scholarly journals Regional Heterogeneity of Migrant Rent Affordability Stress in Urban China: A Comparison between Skilled and Unskilled Migrants at Prefecture Level and Above

2019 ◽  
Vol 11 (21) ◽  
pp. 5920 ◽  
Author(s):  
Li ◽  
Liu ◽  
Qi
Keyword(s):  
Migrant Workers ◽  
Statistical Tests ◽  
Spatial Regression ◽  
Rental Housing ◽  
Urban China ◽  
Least Square ◽  
Housing Provision ◽  
High Rent ◽  
Income Ratio ◽  
Spatial Regression Analysis

Rental housing unaffordability has been widely used to assess the housing poverty problem among immigrants in the developed and developing countries. China is experiencing an unprecedented urbanization process, with two-thirds of its 250 million migrants now being sheltered in private rental housing in the host cities. In this paper, we aimed to examine the rental housing unaffordability problems faced by migrant workers in urban China and provide policy recommendations for a more accessible and affordable migrant housing provision system. We used the household data on China’s Migrant Dynamics Monitoring Survey (MDMS), released in 2016, across China’s 329 prefecture-level cities and above to look into the sociality and spatiality of migrant rent expenses and rent-income ratio at the prefecture-level cities and above. The statistical tests were conducted to examine the socio- and spatial-variance of these rent stress indexes, and it was found that educational level is a significant and quite powerful indicator in predicting who will or will not assume the heavier rental housing pressure. We then continued to reveal the different spatiality of high-rent-stress migrants across the high- and low-skilled categories. An agglomeration of the high-skilled high-rent-stress migrants was witnessed in the coastal growth engines of urban clusters, while a more spillover-like pattern among the low-skilled high-rent-stress migrants was reported in our study. An ordinary least square and spatial regression analysis was conducted to explain their respective mechanisms.

scholarly journals Pemodelan Indeks Pembangunan Manusia (IPM) Metode Baru Menurut Provinsi Tahun 2015 Menggunakan Geographically Weighted Regression (GWR)

2019 ◽  
Vol 2 (1) ◽  
pp. 21
Author(s):  
Akbar Maulana ◽  
Renny Meilawati ◽  
Vita Widiastuti
Keyword(s):  
Regression Analysis ◽  
Human Development ◽  
Geographically Weighted Regression ◽  
Human Development Index ◽  
Spatial Regression ◽  
Least Square ◽  
Gaussian Kernel ◽  
Weighted Regression ◽  
Development Index ◽  
Spatial Regression Analysis

<p>The Human Development Index (HDI) is a parameter of quality of life for an area. The HDI explains how residents can access the results of development in obtaining income, health and education. One method that can be used to find out the factors that influence the human development index in modeling is regression analysis of ordinary least square (OLS). In the Human Development Index data, there is a dependency between measuring data and the location of a region. Therefore, spatial regression analysis can be used in this study. The local form of spatial regression analysis is <em>geographically weighted regression</em> (GWR). GWR shows the existence of spatial heterogeneity (location). This study compares between OLS regression and GWR in the new human development index method by province in 2015. In the GWR model we use fixed Gaussian kernel and kernel fixed bisquare as weighted function. The optimal bandwidth value is obtained by minimizing the cross validation (CV) and Akaike information criterion (AIC) coefficients. The results showed that the GWR model with Gaussian kernel function is better than GWR with bisquare kernel function and OLS model.</p><p><strong>Keywords</strong><strong>: </strong>human development index, ordinary least square,<strong> </strong>geographically weighted regression, kernel fixed Gaussian,  kernel fixed bisquare</p>

scholarly journals Housing System and Urbanization in the People’s Republic of China

2019 ◽  
pp. 221-255
Author(s):  
Jie Chen
Keyword(s):  
Public Housing ◽  
Rental Housing ◽  
Urban Housing ◽  
Urban China ◽  
Housing System ◽  
Welfare Regimes ◽  
Housing Provision ◽  
Republic Of China ◽  
The Social ◽  
Urban Welfare

This chapter provides a contextualized interpretation of the transformation of housing regimes in urban China since the abolition of the urban welfare housing system in 1998. Particular attention is given to the impacts of public housing provision on China’s urbanization mode. The employment of the widely used state-market-family model is supplemented by contextualization. A close examination of the case of Public Rental Housing (PRH) in Shanghai helps to show that the recent revival of public housing in Chinese cities is mostly driven by economic growth motives. Despite that the Chinese urban housing regime up to now could be located within the context of other Asian countries’ ‘productivist’ welfare regimes, this chapter however discerns mixed evidence that it is recently shifting towards a ‘developmentalist’ regime. This investigation offers multifaceted insights on the complexity of the social-economic dynamics in post-reform urban China.

