House vacancy at urban areas in China with nocturnal light data of DMSP-OLS

Yao, Yongling; Yao, Li

doi:10.1109/icsdm.2011.5969087

Cited by 10 publications

(9 citation statements)

References 6 publications

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“…RMSE has also been applied for several data validations [27], and can also be adopted to evaluate the results in this study. RMSE is calculated using (8) and (9), and a value of 17.72% is obtained…”

Section: A Validation Of the Estimated Hvrmentioning

confidence: 99%

“…To overcome these drawbacks, researchers tried to derive HVR from existing data. For example, Yao and Li [9] proposed a ratio (named GAP) between house price and light brightness derived from the DMSP-OLS NTL data, and used such radio as an indicator for the level of house vacancy. While GAP can provide useful information, it does not directly inform us about the specific values of the HVR.…”

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confidence: 99%

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Estimating House Vacancy Rate in Metropolitan Areas Using NPP-VIIRS Nighttime Light Composite Data

Chen

et al. 2015

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

120

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House vacancy rate (HVR) is an important index in assessing the healthiness of residential real estate market. Investigating HVR by field survey requires a lot of human and economic resources. The nighttime light (NTL) data, derived from Suomi National Polar-orbiting Partnership, can detect the artificial light from the Earth surface, and have been used to study social-economic activities. This paper proposes a method for estimating the HVR in metropolitan areas using NPP-VIIRS NTL composite data. This method combines NTL composite data with land cover information to extract the light intensity in urbanized areas. Then, we estimate the light intensity values for nonvacancy areas, and use such values to calculate the HVR in corresponding regions. Fifteen metropolitan areas in the United States have been selected for this study, and the estimated HVR values are validated using corresponding statistical data. The experimental results show a strong correlation between our derived HVR values and the statistical data. We also visualize the estimated HVR on maps, and discover that the spatial distribution of HVR is influenced by natural situations as well as the degree of urban development.Index Terms-House vacancy rate (HVR), nighttime light (NTL) composite data, NPP-VIIRS, USA.

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Section: A Validation Of the Estimated Hvrmentioning

confidence: 99%

mentioning

confidence: 99%

Estimating House Vacancy Rate in Metropolitan Areas Using NPP-VIIRS Nighttime Light Composite Data

Chen

et al. 2015

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

120

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“…To improve the continuity and comparability of DMSP/OLS NTL data, Liu et al [29] developed a new data pre-processing method to monitor the urban expansion dynamic in China between 1992 and 2008, based on the previous work of Yuan et al [30] that estimated the territorial development intensity (TDI) of China at provincial scale using NTL data. In the same year, Yao et al [31] successfully analyzed the economic bubbles in the Chinese real estate market by comparing the Digital Number (DN) values of NTL data with the house prices in 50 Chinese cities. A number of research papers have proved that multi-temporal NTL data could effectively portray the dynamic processes of urbanization (e.g., Ma et al [32]; Sutton [33]).…”

Section: Introductionmentioning

confidence: 99%

Estimating Land Development Time Lags in China Using DMSP/OLS Nighttime Light Image

Zhang

Wang

2015

Remote Sensing

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Abstract:The Chinese real estate industry has experienced rapid growth since China's economic reform. Along with a booming industry, a third of purchased lands were left undeveloped in the last decade. Knowledge of real estate development time lags between land being purchased and property being occupied can enable policymakers to produce more effective policies and regulations to guide the real estate industry and sustain economic development and social welfare. This paper presents an innovative method to estimate provincial land development time lags in China using DMSP/OLS NTL imagery and real estate statistical data. The results showed that real estate development time lag was common in China during 2000-2010. More than half of the study sites showed development time lags of three years or longer. An Increment of Developed Pixels (IDP) index was established to outline yearly land development completions in China between 2000 and 2010. A Comprehensive Real Estate Price Index (CREPI) was created to explore the causes of the time lags. A strong and positive correlation was found between the real estate development time lags and CREPI values (with r = 0.619, n = 31, p < 0.0005). The results indicated that the land development time lag during the study period was positively correlated to the activity of the local real estate market, the price trend of land and housing properties, and the local economic situation. The results also proved that with the support of statistical data the

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“…Since the vacancy status of houses is caused by a lack of human activities, the night-time light (NTL) data have been used as a proxy to derive the vacancy rates. Yao et al [31] assessed the housing vacancy in urban areas in China using nocturnal light data from the Defense Meteorological Satellite Program's Operational Linescan System (DMSP-OLS) in 2011, and Chen et al [32] used the same dataset to calculate HVR in 15 metropolitan areas in the U.S. with an R 2 of 0.734 in 2015. However, due to the coarse spatial resolution of these datasets, existing studies have only been carried out on the national or metropolitan scale, not finer scales.…”

Section: Introductionmentioning

confidence: 99%

Modeling the Census Tract Level Housing Vacancy Rate with the Jilin1-03 Satellite and Other Geospatial Data

Wang

Zou

et al. 2018

Remote Sensing

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The vacant house is an essential phenomenon of urban decay and population loss. Exploration of the correlations between housing vacancy and some socio-environmental factors is conducive to understanding the mechanism of urban shrinking and revitalization. In recent years, rapidly developing night-time remote sensing, which has the ability to detect artificial lights, has been widely applied in applications associated with human activities. Current night-time remote sensing studies on housing vacancy rates are limited by the coarse spatial resolution of data. The launch of the Jilin1-03 satellite, which carried a high spatial resolution (HSR) night-time imaging camera, provides a new supportive data source. In this paper, we examined this new high spatial resolution night-time light dataset in housing vacancy rate estimation. Specifically, a stepwise multivariable linear regression model was engaged to estimate the housing vacancy rate at a very fine scale, the census tract level. Three types of variables derived from geospatial data and night-time image represent the physical environment, landuse (LU) structure, and human activities, respectively. The linear regression models were constructed and analyzed. The analysis results show that (1) the HVRs estimating model using the Jilin1-03 satellite and other ancillary geospatial data fits well with the Census statistical data (adjusted R2 = 0.656, predicted R2 = 0.603, RMSE = 0.046) and thus is a valid estimation model; (2) the Jilin1-03 satellite night-time data contributed a 28% (from 0.510 to 0.656) fitting accuracy increase and a 68% (from 0.359 to 0.603) predicting accuracy increase in the estimate model of the housing vacancy rate. Reflecting socio-economic conditions, the luminous intensity of commercial areas derived from the Jilin1-03 satellite is the most influential variable to housing vacancy. Land use structure indirectly and partially demonstrated that the social environment factors in the community have strong correlations with residential vacancy. Moreover, the physical environment factor, which depicts vegetation conditions in the residential areas, is also a significant indicator of housing vacancy. In conclusion, the emergence of HSR night light data opens a new door to future microscopic scale study within cities.

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House vacancy at urban areas in China with nocturnal light data of DMSP-OLS

Cited by 10 publications

References 6 publications

Estimating House Vacancy Rate in Metropolitan Areas Using NPP-VIIRS Nighttime Light Composite Data

Estimating House Vacancy Rate in Metropolitan Areas Using NPP-VIIRS Nighttime Light Composite Data

Estimating Land Development Time Lags in China Using DMSP/OLS Nighttime Light Image

Modeling the Census Tract Level Housing Vacancy Rate with the Jilin1-03 Satellite and Other Geospatial Data

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