scholarly journals ESTIMATING HOUSING VACANCY RATE IN QINGDAO CITY WITH NPP-VIIRS NIGHTTIME LIGHT AND GEOGRAPHICAL NATIONAL CONDITIONS MONITORING DATA

2018 ◽  
Vol XLII-3 ◽  
pp. 1319-1326 ◽  
Author(s):  
X. Niu
Keyword(s):  
Population Distribution ◽  
Monitoring Data ◽  
Basic Unit ◽  
Distribution Data ◽  
Vacancy Rate ◽  
Rapid Urbanization ◽  
Nighttime Light ◽  
Qingdao City ◽  
The City ◽  
Housing Vacancy

Accompanying China's rapid urbanization in recent decades, especially in the new millennium, the housing problem has become one of the most important issues. The estimation and analysis of housing vacancy rate (HVR) can assist decision-making in solving this puzzle. It is particularly significant to government departments. This paper proposed a practical model for estimating the HVR in Qingdao city using NPP-VIIRS nighttime light composed data, Geographic National Conditions Monitoring data (GNCMD) and resident population distribution data. The main steps are: Firstly, pre-process the data, and finally forming a series of data sets with 500*500 grid as the basic unit; Secondly, select 400 grids of different types within the city as sample grids for SVM training, and establish a reasonable HVR model; Thirdly, using the model to estimate HVR in Qingdao and employing spatial statistical analysis methods to reveal the spatial differentiation pattern of HVR in this city; Finally test the accuracy of the model with two different methods. The results conclude that HVR in the southeastern coastal area of Qingdao city is relatively low and the low-low clusters distributed in patches. Simultaneously, in other regions it shows the tendency of the low value accumulation in the downtown area and the increasing trend towards the outer suburbs. Meanwhile the suburban and scenery regions by the side of the sea and mountains are likely to be the most vacant part of the city.

scholarly journals Modeling Spatiotemporal Population Changes by Integrating DMSP-OLS and NPP-VIIRS Nighttime Light Data in Chongqing, China

2021 ◽  
Vol 13 (2) ◽  
pp. 284
Author(s):  
Dan Lu ◽  
Yahui Wang ◽  
Qingyuan Yang ◽  
Kangchuan Su ◽  
Haozhe Zhang ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Relative Error ◽  
Urban Areas ◽  
Large Scale ◽  
Population Distribution ◽  
Spatial Optimization ◽  
Distribution Data ◽  
Mountainous Areas ◽  
Mean Relative Error ◽  
Nighttime Light

The sustained growth of non-farm wages has led to large-scale migration of rural population to cities in China, especially in mountainous areas. It is of great significance to study the spatial and temporal pattern of population migration mentioned above for guiding population spatial optimization and the effective supply of public services in the mountainous areas. Here, we determined the spatiotemporal evolution of population in the Chongqing municipality of China from 2000–2018 by employing multi-period spatial distribution data, including nighttime light (NTL) data from the Defense Meteorological Satellite Program’s Operational Linescan System (DMSP-OLS) and the Suomi National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS). There was a power function relationship between the two datasets at the pixel scale, with a mean relative error of NTL integration of 8.19%, 4.78% less than achieved by a previous study at the provincial scale. The spatial simulations of population distribution achieved a mean relative error of 26.98%, improved the simulation accuracy for mountainous population by nearly 20% and confirmed the feasibility of this method in Chongqing. During the study period, the spatial distribution of Chongqing’s population has increased in the west and decreased in the east, while also increased in low-altitude areas and decreased in medium-high altitude areas. Population agglomeration was common in all of districts and counties and the population density of central urban areas and its surrounding areas significantly increased, while that of non-urban areas such as northeast Chongqing significantly decreased.

scholarly journals An Improved Method of Determining Human Population Distribution Based on Luojia 1-01 Nighttime Light Imagery and Road Network Data—A Case Study of the City of Shenzhen

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5032 ◽  
Author(s):  
Qiang Zhou ◽  
Yuanmao Zheng ◽  
Jinyuan Shao ◽  
Yinglun Lin ◽  
Haowei Wang
Keyword(s):  
Spatial Distribution ◽  
Population Density ◽  
Spatial Resolution ◽  
Urban Population ◽  
Road Network ◽  
Population Distribution ◽  
Small Scale ◽  
Human Settlement ◽  
Nighttime Light ◽  
The City