Revisiting Engels’ ‘housing question’: Work and housing conditions of immigrant platform delivery riders in Barcelona

2021 ◽  
pp. 194277862110101
Author(s):  
Prachi Metawala ◽  
Kathrin Golda-Pongratz ◽  
Clara Irazábal
Keyword(s):  
Working Conditions ◽  
Migrant Workers ◽  
Rental Housing ◽  
Food Delivery ◽  
Social Market Economy ◽  
Housing Provision ◽  
Friedrich Engels ◽  
Negative Impacts ◽  
Social Market ◽  
Platform Economy

In his 1872 The housing question, Friedrich Engels addressed the housing problems faced by the proletarian migrant workers in major industrial centres. He asserted that they could only be solved by first resolving their harsh working conditions in the capitalist mode of mass production. Presently, with transnational migrant flows to urban centres and the mass acceptance of the digital platform economy, the housing question manifests itself, among other expressions, in the case of immigrants working in this digital contract-based market. While the platform economy provides immigrants with quick access into a host country’s labour market, the income insecurity and high risks associated with such work put them in a state of precariousness. Through the framework of Engels’ proposed action lines and analysis of observations and interviews with immigrant riders working for the food delivery platforms Glovo and Deliveroo, the paper highlights the negative impacts that this contemporary capitalist model of work, the municipal housing plan and the ongoing Covid-19 crisis have on the immigrant riders’ residential and working conditions in Barcelona, Spain, a city facing a severe rental housing shortage. Lastly, it suggests that, while the social market economy in Spain can be reformed to ameliorate the negative impacts of the platform economy on immigrant riders, bridging the gap between immigrant housing provision and employment inclusion would need to consider decent labour and housing as rights for residents, immigrants included, asserting the currency of Engels’ ideas.

The Plight of Rural Migrant Workers in Urban China

2016 ◽  
Author(s):  
Rongwei Chu ◽  
James W. Gentry ◽  
Jie Fowler Gao ◽  
Xin Zhao
Keyword(s):  
Migrant Workers ◽  
Urban China ◽  
Rural Migrant ◽  
Rural Migrant Workers

scholarly journals Governing investors and developers: Analysing the role of risk allocation in urban development

Urban Studies ◽  
2021 ◽  
pp. 004209802110178
Author(s):  
Frances Brill
Keyword(s):  
Real Estate ◽  
Private Sector ◽  
Urban Development ◽  
Urban Governance ◽  
Rental Housing ◽  
Risk Allocation ◽  
Housing Provision ◽  
Institutional Investment ◽  
Housing Systems ◽  
Governance Analysis

This article argues that urban governance, and academic theorisations of it, have focused on the role and strategies of real estate developers at the expense of understanding how investors are shaped by regulatory environments. In contrast, using the case of institutional investment in London’s private rental housing (Build to Rent), in this article I argue that unpacking the private sector and the development process helps reveal different types of risk which necessitate variegated responses from within the real estate sector. In doing so, I demonstrate the complexities of the private sector in urban development, especially housing provision, and the limitations of a binary conceptualised around pro- and anti-development narratives when discussing planning decisions. Instead, I show the multiplicity of responses from within the private sector, and how these reflect particular approaches to risk management. Uncovering this helps theorise the complexities of governing housing systems and demonstrates the potential for risk-based urban governance analysis in the future.

scholarly journals Spatial analysis for political scientists

2021 ◽  
pp. 1-17
Author(s):  
Jessica Di Salvatore ◽  
Andrea Ruggeri
Keyword(s):  
Comparative Politics ◽  
Spatial Data ◽  
Spatial Clustering ◽  
Statistical Tests ◽  
Spatial Regression ◽  
Spatial Models ◽  
Building Blocks ◽  
Spatial Relationships ◽  
Electoral Studies ◽  
Data Points

Abstract How does space matter in our analyses? How can we evaluate diffusion of phenomena or interdependence among units? How biased can our analysis be if we do not consider spatial relationships? All the above questions are critical theoretical and empirical issues for political scientists belonging to several subfields from Electoral Studies to Comparative Politics, and also for International Relations. In this special issue on methods, our paper introduces political scientists to conceptualizing interdependence between units and how to empirically model these interdependencies using spatial regression. First, the paper presents the building blocks of any feature of spatial data (points, polygons, and raster) and the task of georeferencing. Second, the paper discusses what a spatial matrix (W) is, its varieties and the assumptions we make when choosing one. Third, the paper introduces how to investigate spatial clustering through visualizations (e.g. maps) as well as statistical tests (e.g. Moran's index). Fourth and finally, the paper explains how to model spatial relationships that are of substantive interest to some of our research questions. We conclude by inviting researchers to carefully consider space in their analysis and to reflect on the need, or the lack thereof, to use spatial models.

