scholarly journals Spatial Mismatch between the Supply and Demand of Urban Leisure Services with Multisource Open Data

2020 ◽  
Vol 9 (8) ◽  
pp. 466
Author(s):  
Yue Deng ◽  
Jiping Liu ◽  
An Luo ◽  
Yong Wang ◽  
Shenghua Xu ◽  
...  
Keyword(s):  
Urban Areas ◽  
Evaluation Method ◽  
Spatial Scales ◽  
Supply And Demand ◽  
Open Data ◽  
Spatial Mismatch ◽  
Short Supply ◽  
Social Sensing ◽  
Sensing Data ◽  
Leisure Services

Understanding the balance between the supply and demand of leisure services (LSs) in urban areas can benefit urban spatial planning and improve the quality of life of residents. In cities in developing countries, the pursuit of rapid economic growth has ignored residents’ demand for LSs, thereby leading to a high demand for and short supply of these services. However, due to the lack of relevant research data, few studies have focused on the spatial mismatch in the supply and demand of LSs in urban areas. As typical representatives of multisource geographic data, social sensing data are readily available at various temporal and spatial scales, thus making social sensing data ideal for quantitative urban research. The objectives of this study are to use openly accessible datasets to explore the spatial pattern of the supply and demand of LSs in urban areas and then to depict the relationship between the supply and demand by using correlation analysis. Therefore, taking Beijing, China, as an example, the LS supply index (SI) and societal needs index (SNI) are proposed based on open data to reflect the supply and demand of LSs. The results show that the spatial distribution of the LS supply and demand in Beijing varies with a concentric pattern from the urban center to suburban areas. There is a strong correlation between the supply and demand of commercial and multifunctional services in Chaoyang, Fengtai, Haidian and Shijingshan, but there is no obvious correlation between the supply and demand of ecological and cultural services in Beijing. Especially in Dongcheng and Xicheng, there is no obvious correlation between the supply and demand of all services. The proposed approach provides an effective urban LS supply and demand evaluation method. In addition, the research results can provide a reference for the construction of “happy cities” in China.

scholarly journals A New Remote Sensing Images and Point-of-Interest Fused (RPF) Model for Sensing Urban Functional Regions

2020 ◽  
Vol 12 (6) ◽  
pp. 1032
Author(s):  
Shengyu Xu ◽  
Linbo Qing ◽  
Longmei Han ◽  
Mei Liu ◽  
Yonghong Peng ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Urban Areas ◽  
Image Data ◽  
Urban Region ◽  
Remote Sensing Images ◽  
Social Sensing ◽  
Sensing Data ◽  
Functional Regions ◽  
Point Of Interest ◽  
Wide Range

For urban planning and environmental monitoring, it is essential to understand the diversity and complexity of cities to identify urban functional regions accurately and widely. However, the existing methods developed in the literature for identifying urban functional regions have mainly been focused on single remote sensing image data or social sensing data. The multi-dimensional information which was attained from various data source and could reflect the attribute or function about the urban functional regions that could be lost in some extent. To sense urban functional regions comprehensively and accurately, we developed a multi-mode framework through the integration of spatial geographic characteristics of remote sensing images and the functional distribution characteristics of social sensing data of Point-of-Interest (POI). In this proposed framework, a deep multi-scale neural network was developed first for the functional recognition of remote sensing images in urban areas, which explored the geographic feature information implicated in remote sensing. Second, the POI function distribution was analyzed in different functional areas of the city, then the potential relationship between POI data categories and urban region functions was explored based on the distance metric. A new RPF module is further deployed to fuse the two characteristics in different dimensions and improve the identification performance of urban region functions. The experimental results demonstrated that the proposed method can efficiently achieve the accuracy of 82.14% in the recognition of functional regions. It showed the great usability of the proposed framework in the identification of urban functional regions and the potential to be applied in a wide range of areas.

scholarly journals A Spatial Ecosystem Services Assessment to Support Decision and Policy Making: The Case of the City of Bologna

2021 ◽  
Vol 13 (5) ◽  
pp. 2787
Author(s):  
Francesca Vignoli ◽  
Claudia de Luca ◽  
Simona Tondelli
Keyword(s):  
Ecosystem Services ◽  
Urban Areas ◽  
Methodological Approach ◽  
Supply And Demand ◽  
Rapid Assessment ◽  
Open Data ◽  
Canopy Cover ◽  
Tree Canopy ◽  
Tree Canopy Cover ◽  
The City