Previously published studies on population distribution were based on the provincial level, while the number of urban-level studies is more limited. In addition, the rough spatial resolution of traditional nighttime light (NTL) data has limited their fine application in current small-scale population distribution research. For the purpose of studying the spatial distribution of populations at the urban scale, we proposed a new index (i.e., the road network adjusted human settlement index, RNAHSI) by integrating Luojia 1-01 (LJ 1-01) NTL data, the enhanced vegetation index (EVI), and road network density (RND) data based on population density relationships to depict the spatial distribution of urban human settlements. The RNAHSI updated the high-resolution NTL data and combined the RND data on the basis of human settlement index (HSI) data to refine the spatial pattern of urban population distribution. The results indicated that the mean relative error (MRE) between the population estimation data based on the RNAHSI and the demographic data was 34.80%, which was lower than that in the HSI and WorldPop dataset. This index is suitable primarily for the study of urban population distribution, as the RNAHSI can clearly highlight human activities in areas with dense urban road networks and can refine the spatial heterogeneity of impervious areas. In addition, we also drew a population density map of the city of Shenzhen with a 100 m spatial resolution for 2018 based on the RNAHSI, which has great reference significance for urban management and urban resource allocation.

scholarly journals Housing Vacancy Rate in Major Cities in China: Perspectives from Nighttime Light Data

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Zhiru Tan ◽  
Donglan Wei ◽  
Zixu Yin
Keyword(s):  
Data Center ◽  
Housing Prices ◽  
Vacancy Rate ◽  
Nighttime Light Data ◽  
Building Information ◽  
Nighttime Light ◽  
Vacancy Rates ◽  
Second Tier ◽  
Nighttime Light Imagery ◽  
Housing Vacancy

In recent years, the phenomenon of housing vacancy rate (HVR) has attracted considerable attention, especially concerning unjustified expansions of Chinese cities. The aforementioned trend is disadvantageous in that it will ultimately lead to tremendous wastage of valuable land that could otherwise be more productively utilized. Consequently, the methods for accurately determining the HVR are of great importance. Based on nighttime light data from the Luojia 1-01 nighttime light imagery provided by Wuhan University in June 2018 and the building data obtained from the Resources and Environmental Sciences Data Center, we estimated the HVRs of 49 cities in China by determining the building areas and considering the floor height. The results revealed that (1) the lowest (15%) and highest (24.3%) HVRs occur in Shenzhen and Nanning, respectively. (2) The urban HVR correlates positively with the three production structures (0.3143) but is significantly negatively correlated with population (0.3841), GDP (0.6139), and urban average housing prices (0.5083). (3) The first-tier, new first-tier, and second-tier cities showed the lowest (16.9%), relatively concentrated (20.5%), and highest (21.3%) average vacancy rates, respectively. (4) The vacancy rate is relatively low in the eastern coastal areas, whereas high in the northeast and western inland areas. The proposed method can help urban planners by identifying vacant areas and providing building information.

scholarly journals Real Time Multipurpose Smart Waste Classification Model for Efficient Recycling in Smart Cities Using Multilayer Convolutional Neural Network and Perceptron

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4916
Author(s):  
Ali Usman Gondal ◽  
Muhammad Imran Sadiq ◽  
Tariq Ali ◽  
Muhammad Irfan ◽  
Ahmad Shaf ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Real Time ◽  
Multilayer Perceptron ◽  
Smart Cities ◽  
Classification Model ◽  
Rapid Urbanization ◽  
Metal Waste ◽  
Waste Classification ◽  
The City

Urbanization is a big concern for both developed and developing countries in recent years. People shift themselves and their families to urban areas for the sake of better education and a modern lifestyle. Due to rapid urbanization, cities are facing huge challenges, one of which is waste management, as the volume of waste is directly proportional to the people living in the city. The municipalities and the city administrations use the traditional wastage classification techniques which are manual, very slow, inefficient and costly. Therefore, automatic waste classification and management is essential for the cities that are being urbanized for the better recycling of waste. Better recycling of waste gives the opportunity to reduce the amount of waste sent to landfills by reducing the need to collect new raw material. In this paper, the idea of a real-time smart waste classification model is presented that uses a hybrid approach to classify waste into various classes. Two machine learning models, a multilayer perceptron and multilayer convolutional neural network (ML-CNN), are implemented. The multilayer perceptron is used to provide binary classification, i.e., metal or non-metal waste, and the CNN identifies the class of non-metal waste. A camera is placed in front of the waste conveyor belt, which takes a picture of the waste and classifies it. Upon successful classification, an automatic hand hammer is used to push the waste into the assigned labeled bucket. Experiments were carried out in a real-time environment with image segmentation. The training, testing, and validation accuracy of the purposed model was 0.99% under different training batches with different input features.

