And more, the rules in this model can be easy realized in computer program.

Spatio-temporal change of ecosystem service value in Bosten Lake Watershed based on land use

2021 ◽  
Vol 41 (13) ◽  
Author(s):  
张发，玉素甫江·如素力，艾尔肯·图尔逊 ZHANG Fa
Keyword(s):  
Land Use ◽  
Ecosystem Service ◽  
Temporal Change ◽  
Ecosystem Service Value ◽  
Bosten Lake ◽  
Service Value ◽  
Spatio Temporal

scholarly journals Spatio-temporal change assessment using normalize difference vegetative index and spectral analysis techniques for vegetation cover on Wadi Auranah

2014 ◽  
Vol 16 (5) ◽  
pp. 955-965
Keyword(s):  
Spectral Analysis ◽  
Land Cover ◽  
Temporal Change ◽  
Treated Wastewater ◽  
Change Assessment ◽  
Field Assessment ◽  
Normalize Difference Vegetative Index ◽  
Increasing Trend ◽  
Geologic Map ◽  
Spatio Temporal

<div> <p>Wadi Auranah is one of the potential wadis in respect of land use, located on the western shield (Hijaz escarpment) of Saudi Arabia. A spatio-temporal change assessment of this wadi (valley) was conducted with the help of landsat data. The results of the spectral analysis and NDVI for vegetation changes assessment reveals a continuous increasing trend of land cover biomass in specific parts of the wadi. Geological review of geologic map supports soil fertility. It is observed from field assessment of the study area that has potential for more land more to be cultivated. About 73 km<sup>2</sup> of vegetation land cover has been increased in the last 20 years. Treated wastewater is the main source of water supply that is used for afforestation and cultivation purpose.</p> </div> <p>&nbsp;</p>

scholarly journals Seasonal patterns of dengue incidence in Thailand across the urban-rural gradient

2020 ◽  
Author(s):  
Qifang Bi ◽  
Derek AT Cummings ◽  
Nicholas G. Reich ◽  
Lindsay T. Keegan ◽  
Joshua Kaminsky ◽  
...  
Keyword(s):  
Wavelet Analysis ◽  
Temporal Variation ◽  
Rural Areas ◽  
Urban Areas ◽  
Dynamic Models ◽  
Seasonal Patterns ◽  
Disease Dynamics ◽  
Urban Rural ◽  
Spatio Temporal ◽  
Dengue Epidemics

AbstractIn Southeast Asia, endemic dengue follows strong spatio-temporal patterns with major epidemics occurring every 2-5 years. However, important spatio-temporal variation in seasonal dengue epidemics remains poorly understood. Using 13 years (2003-2015) of dengue surveillance data from 926 districts in Thailand and wavelet analysis, we show that rural epidemics lead urban epidemics within a dengue season, both nationally and within health regions. However, local dengue fade-outs are more likely in rural areas than in urban areas during the off season, suggesting rural areas are not the source of viral dispersion. Simple dynamic models show that stronger seasonal forcing in rural areas could explain the inconsistency between earlier rural epidemics and dengue “over wintering” in urban areas. These results add important nuance to earlier work showing the importance of urban areas in driving multi-annual patterns of dengue incidence in Thailand. Feedback between geographically linked locations with markedly different ecology is key to explaining full disease dynamics across urban-rural gradient.

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