In recent years, both mapping and assessing urban Ecosystem Services (ESs) to support urban planning has been a topic of great debate. This work aims at contributing to this discussion by developing and testing a methodological approach to first assess and map supply and demand of ESs, and then identify areas of priority of intervention. Starting from the existing models, the work develops a tailored approach to map and assess three ESs (water retention and runoff, PM10 removal, and carbon sequestration and storage) that are tested in the city of Bologna and tailored according to available open data. All data are processed in a GIS environment to allow for spatial distribution and visualization of ESs. These maps facilitate defining supply and demands and, consequently, the presence and distribution of ESs deficiencies. Building on mismatches, this paper proposes four clusters by grouping the city’s districts based on predominant land use (built-up, green urban areas) and tree canopy cover. This classification enabled the identification of intervention priority areas and suggestions of relevant nature-based solutions (NBS) to be implemented. The proposed method can serve other urban areas to perform a rapid assessment of their current needs and challenges in terms of ES provision.

scholarly journals Local Population Mapping Using a Random Forest Model Based on Remote and Social Sensing Data: A Case Study in Zhengzhou, China

2020 ◽  
Vol 12 (10) ◽  
pp. 1618
Author(s):  
Ge Qiu ◽  
Yuhai Bao ◽  
Xuchao Yang ◽  
Chen Wang ◽  
Tingting Ye ◽  
...  
Keyword(s):  
Random Forest ◽  
Population Density ◽  
Urban Areas ◽  
Local Population ◽  
Population Data ◽  
Field Work ◽  
Local Planning ◽  
Statistical Tool ◽  
Social Sensing ◽  
Sensing Data

High-resolution gridded population data are important for understanding and responding to many socioeconomic and environmental problems. Local estimates of the population allow officials and researchers to make a better local planning (e.g., optimizing public services and facilities). This study used a random forest algorithm, on the basis of remote sensing (i.e., satellite imagery) and social sensing data (i.e., point-of-interest and building footprint), to disaggregate census population data for the five municipal districts of Zhengzhou city, China, onto 100 × 100 m grid cells. We used a statistical tool to detect areas with an abnormal population density; e.g., areas containing many empty houses or houses rented by more people than allowed, and conducted field work to validate our findings. Results showed that some categories of points-of-interest, such as residential communities, parking lots, banks, and government buildings were the most important contributing elements in modeling the spatial distribution of the residential population in Zhengzhou City. The exclusion of areas with an abnormal population density from model training and dasymetric mapping increased the accuracy of population estimates in other areas with a more common population density. We compared our product with three widely used gridded population products: Worldpop, the Gridded Population of the World, and the 1-km Grid Population Dataset of China. The relative accuracy of our modeling approach was higher than that of those three products in the five municipal districts of Zhengzhou. This study demonstrated potential for the combination of remote and social sensing data to more accurately estimate the population density in urban areas, with minimum disturbance from the abnormal population density.

An Agent-Based Simulation Model for Parking Variable Message Sign Location Problem

2018 ◽  
Vol 2672 (19) ◽  
pp. 135-144 ◽  
Author(s):  
Xun-You Ni ◽  
Daniel (Jian) Sun
Keyword(s):  
Urban Areas ◽  
Supply And Demand ◽  
Agent Based Simulation ◽  
Short Supply ◽  
Agent Based ◽  
Average Travel Time ◽  
Parking Lot ◽  
The One ◽  
Message Signs ◽  
Urban Parking

Parking spaces are often in short supply in urban areas. To balance the supply and demand and alleviate the overconsumption of public spaces, parking variable message signs (parking VMSs) are commonly used to release information on space availability to drivers en route. The aim of this study was to find the optimal positions for parking VMSs. To achieve the objective, we first define the major decision point (MDP) as the intersection where the newly generated path deviates from the previous one. When informed that the target parking lot is fully occupied, the driver would divert to an alternative one. The route to the alternative parking lot is indicated as the newly generated path, while the one leading to the original parking lot is denoted as the previous one. Quantitatively, MDPs with the highest frequency of occurrence are selected as the candidate positions. Then, an agent-based simulation is proposed to identify the MDPs induced by changes of space availability and the selection of routes. The results indicate that the proposed location algorithm slightly outperforms the scheme with the completed parking information in terms of average travel time and average travel distance. The algorithm can be further integrated into a simulation package, which may assist in the design and operation of an urban parking guidance and information system.

scholarly journals Integration of Remote Sensing and Social Sensing Data in a Deep Learning Framework for Hourly Urban PM2.5 Mapping

