scholarly journals Perspectives of Socio-Spatial Differentiation from Soaring Housing Prices: A Case Study in Nanjing, China

2019 ◽  
Vol 11 (9) ◽  
pp. 2627 ◽  
Author(s):  
Chunhui Liu ◽  
Weixuan Song
Keyword(s):  
Rapid Growth ◽  
Growth Models ◽  
Housing Prices ◽  
Spatial Differentiation ◽  
Urban Housing ◽  
Spatial Effects ◽  
Negative Effects ◽  
Residential Housing ◽  
Different Types ◽  
Differentiation Pattern

Launched in 1998, the market-oriented reform of urban housing has given urban housing the dual attributes of residence and investment, and led to the rapid growth of housing prices as well as the intensification of its spatial differentiation within cities. However, the spatial patterns of the differentiation and its mechanism as well as socio-spatial effects are rarely touched. This paper studies 3963 urban residential districts in central Nanjing and explores the socio-spatial differentiation pattern and process of the urban housing prices and its growth in Nanjing based on the sample data of housing transactions over 30 quarters during the period of 2009–2017. The paper concludes that, by splitting the research duration into phases of six quarters each, the average housing prices in Nanjing alternates between “rapid growth” and “relatively stable” phases. At the same time, this paper finds that the spatial heterogeneity of housing prices in the city has been enhanced constantly, and the price gap in different types of residential housing has been clearly widened. In combination with the price level, location characteristics and architectural attributes of residential districts, this paper has categorized housing in Nanjing into nine typical types in a comprehensive manner. Based on the differences in their spatial attributes such as location, comfort and scarcity etc., different types of residences exhibit different pricing and price-to-rent ratio growth models. Based on those findings, we discussed the mechanism of the socio-spatial differentiation of housing prices in Nanjing from the housing reform and strategies of urban renewal and expansion. Beyond that, we discussed the role of urban housing consumption in the process of (re)production of urban classes, and its negative effects on urban young people, rural immigrants and other disadvantaged families. At the end of the paper, the policy suggestion about the supply-side reform of the housing market to promote socio-spatial equity and sustainable development is also presented.

scholarly journals Temporal and Spatial Effects of Urban Center on Housing Price — A Case Study on Hangzhou, China

2018 ◽  
Vol 5 (1) ◽  
pp. 89 ◽  
Author(s):  
Luhong Chu ◽  
Haizhen Wen
Keyword(s):  
Rapid Development ◽  
Housing Prices ◽  
Housing Price ◽  
Urban Land ◽  
Urban Housing ◽  
Spatial Effects ◽  
Hedonic Price ◽  
Urban Center ◽  
Center City ◽  
The Impact

<em>With the acceleration of urbanization and the rapid development of real estate, people pay more and more attention to the change of urban housing prices. Over time, the change of city center will inevitably affect the urban land or housing prices, which is reflected in the spatial distribution of urban land or housing prices. Therefore, this article attempts to explore the impact of urban center on housing prices from the perspective of multi-center city and study separately from two aspects of time and space. This paper takes the six main urban districts of Hangzhou as the research scope. At the time level, we select the residential data from 2007 to 2015 to construct models respectively based on the hedonic price theory and find that the influence of different urban center on housing price shows a certain change with time. On the spatial level, this paper choses the residential data in 2012 to construct geographic weighted regression model and the result shows that the impact of three centers on housing prices shows a certain degree of spatial heterogeneity.</em>

scholarly journals An Agent-Based Crop Model Framework for Heterogeneous Soils

Agronomy ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 85
Author(s):  
Jorge Lopez-Jimenez ◽  
Nicanor Quijano ◽  
Alain Vande Wouwer
Keyword(s):  
Growth Models ◽  
Control Strategies ◽  
Low Cost ◽  
Soil Heterogeneity ◽  
Crop Growth ◽  
Negative Effects ◽  
Model Framework ◽  
Agent Based ◽  
Crop Growth Models ◽  
Definition Of

