Missing Value Imputation for PM10 Concentration in Sabah using Nearest Neighbour Method (NNM) and Expectation-Maximization (EM) Algorithm

Rumaling, Muhammad Izzuddin; Chee, Fuei Pien; Dayou, Jedol; Chang, Jackson Hian Wui; Kong, Steven Soon-Kai; Sentian, Justin

doi:10.5572/ajae.2020.14.1.062

Cited by 11 publications

(4 citation statements)

References 30 publications

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“…It is worth noting that the nearest neighbor method is a general method and has a number of limitations for the evaluation of the service extent of urban refugee parks [47,48]. For example, the hypothetical conditions used in the method are simpler than the real scenarios of disasters [49].…”

Section: Discussionmentioning

confidence: 99%

Evaluating Methodology for the Service Extent of Refugee Parks in Changchun, China

Xiu

Wei

et al. 2020

Sustainability

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Refugee parks are general parks that can serve as emergency shelters in cities. The core issue of refugee parks lies in their service extent they provided. Globally, the service extent of refugee parks is determined by the Euclidean or actual road network distance methods. The former lacks measurement accuracy, whereas the latter lacks the consideration of human dimension and proximity. Hence, we propose the nearest neighbor method, which considers not only the locations of refugee parks and sub-districts, but also road networks and census data. Using this method, we evaluated the service extent of refugee parks in Changchun, northern China. We compared our results with the Euclidean distance method. Results showed that the nearest neighbor method effectively accounted for the effect of road network resistance and results aligned with the refuge needs of residents. Differences in both methods were mainly affected by the size of the parks and local road network and population densities. The Euclidean approach determines the service extent based on a unified service radius, thus producing greater errors. The nearest neighbor method can reveal the spatial imbalance of refugee parks, as well as the mismatch between the park size and population distribution. Furthermore, the nearest neighbor method implements policies of spatial optimization of urban refugee parks. As a general method, it should be suited to different types of disasters.

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Section: Discussionmentioning

confidence: 99%

Evaluating Methodology for the Service Extent of Refugee Parks in Changchun, China

Xiu

Wei

et al. 2020

Sustainability

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“…Thereby, the impact is pollutant specific. Humidity negatively correlates with PM 10 levels since the particles become heavier as they absorb water and therefore reduce their distribution range (Rumaling et al, 2021). Conversely, the finer particle size is less affected by humidity, which is the case with PM 2.5 (Munir et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The price of clean air – quantifying air pollution exposure in real estate decisions

Restle,

Cajias,

Knoppik

2024

JPIF

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PurposeThe purpose of this paper is to explore the significance impact of air quality as a contributing factor on residential property rents by applying geo-informatics to economic issues. Since air pollution poses a severe health threat, city residents should have a right to know about the (invisible) hazards they are exposed to.Design/methodology/approachWithin spatial-temporal modeling of air pollutants in Berlin, Germany, three interpolation techniques are tested. The most suitable one is selected to create seasonal maps for 2018 and 2021 with pollution concentrations for particulate matter values and nitrogen dioxide for each 1,000 m2 cell within the administrative boundaries. Based on the evaluated pollution particulate matter values, which are used as additional variables for semi-parametric regressions the impact of the air quality on rents is estimated.FindingsThe findings reveal a compelling association between air quality and the economic aspect of the residential real estate market, with noteworthy implications for both tenants and property investors. The relationship between air pollution variables and rents is statistically significant. However, there is only a “willingness-to- pay” for low particulate matter values, but not for nitrogen dioxide concentrations. With good air quality, residents in Berlin are willing to pay a higher rent (3%).Practical implicationsThese results suggest that a “marginal willingness-to-pay” occurs in a German city. The research underscores the multifaceted impact of air quality on the residential rental market in Berlin. The evidence supports the notion that a cleaner environment not only benefits human health and the planet but also contributes significantly to the economic bottom line of property investors.Originality/valueThe paper has a unique data engineering approach. It collects spatiotemporal data from network of state-certified measuring sites to create an index of air pollution. This spatial information is merged with residential listings. Afterward non-linear regression models are estimated.

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“…보다 구체적인 결측 보충 연구는 다양한 분야에서 특정 항목에 국한되어 수행되어 왔다. 표층 해류자료 (Fredj et al, 2016), Climate Change Initiative(CCI) 토양습윤 자료(Almendra-Martin, 2021), 해양센서자료의 Real-Time 보충(Velasco-Gallego and Lazakis, 2020), 강우 자료 (Sattari et al, 2020;Bellido-Jimenez et al, 2021), 위성 정보를 이용한 LAI(leaf area index) 산출정보 (Kandasamy et al, 2013), 수량자료 (Baddoo et al, 2021), Eddy Covariance Carbon 플럭스 자 료 (Zhao and Huang, 2015), 다양한 기후(기온, 습도, 바람 항목) Time-Series 자료(Afrifa-Yamoah et al, 2020), PM10 농도자료 (Rumaling et al, 2020), SAR(Search and Rescue) 자료 (Wang et al, 2021), VIIRS/NOAA-20 해색산출 정보 (Liu and Wang, 2019), 지표온도 및 NDVI 자료 (Sarafanov et al, 2020), 연안해역의 수온 자료 (Cho et al, 2013) (Hair Jr. et al, 2010)…”

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Long-gap Filling Method for the Coastal Monitoring Data

Cho¹,

Lee²,

Lee³

2021

J Korean Soc Coast Ocean Eng

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Technique for the long-gap filling that occur frequently in ocean monitoring data is developed. The method estimates the unknown values of the long-gap by the summation of the estimated trend and selected residual components of the given missing intervals. The method was used to impute the data of the long-term missing interval of about 1 month, such as temperature and water temperature of the Ulleungdo ocean buoy data. The imputed data showed differences depending on the monitoring parameters, but it was found that the variation pattern was appropriately reproduced. Although this method causes bias and variance errors due to trend and residual components estimation, it was found that the bias error of statistical measure estimation due to long-term missing is greatly reduced. The mean, and the 90% confidence intervals of the gap-filling model’s RMS errors are 0.93 and 0.35~1.95, respectively.

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Missing Value Imputation for PM10 Concentration in Sabah using Nearest Neighbour Method (NNM) and Expectation-Maximization (EM) Algorithm

Cited by 11 publications

References 30 publications

Evaluating Methodology for the Service Extent of Refugee Parks in Changchun, China

Evaluating Methodology for the Service Extent of Refugee Parks in Changchun, China

The price of clean air – quantifying air pollution exposure in real estate decisions

Long-gap Filling Method for the Coastal Monitoring Data

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