scholarly journals Trends and dynamics of material and energy flows in an urban context: a case study of a city with an emerging economy

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Clara Inés Pardo Martínez ◽  
William Alfonso Piña ◽  
Angelo Facchini ◽  
Alexander Cotte Poveda
Keyword(s):  
Latin America ◽  
Population Growth ◽  
Construction Materials ◽  
The Other ◽  
Material Flows ◽  
Urban Context ◽  
Rapid Urbanization ◽  
Urban Agglomerations ◽  
Energy Flows ◽  
The City

Abstract Background Currently, most of the world’s population lives in cities, and the rapid urbanization of the population is driving increases in the demand for products, goods and services. To effectively design policies for urban sustainability, it is important to understand the trends of flows in energy and materials as they enter and leave a city. This knowledge is essential for determining the key elements characterizing future urban growth and addressing future supply challenges. Methods This paper presents an analysis of the energy and material flows in the city of Bogotá over the time span from 2001 to 2017. Urban flows are also characterized in terms of their temporal evolution with respect to population growth to compare and identify the changes in the main input flows, wealth production, emissions and waste in the city. Results The results of the analysis are then compared with those for other selected large urban agglomerations in Latin America and worldwide to highlight similarities and make inferences. The results show that in Bogotá, there was a decrease in some of the material flows, such as the consumption of water and the generation of discharge, in recent years, while there was an increase in the consumption of energy and cement and in the production of CO2 emissions and construction materials. Solid waste production remained relatively stable. With respect to the other large cities considered, we observe that the 10-year growth rates of the flows with respect to population growth are lower in Bogotá, particularly when compared with the other urban agglomerations in Latin America. Conclusions The findings of this study are important for advancing characterizations of the trends of material and energy flows in cities, and they contribute to the establishment of a benchmark that allows for the definition and evaluation of the different impacts of public policy while promoting the sustainability of Bogotá in the coming decades.

scholarly journals Seasonal Variations of Daytime Land Surface Temperature and Their Underlying Drivers over Wuhan, China

2021 ◽  
Vol 13 (2) ◽  
pp. 323
Author(s):  
Liang Chen ◽  
Xuelei Wang ◽  
Xiaobin Cai ◽  
Chao Yang ◽  
Xiaorong Lu
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Maximum Temperature ◽  
Maximum Temperature Difference ◽  
Rapid Urbanization ◽  
The Impact ◽  
The City

Rapid urbanization greatly alters land surface vegetation cover and heat distribution, leading to the development of the urban heat island (UHI) effect and seriously affecting the healthy development of cities and the comfort of living. As an indicator of urban health and livability, monitoring the distribution of land surface temperature (LST) and discovering its main impacting factors are receiving increasing attention in the effort to develop cities more sustainably. In this study, we analyzed the spatial distribution patterns of LST of the city of Wuhan, China, from 2013 to 2019. We detected hot and cold poles in four seasons through clustering and outlier analysis (based on Anselin local Moran’s I) of LST. Furthermore, we introduced the geographical detector model to quantify the impact of six physical and socio-economic factors, including the digital elevation model (DEM), index-based built-up index (IBI), modified normalized difference water index (MNDWI), normalized difference vegetation index (NDVI), population, and Gross Domestic Product (GDP) on the LST distribution of Wuhan. Finally, to identify the influence of land cover on temperature, the LST of croplands, woodlands, grasslands, and built-up areas was analyzed. The results showed that low temperatures are mainly distributed over water and woodland areas, followed by grasslands; high temperatures are mainly concentrated over built-up areas. The maximum temperature difference between land covers occurs in spring and summer, while this difference can be ignored in winter. MNDWI, IBI, and NDVI are the key driving factors of the thermal values change in Wuhan, especially of their interaction. We found that the temperature of water area and urban green space (woodlands and grasslands) tends to be 5.4 °C and 2.6 °C lower than that of built-up areas. Our research results can contribute to the urban planning and urban greening of Wuhan and promote the healthy and sustainable development of the city.