Climate change and the efficient use of freshwater for irrigation pose a challenge for sustainable agriculture. Traditionally, the prediction of agricultural production is carried out through crop-growth models and historical records of the climatic variables. However, one of the main flaws of these models is that they do not consider the variability of the soil throughout the cultivation area. In addition, with the availability of new information sources (i.e., aerial or satellite images) and low-cost meteorological stations, it is convenient that the models incorporate prediction capabilities to enhance the representation of production scenarios. In this work, an agent-based model (ABM) that considers the soil heterogeneity and water exchanges is proposed. Soil heterogeneity is associated to the combination of individual behaviours of uniform portions of land (agents), while water fluxes are related to the topography. Each agent is characterized by an individual dynamic model, which describes the local crop growth. Moreover, this model considers positive and negative effects of water level, i.e., drought and waterlogging, on the biomass production. The development of the global ABM is oriented to the future use of control strategies and optimal irrigation policies. The model is built bottom-up starting with the definition of agents, and the Python environment Mesa is chosen for the implementation. The validation is carried out using three topographic scenarios in Colombia. Results of potential production cases are discussed, and some practical recommendations on the implementation are presented.

scholarly journals Not all biases are bad: equitable and inequitable biases in machine learning and radiology

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Mirjam Pot ◽  
Nathalie Kieusseyan ◽  
Barbara Prainsack
Keyword(s):  
Machine Learning ◽  
Human Error ◽  
Technical Problem ◽  
Social Dimension ◽  
Patient Treatment ◽  
Technical Solution ◽  
Negative Effects ◽  
Different Types ◽  
Human Decision

AbstractThe application of machine learning (ML) technologies in medicine generally but also in radiology more specifically is hoped to improve clinical processes and the provision of healthcare. A central motivation in this regard is to advance patient treatment by reducing human error and increasing the accuracy of prognosis, diagnosis and therapy decisions. There is, however, also increasing awareness about bias in ML technologies and its potentially harmful consequences. Biases refer to systematic distortions of datasets, algorithms, or human decision making. These systematic distortions are understood to have negative effects on the quality of an outcome in terms of accuracy, fairness, or transparency. But biases are not only a technical problem that requires a technical solution. Because they often also have a social dimension, the ‘distorted’ outcomes they yield often have implications for equity. This paper assesses different types of biases that can emerge within applications of ML in radiology, and discusses in what cases such biases are problematic. Drawing upon theories of equity in healthcare, we argue that while some biases are harmful and should be acted upon, others might be unproblematic and even desirable—exactly because they can contribute to overcome inequities.

scholarly journals The role and significance of planning in the determination of house prices in Australia: Recent policy debates

2021 ◽  
pp. 0308518X2198894
Author(s):  
Peter Phibbs ◽  
Nicole Gurran
Keyword(s):  
Land Use ◽  
Housing Market ◽  
Land Use Planning ◽  
House Prices ◽  
Housing Prices ◽  
House Price ◽  
Evidence Base ◽  
Urban Housing ◽  
Price Inflation ◽  
Depth Analysis

On the world stage, Australian cities have been punching above their weight in global indexes of housing prices, sparking heated debates about the causes of and remedies for, sustained house price inflation. This paper examines the evidence base underpinning such debates, and the policy claims made by key commentators and stakeholders. With reference to the wider context of Australia’s housing market over a 20 year period, as well as an in depth analysis of a research paper by Australia’s central Reserve Bank, we show how economic theories commonly position land use planning as a primary driver of new supply constraints but overlook other explanations for housing market behavior. In doing so, we offer an alternative understanding of urban housing markets and land use planning interventions as a basis for more effective policy intervention in Australian and other world cities.

scholarly journals Assessing the impacts of Airbnb listings on London house prices

2021 ◽  
pp. 239980832110018
Author(s):  
James Todd ◽  
Anwar Musah ◽  
James Cheshire
Keyword(s):  
Random Effects ◽  
Rapid Growth ◽  
House Prices ◽  
Housing Prices ◽  
House Price ◽  
Positive Association ◽  
Sharing Economy ◽  
Local Context ◽  
Fixed And Random Effects ◽  
The World

