Bootstrap approaches for spatial data

García-Soidán, Pilar; Menezes, Raquel; Rubiños, Óscar

doi:10.1007/s00477-013-0808-9

Cited by 29 publications

(16 citation statements)

References 23 publications

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“…ese methods allow increasing the data size by generating new samples based on the original samples. erefore, bootstrap methods for spatial data [9,13] can also be potentially used for data generation if a subset of customer locations (i.e. AI 4) is known.…”

Section: Resultsmentioning

confidence: 99%

Predicting Optimal Facility Location without Customer Locations

Yılmaz

Elbaşı

Ferhatosmanoğlu

2017

Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

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Deriving meaningful insights from location data helps businesses make beer decisions. One critical decision made by a business is choosing a location for its new facility. Optimal location queries ask for a location to build a new facility that optimizes an objective function. Most of the existing works on optimal location queries propose solutions to return best location when the set of existing facilities and the set of customers are given. However, most businesses do not know the locations of their customers. In this paper, we introduce a new problem seing for optimal location queries by removing the assumption that the customer locations are known. We propose an optimal location predictor which accepts partial information about customer locations and returns a location for the new facility. e predictor generates synthetic customer locations by using given partial information and it runs optimal location queries with generated location data. Experiments with real data show that the predictor can nd the optimal location when sucient information is provided.

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Section: Resultsmentioning

confidence: 99%

Predicting Optimal Facility Location without Customer Locations

Yılmaz

Elbaşı

Ferhatosmanoğlu

2017

Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

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“…It is determined: , where is MIO of dimension 3 for one polygonal spatial object. It is determined: , (7) where is the list of lines bounding the contours of the polygon object, is the number of bounding the polygon lines, is i-linear spatial object MIO, defined by (4). The complete description of one thematic layer on the master plan is represented as a group of similar objects are point, linear or polygon (for example, a river network or a set of hydrological control posts) and their attributive characteristics.…”

Section: Main Researchmentioning

confidence: 99%

Modeling of Spatial Data on the Construction Site Based on Multidimensional Information Objects

Mihaylenko*,

Honcharenko*,

Khrystyna

et al. 2019

IJEAT

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The article presents information modeling different types of spatial data on the construction site. The description of spatial and attribute data based on multidimensional information objects (MIO) is determined. It is proposed to use a new type of MIO called modified multidimensional information objects (MMIO). MIO and MMIO schemes are developed for each type of master planning object in the form of one multidimensional information object. Complete descriptions of layers of point, linear and polygonal spatial objects are presented, which allows simplifying the model of the structure of existing databases of spatial information processing systems. The results of the study show that multidimensional information objects allow describing in unified mathematical form sets of various types of spatial data models. The result of modeling of general planning spatial based on multidimensional information objects corresponds the modern requirements of information modeling in construction  BIM-technology and design of master plans on the new technological level.

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“…To this end, first, the bootstrap method, which has the advantages of simplicity (i.e., no need to make any assumption of normality) and high-accuracy (i.e., asymptotically more accurate than the results obtained using sample variance and assumption of normality) [35][36][37], was introduced in this study to quantitatively analyze the spatial uncertainty of rainfall in river basins at different scales [38][39][40]. Second, by employing a distributed hydrological model on a high-performance computing (HPC) system, the uncertainty of simulated runoff was also analyzed using the bootstrap method.…”

Section: Introductionmentioning

confidence: 99%

Analysis of the Influence of Rainfall Spatial Uncertainty on Hydrological Simulations Using the Bootstrap Method

Zhang

Shi

et al. 2018

Atmosphere

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Abstract:Rainfall stations of a certain number and spatial distribution supply sampling records of rainfall processes in a river basin. Uncertainty may be introduced when the station records are spatially interpolated for the purpose of hydrological simulations. This study adopts a bootstrap method to quantitatively estimate the uncertainty of areal rainfall estimates and its effects on hydrological simulations. The observed rainfall records are first analyzed using clustering and correlation methods and possible average basin rainfall amounts are calculated with a bootstrap method using various combinations of rainfall station subsets. Then, the uncertainty of simulated runoff, which is propagated through a hydrological model from the spatial uncertainty of rainfall estimates, is analyzed with the bootstrapped rainfall inputs. By comparing the uncertainties of rainfall and runoff, the responses of the hydrological simulation to the rainfall spatial uncertainty are discussed. Analyses are primarily performed for three rainfall events in the upstream of the Qingjian River basin, a sub-basin of the middle Yellow River; moreover, one rainfall event in the Longxi River basin is selected for the analysis of the areal representation of rainfall stations. Using the Digital Yellow River Integrated Model, the results show that the uncertainty of rainfall estimates derived from rainfall station network has a direct influence on model simulation, which can be conducive to better understand of rainfall spatial characteristic. The proposed method can be a guide to quantify an approximate range of simulated error caused by the spatial uncertainty of rainfall input and the quantified relationship between rainfall input and simulation performance can provide useful information about rainfall station network management in river basins.

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Bootstrap approaches for spatial data

Cited by 29 publications

References 23 publications

Predicting Optimal Facility Location without Customer Locations

Predicting Optimal Facility Location without Customer Locations

Modeling of Spatial Data on the Construction Site Based on Multidimensional Information Objects

Analysis of the Influence of Rainfall Spatial Uncertainty on Hydrological Simulations Using the Bootstrap Method

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