A Precise Method for Determining Contoured Surfaces

Philip, George; Watson, D. F.

doi:10.1071/aj81016

Cited by 118 publications

(86 citation statements)

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“…This method is based on the assumption that the nearest points surrounding a selected location have more influence on the prediction than those that are farther away. The IDW interpolation technique weights the points being evaluated based on the distance from the sampled location; the weight is a function of inverse distance [45].…”

Section: Spatial Distribution Of the Cooling Degree Daysmentioning

confidence: 99%

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Urban Heat Island and Overheating Characteristics in Sydney, Australia. An Analysis of Multiyear Measurements

et al. 2017

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It has become increasingly important to study the urban heat island phenomenon due to the adverse effects on summertime cooling energy demand, air and water quality and most importantly, heat-related illness and mortality. The present article analyses the magnitude and the characteristics of the urban heat island in Sydney, Australia. Climatic data from six meteorological stations distributed around the greater Sydney region and covering a period of 10 years are used. It is found that both strong urban heat island (UHI) and oasis phenomena are developed. The average maximum magnitude of the phenomena may exceed 6 K. The intensity and the characteristics of the phenomena are strongly influenced by the synoptic weather conditions and in particular the development of the sea breeze and the westerly winds from the desert area. The magnitude of the urban heat island varies between 0 and 11 • C, as a function of the prevailing weather conditions. The urban heat island mainly develops during the warm summer season while the oasis phenomenon is stronger during the winter and intermediate seasons. Using data from an extended network of stations the distribution of Cooling Degree Days in the greater Sydney area is calculated. It is found that because of the intense development of the UHI, Cooling Degree Days in Western Sydney are about three times higher than in the Eastern coastal zone. The present study will help us to better design and implement urban mitigation strategies to counterbalance the impact of the urban heat island in the city.

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Section: Spatial Distribution Of the Cooling Degree Daysmentioning

confidence: 99%

“…Sustainability 2017, 9, 712 15 of 21 weights the points being evaluated based on the distance from the sampled location; the weight is a function of inverse distance [45]. For the calculation of CDDs, the period 2014-2016 was selected.…”

Section: Spatial Distribution Of the Cooling Degree Daysmentioning

confidence: 99%

Urban Heat Island and Overheating Characteristics in Sydney, Australia. An Analysis of Multiyear Measurements

et al. 2017

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“…If the p value is very high, only the immediate few surrounding points influence the prediction. P = 2 is normally used in the interpolation [21,22].…”

Section: Inverse Distance Weighted (Idw Method) and Linear Regressionmentioning

confidence: 99%

Influence of Water Temperature and Salinity on PH During Dry Season in Lower Dong Nai River System, Vietnam

Dung¹,

Duc²

2015

GeoScience Engineering

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This paper uses the gvSIG 2.2.0 software, IDW interpolation method, river and stream network data, and 36 sampling sites to build the maps of three monitored parameters such as pH, water temperature, and salinity in the Lower Dong Nai River system (2009)(2010) in dry season. Based on an analysis of these maps and statistical assessment by using the R software, the correlations between pH, temperature, and salinity are clarified. The results show that the pH and temperature values have a tendency to decrease, whereas the salinity tends to increase annually. The pH value has good and significant correlations with the water temperature and salinity in both simple and multiple linear regression models. The results aim to provide a scientific reference for further research on the water environment in this area.

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“…Inverse distance weighted (IDW) interpolation is an automated technique [42], requiring very few parameters from the operators [43]. It is specifically suitable where the dataset range is narrow and other fitting techniques are heavily affected by errors.…”

Section: Inverse Distance Weighted (Idw)mentioning

confidence: 99%

“…IDW estimates cell values through a linearly weighted combination of sample points, where the weight assigned to each sample point is the inverse of its distance from the cell being estimated [42]. The underlying assumption of IDW is that an unsampled cell's value is a weighted average of known cells' data in the local neighbourhood [44].…”

Section: Inverse Distance Weighted (Idw)mentioning

confidence: 99%

A Comparative Study of Three Non-Geostatistical Methods for Optimising Digital Elevation Model Interpolation

Salekin

Burgess

Morgenroth

et al. 2018

IJGI

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Abstract:It is common to generate digital elevation models (DEMs) from aerial laser scanning (ALS) data. However, cost and lack of knowledge may preclude its use. In contrast, global navigation satellite systems (GNSS) are seldom used to collect and generate DEMs. These receivers have the potential to be considered as data sources for DEM interpolation, as they can be inexpensive, easy to use, and mobile. The data interpolation method and spatial resolution from this method needs to be optimised to create accurate DEMs. Moreover, the density of GNSS data is likely to affect DEM accuracy. This study investigates three different deterministic approaches, in combination with spatial resolution and data thinning, to determine their combined effects on DEM accuracy. Digital elevation models were interpolated, with resolutions ranging from 0.5 m to 10 m using natural neighbour (NaN), topo to raster (ANUDEM), and inverse distance weighted (IDW) methods. The GNSS data were thinned by 25% (0.389 points m −2 ), 50% (0.259 points m −2 ), and 75% (0.129 points m −2 ) and resulting DEMs were contrast against a DEM interpolated from unthinned data (0.519 points m −2 ). Digital elevation model accuracy was measured by root mean square error (RMSE) and mean absolute error (MAE). It was found that the highest resolution, 0.5 m, produced the lowest errors in resulting DEMs (RMSE = 0.428 m, MAE = 0.274 m). The ANUDEM method yielded the greatest DEM accuracy from a quantitative perspective (RMSE = 0.305 m and MAE = 0.197 m); however, NaN produced a more visually appealing surface. In all the assessments, IDW showed the lowest accuracy. Thinning the input data by 25% and even 50% had relatively little impact on DEM quality; however, accuracy decreased markedly at 75% thinning (0.129 points m −2 ). This study showed that, in a time where ALS is commonly used to generate DEMs, GNSS-surveyed data can be used to create accurate DEMs. This study confirmed the need for optimization to choose the appropriate interpolation method and spatial resolution in order to produce a reliable DEM.

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A Precise Method for Determining Contoured Surfaces

Cited by 118 publications

References 0 publications

Urban Heat Island and Overheating Characteristics in Sydney, Australia. An Analysis of Multiyear Measurements

Urban Heat Island and Overheating Characteristics in Sydney, Australia. An Analysis of Multiyear Measurements

Influence of Water Temperature and Salinity on PH During Dry Season in Lower Dong Nai River System, Vietnam

A Comparative Study of Three Non-Geostatistical Methods for Optimising Digital Elevation Model Interpolation

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