Over the course of the last decade, sharing economy platforms have experienced significant growth within cities around the world. Airbnb, which is one of the largest and best-known platforms, provides the focus for this paper and offers a service that allows users to rent properties or spare rooms to guests. Its rapid growth has led to a growing discourse around the consequences of Airbnb rentals within the local context. The research within this paper focuses on determining impact on local housing prices within the inner London boroughs by constructing a longitudinal panel dataset, on which a fixed and random effects regression was conducted. The results indicate that there is a significant and modest positive association between the frequency of Airbnb and the house price per square metre in these boroughs.

Exploring a multilevel approach with spatial effects to model housing price in San José, Costa Rica

2021 ◽  
pp. 239980832110411
Author(s):  
Eduardo Pérez-Molina
Keyword(s):  
Costa Rica ◽  
Multilevel Models ◽  
Housing Prices ◽  
Housing Price ◽  
Autoregressive Models ◽  
Metropolitan Region ◽  
Spatial Effects ◽  
San Jose ◽  
Conditional Autoregressive ◽  
Conditional Autoregressive Models

A multilevel model of the housing market for San José Metropolitan Region (Costa Rica) was developed, including spatial effects. The model is used to explore two main questions: the extent to which contextual (of the surroundings) and compositional (of the property itself) effects explain variation of housing prices and how does the relation between price and key covariates change with the introduction of multilevel effects. Hierarchical relations (lower level units nested into higher level) were modeled by specifying multilevel models with random intercepts and a conditional autoregressive term to include spatial effects from neighboring units at the higher level (districts). The random intercepts and conditional autoregressive models presented the best fit to the data. Variation at the higher level accounted for 16% of variance in the random intercepts model and 28% in the conditional autoregressive model. The sign and magnitude of regression coefficients proved remarkably stable across model specifications. Travel time to the city center, which presented a non-linear relation to price, was found to be the most important determinant. Multilevel and conditional autoregressive models constituted important improvements in modeling housing price, despite most of the variation still occurring at the lower level, by improving the overall model fit. They were capable of representing the regional structure and of reducing sampling bias in the data. However, the conditional autoregressive specification only represented a limited advance over the random intercepts formulation.

Shape-Tailored TiO2 Photocatalysts Obtained in the Presence of Different Types of Carbon Materials

2021 ◽  
Vol 21 (4) ◽  
pp. 2360-2367
Author(s):  
Krisztina Vajda ◽  
Klára Hernádi ◽  
Cosmin Coteţ ◽  
Gábor Kovács ◽  
Zsolt Pap
Keyword(s):  
Carbon Materials ◽  
Uv Light ◽  
Carbon Aerogel ◽  
Low Carbon ◽  
Negative Effects ◽  
Tio2 Photocatalysts ◽  
Uv Light Irradiation ◽  
Specific Morphology ◽  
Different Types ◽  
Photocatalytic Applications

Titania and carbon materials are intensively studied in composite materials including photocatalytic applications. Both positive and negative effects were described in the literature, including charge separation, adsorption enhancement and short-circuiting of the photoelectrons as well. In the present study a more sparsely investigated properties of carbon materials will be highlighted, namely their role as crystallization promoters for titania, during hydrothermal synthesis of the composites. Therefore, carbon nanotubes, carbon coils, activated carbon, graphite and carbon aerogel was used to identify the importance of carbon during the time dependent crystallization of titanium dioxide. The crystal phase composition, morphology, optical properties and photocatalytic activity was followed, and it was found that the anatase and rutile crystallization depended on the used carbon material. The morphology of the particles varied from single anatase sheet-like crystals to hierarchical microball-like structures, while in some cases no specific morphology was observed. Furthermore, it was found that despite the low carbon content (2 wt.%) and microcrystalline structure of TiO2 the composites were proven to be efficient in the degradation of Rhodamine B under UV light irradiation.

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