2019 ◽  
Vol 16 (21) ◽  
pp. 4102 ◽  
Author(s):  
Shen ◽  
Zhou ◽  
Li ◽  
Zeng
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Urban Areas ◽  
Heterogeneous Data ◽  
Research Strategy ◽  
Concentration Data ◽  
Social Sensing ◽  
Sensing Data ◽  
Nonlinear Relationships ◽  
Pm2.5 Concentration

Fine spatiotemporal mapping of PM2.5 concentration in urban areas is of great significance in epidemiologic research. However, both the diversity and the complex nonlinear relationships of PM2.5 influencing factors pose challenges for accurate mapping. To address these issues, we innovatively combined social sensing data with remote sensing data and other auxiliary variables, which can bring both natural and social factors into the modeling; meanwhile, we used a deep learning method to learn the nonlinear relationships. The geospatial analysis methods were applied to realize effective feature extraction of the social sensing data and a grid matching process was carried out to integrate the spatiotemporal multi-source heterogeneous data. Based on this research strategy, we finally generated hourly PM2.5 concentration data at a spatial resolution of 0.01°. This method was successfully applied to the central urban area of Wuhan in China, which the optimal result of the 10-fold cross-validation R2 was 0.832. Our work indicated that the real-time check-in and traffic index variables can improve both quantitative and mapping results. The mapping results could be potentially applied for urban environmental monitoring, pollution exposure assessment, and health risk research.

scholarly journals Understanding policing demand and deployment through the lens of the city and with the application of big data

Urban Studies ◽  
2021 ◽  
pp. 004209802098100
Author(s):  
Mark Ellison ◽  
Jon Bannister ◽  
Won Do Lee ◽  
Muhammad Salman Haleem
Keyword(s):  
Big Data ◽  
Urban Environment ◽  
Urban Areas ◽  
Multilevel Model ◽  
Open Data ◽  
Front Line ◽  
Criminological Research ◽  
The Social ◽  
Generalised Method Of Moments ◽  
The City

The effective, efficient and equitable policing of urban areas rests on an appreciation of the qualities and scale of, as well as the factors shaping, demand. It also requires an appreciation of the factors shaping the resources deployed in their address. To this end, this article probes the extent to which policing demand (crime, anti-social behaviour, public safety and welfare) and deployment (front-line resource) are similarly conditioned by the social and physical urban environment, and by incident complexity. The prospect of exploring policing demand, deployment and their interplay is opened through the utilisation of big data and artificial intelligence and their integration with administrative and open data sources in a generalised method of moments (GMM) multilevel model. The research finds that policing demand and deployment hold varying and time-sensitive association with features of the urban environment. Moreover, we find that the complexities embedded in policing demands serve to shape both the cumulative and marginal resources expended in their address. Beyond their substantive policy relevance, these findings serve to open new avenues for urban criminological research centred on the consideration of the interplay between policing demand and deployment.

scholarly journals Reliability Estimation of Reinforced Slopes to Prioritize Maintenance Actions

2021 ◽  
Vol 18 (2) ◽  
pp. 373
Author(s):  
Farshad BahooToroody ◽  
Saeed Khalaj ◽  
Leonardo Leoni ◽  
Filippo De Carlo ◽  
Gianpaolo Di Bona ◽  
...  
Keyword(s):  
Urban Areas ◽  
Evaluation Method ◽  
Likelihood Function ◽  
Reliability Estimation ◽  
Analytical Models ◽  
Geotechnical Structures ◽  
Reinforced Slopes ◽  
The Stability ◽  
Methods Analytical ◽  
Asset Managers

Geosynthetics are extensively utilized to improve the stability of geotechnical structures and slopes in urban areas. Among all existing geosynthetics, geotextiles are widely used to reinforce unstable slopes due to their capabilities in facilitating reinforcement and drainage. To reduce settlement and increase the bearing capacity and slope stability, the classical use of geotextiles in embankments has been suggested. However, several catastrophic events have been reported, including failures in slopes in the absence of geotextiles. Many researchers have studied the stability of geotextile-reinforced slopes (GRSs) by employing different methods (analytical models, numerical simulation, etc.). The presence of source-to-source uncertainty in the gathered data increases the complexity of evaluating the failure risk in GRSs since the uncertainty varies among them. Consequently, developing a sound methodology is necessary to alleviate the risk complexity. Our study sought to develop an advanced risk-based maintenance (RBM) methodology for prioritizing maintenance operations by addressing fluctuations that accompany event data. For this purpose, a hierarchical Bayesian approach (HBA) was applied to estimate the failure probabilities of GRSs. Using Markov chain Monte Carlo simulations of likelihood function and prior distribution, the HBA can incorporate the aforementioned uncertainties. The proposed method can be exploited by urban designers, asset managers, and policymakers to predict the mean time to failures, thus directly avoiding unnecessary maintenance and safety consequences. To demonstrate the application of the proposed methodology, the performance of nine reinforced slopes was considered. The results indicate that the average failure probability of the system in an hour is 2.8×10−5 during its lifespan, which shows that the proposed evaluation method is more realistic than the traditional methods.

scholarly journals Estimation of the temperature distribution in urban areas by using remote sensing data.

