A likelihood-based spatial statistical transformation model (LBSSTM) of regional economic development using DMSP/OLS time-series nighttime light imagery

Li, Chang; Li, Guie; Zhu, Yongzhong; Ge, Yong; Kung, Hsiang‐te; Wu, Yue

doi:10.1016/j.spasta.2017.03.004

Cited by 23 publications

(8 citation statements)

References 41 publications

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“…The scientific studies on the hedonic pricing for the rooms and the factors affecting these pricings at the accommodation facilities in the tourism sector have gained importance in recent years (Papatheodorou et al, 2013;Chen and Rothschild, 2010;Rigall-I-Torrent et al, 2011). Knowing the factors affecting the room pricing as well as being aware of the customer preferences and their behaviors are very important to improve service quality (Baddeley, 2017;Li et al, 2017;Zhang et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

Determination of hedonic hotel room prices with spatial effect in Antalya

Yalçın

2018

EST

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El propósito de este estudio es identificar los factores que afectan el precio de la habitación hotelera en Antalya y crear un modelo de regresión hedónica. Para este propósito, se han seleccionado 1444 hoteles en Antalya. Puesto que estos hoteles están relacionados espacialmente, se ha propuesto un modelo de regresión espacial. Se aplicó la regresión paso a paso (stepwise) a las variables del análisis espacial. El modelo de regresión hedónica se ha construido según el modelo Lag o de retardo espacial (Spatial Lag Model), revelando que es posible utilizar dicho método en estudios similares.

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Section: Introductionmentioning

confidence: 99%

Determination of hedonic hotel room prices with spatial effect in Antalya

Yalçın

2018

EST

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“…Moreover, Doll et al 21 analysed night-time light remote sensing data of 11 European Union countries and the United States; and such data have been shown to correlate with national-level figures of GDP. Meanwhile, studies in China 22,23 , Africa 24 and the United States 25 have led to similar conclusions. Additionally, DMSP-OLS night-time light remote sensing data have been used in the study of urbanization and urban spatial expansion [26][27][28][29] , population migration and distribution [30][31][32][33] .…”

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confidence: 74%

Study on Average Housing Prices in the Inland Capital Cities of China by Night-time Light Remote Sensing and Official Statistics Data

Zhu

et al. 2020

Sci Rep

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In this paper, the annually average Defense Meteorological Satellite Program-Operational Linescan System (DMSP/OLS) night-time light data is first proposed as a surrogate indicator to mine and forecast the average housing prices in the inland capital cities of china. first, based on the time-series analysis of individual cities, five regression models with gross error elimination are established between average night-time light intensity (ANLI) and average commercial residential housing price (ACRHP) adjusted by annual inflation rate or not from 2002 to 2013. Next, an optimal model is selected for predicting the ACRHPs in 2014 of these capital cities, and then verified by the interval estimation and corresponding official statistics. Finally, experimental results show that the quadratic polynomial regression is the optimal mining model for estimating the ACRHP without adjustments in most provincial capitals and the predicted ACRHP of these cities are almost in their interval estimations except for the overrated Chengdu and the underestimated Wuhan, while the adjusted ACRHP is all in prediction interval. Overall, this paper not only provides a novel insight into time-series AcRHp data mining based on time-series AnLi for capital city scale but also reveals the potentiality and mechanism of the comprehensive AnLi to characterize the complicated ACRHP. Besides, other factors influencing housing prices, such as the time-series lags of government policy, are tested and analysed in this paper.

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“…11(a)-(c) shows the annual average distribution of the total solar radiation in three sample areas. In the study area, the minimum and maximum solar radiation values are 1.97 and 23.04 MJ/m 2 /day, respectively. According to the DSM data, we found that buildings could easily block the road area receiving the solar radiance.…”

Section: A Position and Types Of Street Lightsmentioning

confidence: 99%

Automated Extraction of Street Lights From JL1-3B Nighttime Light Data and Assessment of Their Solar Energy Potential

Cheng

Chen

et al. 2020

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

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To realize energy conservation and environmental protection, solar street lights have been widely used in urban areas in China. To reasonably and effectively utilize solar street lights, the original street lights must be located, and the solar street light potential must be assessed. The Jilin1-03B (JL1-3B) satellite provides next-generation nighttime light data with a high spatial resolution and in three spectral bands. Consequently, the street lights can be extracted from the nighttime light data. We used the road network dataset from the open street map with a specific buffer to extract the road area as a constraint region. Next, the grayscale brightness of JL1-3B images was obtained by integrating all the three bands to locate the street light by using a local maximum algorithm. Then, the values of the original three bands were utilized to classify the types of street lights as high-pressure sodium (HPS) lamps or light-emitting diode lamps. Finally, we simulated the replacement of all the HPS lamps with solar street lights and assessed the corresponding solar energy potential by using the digital surface model data and hourly cloud cover data through the SHORTWAVE-C model. The accuracy of location of the street lights was approximately 90%. Replacing an HPS lamp by one solar street light for 20 years can save 1.85 × 10 4 kWh of electrical energy, 7.41 t of standard coal, 5.03 t of C emissions, 18.47 t of CO 2 emissions, 0.55 t of SO 2 emissions, and 0.28 t of NO X emissions.

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A likelihood-based spatial statistical transformation model (LBSSTM) of regional economic development using DMSP/OLS time-series nighttime light imagery

Cited by 23 publications

References 41 publications

Determination of hedonic hotel room prices with spatial effect in Antalya

Determination of hedonic hotel room prices with spatial effect in Antalya

Study on Average Housing Prices in the Inland Capital Cities of China by Night-time Light Remote Sensing and Official Statistics Data

Automated Extraction of Street Lights From JL1-3B Nighttime Light Data and Assessment of Their Solar Energy Potential

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