Geospatial Data Generation and Preprocessing Tools for Urban Computing System Development1

Golubev, Alexey; Chechetkin, Ilya; Parygin, Danila; Соколов, А. Д.; Shcherbakov, Maxim

doi:10.1016/j.procs.2016.11.026

Cited by 25 publications

(7 citation statements)

References 10 publications

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“…In addition, a large number of predictors will also cause a problem called "Multicollinearity". It is a statistical phenomenon of existing a perfect or exact relationship between predictors which will cause incorrectness about the relationship between predictors and outcme variable of that regression model [24]. Therefore, if we apply the model with redundant predictors for prediction purpose, it will be time-consuming and high expensive job indeed.…”

Section: Dimensionality Reduction Using Pcamentioning

confidence: 99%

Predictive geospatial analytics using principal component regression

Khine¹,

Nyunt²

2020

IJECE

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Nowadays, exponential growth in geospatial or spatial data all over the globe, geospatial data analytics is absolutely deserved to pay attention in manipulating voluminous amount of geodata in various forms increasing with high velocity. In addition, dimensionality reduction has been playing a key role in high-dimensional big data sets including spatial data sets which are continuously growing not only in observations but also in features or dimensions. In this paper, predictive analytics on geospatial big data using Principal Component Regression (PCR), traditional Multiple Linear Regression (MLR) model improved with Principal Component Analysis (PCA), is implemented on distributed, parallel big data processing platform. The main objective of the system is to improve the predictive power of MLR model combined with PCA which reduces insignificant and irrelevant variables or dimensions of that model. Moreover, it is contributed to present how data mining and machine learning approaches can be efficiently utilized in predictive geospatial data analytics. For experimentation, OpenStreetMap (OSM) data is applied to develop a one-way road prediction for city Yangon, Myanmar. Experimental results show that hybrid approach of PCA and MLR can be efficiently utilized not only in road prediction using OSM data but also in improvement of traditional MLR model.

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Section: Dimensionality Reduction Using Pcamentioning

confidence: 99%

Predictive geospatial analytics using principal component regression

Khine¹,

Nyunt²

2020

IJECE

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“…Researches [Arnold et al, 2021; Chen, 2000; Dangermond, Goodchild, 2020; Duckham et al, 2017 ;Golubev et al, 2016;Hermes, Poulsen, 2012] stress the importance and value of geospatial information for generating geospatial knowledge as new achievements in geospatial technologies, and they pay more attention to the techniques and methods of geospatial data generation. For this purpose, a source of geospatial data is considered in a case when "this data includes location tag" [Golubev et al, 2016, p. 218].…”

Section: Introductionmentioning

confidence: 99%

Concept of an integration of open data for socio-economic mapping in Uzbekistan

Gulyamova

Rakhmonov

2022

ICIGIS

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The purpose of the study is the development of a method for integrating non-spatial and geospatial data from different sources for the online dynamic mapping of Uzbekistan at different spatial scales. From the perspective of using open resources in socio-economic cartography, the challenges and perspectives of using online open non-spatial statistical data through the government portal of the Republic of Uzbekistan are considered for web mapping. The method is suggested for building a web-based spatial visualization tool and getting geographic information. In the case when open geospatial data is not available, it is suggested to integrate non-spatial statistical data of open sources into the environment of web mapping. For using new opportunities and tools of geospatial technologies a methodology of using open resources including open datasets, open source software and cloud computing are widely used on all stages of map development. It has been confirmed that this approach represents one of the valuable opportunities for geospatial modelling in developing countries. The benefits of integrating government open non-spatial statistics have been verified through an interactive mapping population at the national, regional, district, and city levels to be effective in addressing policy and governance issues for decision-makers and practitioners.

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“…The structure of such systems includes specialized algorithmic tools and implemented with their help software applications [4]. However, their main purpose is to work with the data.…”

Section: Introductionmentioning

confidence: 99%

Parsing of Data on Real Estate Objects from Network Resource

Cherkesov¹,

Malikov²,

Golubev³

et al. 2017

Proceedings of the IV International Research Conference "Information Technologies in Science, Management, Social Sphere and Med

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Abstract-Existing approaches for collecting data from sites on the Internet were considered. A comparative analysis of the solution based on the BeautifulSoup library and the Scrapy framework for parsing the content of network resources was made. Sources of information about real estate objects were analyzed. The method for parsing data on real estate objects was developed based on the results of the conducted studies. In addition, the main problems with the use of parsing technology were identified.

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Geospatial Data Generation and Preprocessing Tools for Urban Computing System Development1

Cited by 25 publications

References 10 publications

Predictive geospatial analytics using principal component regression

Predictive geospatial analytics using principal component regression

Concept of an integration of open data for socio-economic mapping in Uzbekistan

Parsing of Data on Real Estate Objects from Network Resource

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