MODELLING OF 226Ra ACTIVITY CONCENTRATIONS ALONG OGUN RIVER, SOUTH WESTERN NIGERIA

2018 ◽  
Vol 14 (2) ◽  
pp. 1-14
Author(s):  
I C OKEYODE ◽  
N N JIBIRI ◽  
R BELLO
Keyword(s):  
Experimental Data ◽  
Gamma Ray ◽  
Statistical Tests ◽  
Least Square ◽  
Approximation Technique ◽  
Sediment Samples ◽  
Gamma Ray Spectrometer ◽  
Activity Concentrations ◽  
226Ra Activity ◽  
Ogun River

This work was aimed at generating a model using least square approximation technique to predict values of activity concentrations of 226Ra in any location along Ogun river in Nigeria using experimental data. Sediment samples were collected in thirty two locations along the river of about 400 km in length. NaI(Tl) gamma-ray spectrometer system was used to obtain activity concentrations of 226Ra.The aver-age value of activity concentration of 226Ra in the sediment samples from the upper region through the middle to the lower region of the river was found to be 12.65 ± 3.48 Bq/kg, having values ranging from 5.57 ± 2.36 Bq/kg (at Ekerin) to 20.40 ± 4.52 Bq/kg (at Sokori). From this work, it was observed that the generated model and experimental data could be used to predict values of activity concentrations of 226Ra in any location along the river once the latitude and longitude (position) are known. Statistical tests on the model also showed that there were no significant differences between the experimental and predicted data of 226Ra and that 98.70% of the experimental data were predicted by the model.

scholarly journals ANALISIS REGRESI SPASIAL DAN POLA PENYEBARAN PADA KASUS DEMAM BERDARAH DENGUE (DBD) DI PROVINSI JAWA TENGAH

2017 ◽  
Vol 10 (2) ◽  
pp. 95
Author(s):  
Inna Firindra Fatati ◽  
Hari Wijayanto ◽  
Agus M. Sholeh
Keyword(s):  
Spatial Regression ◽  
Spatial Models ◽  
Hemorrhagic Fever ◽  
Spatial Effect ◽  
Health Centers ◽  
Central Java ◽  
Local Moran ◽  
Spatial Regression Analysis ◽  
Different Characteristics ◽  
Central Java Province

Dengue Hemorrhagic Fever (DHF) is one of the diseases that threaten human health. The cases of dengue fever in the district / city certainly has different characteristics, geographic condition, the potential of the region, health facilities, as well as other matters that lie behind them. Based on local moran index values are visualized through thematic maps, some area adjacent quadrant tends to be in the same group. There are two significant quadrant in describing the pattern of spread of dengue cases namely quadrant high-high and lowlow. This indicates a spatial effect on the number of dengue cases, so that the spatial regression analysis. Based on the value of  and AIC, autoregressive spatial models (SAR) is good enough to be used in modeling the number of dengue cases in the province of Central Java. Factors that influence the number of dengue cases Central Java province in 2015 is the number of health centers per 1000 population, the number of polindes per 1000 population, population density (X3), percentage of people with access to drinking water sustainable decent (X6), the percentage of water quality net free of bacteria, fungi and chemicals (X7), and the number of facilities protected springs (X8).

scholarly journals Using Spatial Patterns of COVID-19 to Build a Framework for Economic Reactivation

2021 ◽  
Author(s):  
Renato Quiliche ◽  
Rafael Renteria-Ramos ◽  
Irineu de Brito Junior ◽  
Ana Luna ◽  
Mario Chong
Keyword(s):  
Spatial Patterns ◽  
Research Question ◽  
Spatial Regression ◽  
Mortality Rates ◽  
Main Research ◽  
Goods And Services ◽  
Long Run ◽  
Response And Recovery ◽  
Pandemic Response ◽  
Spatial Regression Analysis

In this article we propose an application of humanitarian logistics theory to build a supportive framework for economic reactivation and pandemic management based on province vulnerability against COVID-19. The main research question is: which factors are related to COVID-19 mortality between Peruvian provinces? We conduct a spatial regression analysis to explore which factors determines the differences in COVID-19 cumulative mortality rates for 189 Peruvian provinces up to December 2020. The most vulnerable provinces are characterized by having low outcomes of long-run poverty and high population density. Low poverty means a high economic activity that leads to more deaths of COVID-19. There is a lack of supply of a set of relief goods defined as Pandemic Response and Recovery Supportive Goods and Services (PRRSGS). These goods must be delivered in order to mitigate the risk associated to COVID-19. A supportive framework for economic reactivation can be built based on regression results and a delivery strategy can be discussed according to the spatial patterns that we found for mortality rates.

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