1994 ◽  
Vol 22 ◽  
pp. 267-273 ◽  
Author(s):  
Shinji KANEKO ◽  
Toshiie MAEDA ◽  
Takahito UENO ◽  
Hidefumi IMURA
Keyword(s):  
Remote Sensing ◽  
Temperature Distribution ◽  
Urban Areas ◽  
Remote Sensing Data ◽  
Sensing Data

Coping with Spatial Mismatch: Subsidy Design for Electric Vehicle and Charging Markets

2021 ◽  
Author(s):  
Wei Zhang ◽  
Yifan Dou
Keyword(s):  
Electric Vehicle ◽  
Service Providers ◽  
Supply And Demand ◽  
Problem Definition ◽  
Environmental Benefits ◽  
Third Party ◽  
Theoretic Model ◽  
Spatial Mismatch ◽  
The Government ◽  
Ev Charging

Problem definition: We study how the government should design the subsidy policy to promote electric vehicle (EV) adoptions effectively and efficiently when there might be a spatial mismatch between the supply and demand of charging piles. Academic/practical relevance: EV charging infrastructures are often built by third-party service providers (SPs). However, profit-maximizing SPs might prefer to locate the charging piles in the suburbs versus downtown because of lower costs although most EV drivers prefer to charge their EVs downtown given their commuting patterns and the convenience of charging in downtown areas. This conflict of spatial preferences between SPs and EV drivers results in high overall costs for EV charging and weak EV adoptions. Methodology: We use a stylized game-theoretic model and compare three types of subsidy policies: (i) subsidizing EV purchases, (ii) subsidizing SPs based on pile usage, and (iii) subsidizing SPs based on pile numbers. Results: Subsidizing EV purchases is effective in promoting EV adoptions but not in alleviating the spatial mismatch. In contrast, subsidizing SPs can be more effective in addressing the spatial mismatch and promoting EV adoptions, but uniformly subsidizing pile installation can exacerbate the spatial mismatch and backfire. In different situations, each policy can emerge as the best, and the rule to determine which side (SPs versus EV buyers) to subsidize largely depends on cost factors in the charging market rather than the EV price or the environmental benefits. Managerial implications: A “jigsaw-piece rule” is recommended to guide policy design: subsidizing SPs is preferred if charging is too costly or time consuming, and subsidizing EV purchases is preferred if charging is sufficiently fast and easy. Given charging costs that are neither too low nor too high, subsidizing SPs is preferred only if pile building downtown is moderately more expensive than pile building in the suburbs.

scholarly journals Health in Brazil: examples for the world

2013 ◽  
Vol 13 (1) ◽  
pp. 51-54 ◽  
Author(s):  
Malaquias Batista Filho ◽  
Anete Rissin
Keyword(s):  
Urban Areas ◽  
Health Care Services ◽  
Supply And Demand ◽  
Rural Families ◽  
Health Sector ◽  
Care Services ◽  
Intervention Models ◽  
History Of ◽  
Demand For Health ◽  
Information Framework

In the year 2012, for the first time in the history of humanity, the urban population has exceeded the rural population. This change has been conditioned, in large part, by migratory flows in the direction of the field to the cities, singularizing the importance of the situation according to epidemiological, ecological, political, and social aspects. These issues are highlighted by the United Nations (UNICEF and WHO) especially considering the remarkable and growing relevance that the poverty condition of rural families exercises in this displacement, creating a remarkable adverse and conflictive environment, mainly in the health sector. This fact occurs because the infrastructure of urban services is not keeping up with the sprawls in the outskirts of the cities of medium and large sizes. These arguments, of universal character, assume a crucial importance in developing countries, as in the case of Brazil, Latin America, an Asian subcontinent and the greater part of Africa. It is a context that justifies the I Brazilian Workshop on the Health of Subnormal Urban Clusters (old slums) to be held in Recife, as a strategy to consolidate a basic information framework about the epidemiological scenario, the supply and demand for health care services in urban areas of poverty. With an propositional objective: establish an agenda for research and intervention models having as focus the priorities of health of these urban spaces submitted to socio-economic conditions of recognized vulnerability.

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