Towards a More Resilient Delhi: Rapid Urbanization and Climate Change

2018 ◽  
Vol 05 (02n03) ◽  
pp. 1850014
Author(s):  
Jasdeep Singh
Keyword(s):  
Research Question ◽  
Healthcare Services ◽  
Global South ◽  
Assessment Tools ◽  
Capital City ◽  
Sample Population ◽  
Rapid Urbanization ◽  
Apparent Lack ◽  
The City ◽  
Resilience Assessment

The discourse on resilient cities encapsulates various analogies, which are further constructed through the work of researchers in creation of several resilience assessment methodologies and toolkits. Despite the presence of numerous resilience assessment tools, there is an apparent lack of participation of residents of the global south within the assessment and iterative transformation processes. The situation, hence, is not truly represented through application of these tools in certain socio-political climates such as of India. Consistent economic growth of India has resulted in rapid urbanization of major cities. But, this has not been supplemented with proper planning, resulting in imbalances in all spheres of city infrastructure. Delhi, capital city of India, has been one of the worst hit cities. The hot seasons have caused thousands of fatalities in the past few years. An attempt is made to review the application of current resilience tools in Delhi against the backdrop of the sustainable development goals. In an attempt to improve the approach of these existing tools, an initial iteration is conducted, hinging on qualitative data obtained through surveying a sample population of the city and accessible quantitative metric data. Possible intervention scenarios are further suggested in view of aforementioned stressors and resilience scores. Research question: Where are the current resilience tools found lacking in the case of the global south, specifically in Delhi? How can the applicability of these tools be improved without compromising the deliverables yet ensuring an all-inclusive approach? Key findings: (1) The city is found lacking in adequate infrastructure facilities to its residents especially within the ambits of basic water and sanitation provision and healthcare services. (2) The city is relatively unprepared to face unforeseen events, both at the administrative and the grassroots levels. The lack of knowledge transfer and cooperation are largely evident.

Do Housing Vacancies Induce More Crime? A Spatiotemporal Regression Analysis

2019 ◽  
Vol 66 (11) ◽  
pp. 1579-1605 ◽  
Author(s):  
Xiaojin Chen ◽  
Patrick Rafail
Keyword(s):  
Regression Analysis ◽  
Collective Efficacy ◽  
Large Scale ◽  
Spillover Effect ◽  
Property Crime ◽  
Drug Offense ◽  
Community Based Programs ◽  
The City ◽  
Housing Vacancy ◽  
Neighborhood Level

This study aims to investigate the longitudinal associations between patterns of housing vacancies, neighborhood social disorder, and crime in the city of New Orleans. Using large-scale administrative and contextual data collected from the year 2012 to 2018, our spatiotemporal regression analysis provides empirical evidence for the salient effects of housing vacancy on neighborhood level of property crime and violence. In addition, the spillover effect of housing vacancy is observed on the neighborhood level of drug offense, property crime, and violence. These results potentially identify vacant properties as a modifiable target for intervention to reduce urban crime and suggest that community-based programs aiming to enhance informal social control and collective efficacy may be as important as broken window policing programs.

scholarly journals Predicting Detached Housing Vacancy: A Multilevel Analysis

2022 ◽  
Vol 14 (2) ◽  
pp. 922
Author(s):  
Jaekyung Lee ◽  
Galen Newman ◽  
Changyeon Lee
Keyword(s):  
Multilevel Model ◽  
Local Environment ◽  
Critical Issue ◽  
Building Structure ◽  
Model Design ◽  
Vacancy Rate ◽  
City Center ◽  
Individual Housing ◽  
The Individual ◽  
Housing Vacancy

Urban shrinkage is a critical issue in local small- and medium-sized cities in Korea. While there have been several studies to analyze the causes and consequences of vacancy increases, most have only focused on socioeconomic associations at larger scale and failed to consider individual housing level characteristics, primarily due to a lack of appropriate data. Based on data including 52,400 individual parcels, this study analyzes the primary contributors to vacant properties and their spatial distribution through a multilevel model design based on data for each parcel. Then, we identify areas at high risk of vacancy in the future to provide evidence to establish policies for improving the local environment. Results indicate that construction year, building structure, and road access conditions have a significant effect on vacant properties at the individual parcel level, and the presence of schools and hypermarket within 500 m are found to decrease vacant properties. Further, prediction outcomes show that the aged city center and areas with strict regulations on land use are expected to have a higher vacancy rate. These findings are used to provide a set of data-based revitalization strategies through the development of a vacancy prediction model.

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