Urban Geo Big Data

Brovelli, M. A.; Boccardo, Piero; Bordogna, Gloria; Pepe, Antonio; Crespi, Mattia; Munafò, Michele; Pirotti, Francesco

doi:10.5194/isprs-archives-xlii-4-w14-23-2019

Cited by 9 publications

(4 citation statements)

References 30 publications

(27 reference statements)

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“…Thus, efficient treatment and integration in the volume of data become essential. In this scenario, methodologies arise to extract, integrate and evaluate the quality of the relevant information from this ever-increasing amount of geographic data (Brovelli et al, 2019). In the context of geospatial data quality, although methodologies with efficient statistical and theoretical approaches exist, the speed with which information is obtained in real-time and the availability of open data requires free software tools that assist users in these assessments.…”

Section: Introductionmentioning

confidence: 99%

QPEC: QGIS Toolkit for Evaluating Geospatial Data Positional Accuracy according to the Brazilian Cartographic Accuracy Standard

Elias

Giehl

Amorim

et al. 2023

Anu. Inst. Geociênc.

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This paper presents the development of a QGIS plugin to support evaluating the planimetric positional quality for point and linear features based on the metrics established by Brazilian legislation. For this purpose, we used the QGIS environment Graphical Modeler, which consists of an interface to concatenate a series of processes into a single algorithm. The set of tools, called QPEC, allows for performing the statistical tests from the automatic identification of the sample size and discrepancies. In order to demonstrate the implemented functionalities, a case study was carried out. In this illustrative example, the vector files from the Cartographic and Cadastral System of the Municipality of Salvador - BA (SICAD) were the reference data, and their homologous OpenStreetMap (OSM) features were the analysed database. The results obtained are presented in the attributes table. In addition, the spatial distribution of the discrepancies is visualised through the visual variable colour value in a quartile classification. The creation of this toolset corroborates the feasibility of developing more visual, automated and complete interfaces to support users of geospatial data in analysing the quality of the information available, especially when it involves free applications with open-source code.

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Section: Introductionmentioning

confidence: 99%

QPEC: QGIS Toolkit for Evaluating Geospatial Data Positional Accuracy according to the Brazilian Cartographic Accuracy Standard

Elias

Giehl

Amorim

et al. 2023

Anu. Inst. Geociênc.

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“…The URBAN GEOmatics for Bulk Information Generation, Data Assessment and Technology Awareness (URBAN GEO BIG DATA) project (http://www.urbangeobigdata.it/) [29,30] aims to develop innovative GIS tools that employ various big geospatial datasets, built on the existing tools for handling big geospatial data, to understand several urban dynamics better and as a result, to manage natural resources in urban areas and urban infrastructures more effectively [31]. The datasets include the traditional datasets such as topographic databases, LIDAR data and statistical data, the data from Web 2.0 such as volunteered geographic information (VGI) and Earth observation (EO) data.…”

Section: Introductionmentioning

confidence: 99%

Multidimensional Visualization and Processing of Big Open Urban Geospatial Data on the Web

Kilsedar

Brovelli

2020

IJGI

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The focus of this research is addressing a subset of the geovisualization (i.e., geographic visualization) challenges identified in the literature, namely multidimensional vector and raster geospatial data visualization. Moreover, the work implements an approach for multidimensional raster geospatial data processing. The results of this research are provided through a geoportal comprised of multiple applications that are related to 3D visualization of cities, ground deformation, land use and land cover and mobility. In a subset of the applications, the datasets handled are considered to be large in volume. The geospatial data were visualized on dynamic and interactive virtual globes to enable visual exploration. The geoportal is available on the web to enable cross-platform access to it. Furthermore, the geoportal was developed employing open standards, free and open source software (FOSS) and open data, most importantly to ensure interoperability and reduce the barriers to access it. The geoportal brings together various datasets, different both in terms of context and format employing numerous technologies. As a result, the existing web technologies for geovisualization and geospatial data processing were examined and exemplary and innovative software was developed to extend the state of the art.

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“…Several techniques are used to obtain geospatial data, ranging from the field-based topographic and aerial photogrammetric surveys to the web-based collaborative VGI platforms. Efficient data handling and integration help detect issues related to urban growth, establish methodological procedures to assess the data quality, and extract and integrate the growing amounts of geographic data (Brovelli et al 2019). The instructions on how to assess the quality of geographic metadata are provided by the International Organization for Standardization in ISO 19157 (ISO 2013):…”

Section: Introductionmentioning

confidence: 99%

The quality of OpenStreetMap in a large metropolis in northeast Brazil: Preliminary assessment of geospatial data for road axes

et al. 2020

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This paper evaluates the data quality of road axes using the OpenStreetMap (OSM) collaborative mapping platform. OSM was chosen owing to the abundance of data and registered contributors (~ 6 million). We assumed the OSM collaborative data could complement the reference mappings by its quality parameters. We used the cartographic quality indicators of positional accuracy, thematic accuracy, and completeness to validate vector files from OSM. We analyzed the positional accuracy of linear features and we developed the automation of the positional accuracy process. The tool verified the completeness of road axes and thematic accuracy. The positional accuracy of linear features was also used, performed to obtain a range of scales, which reflected the characteristics of mapped areas and varied from 1:22,500 to 1:25,000. The completeness of road axes was 82% of the checked areas. By evaluating the thematic accuracy, we found that the absence of road axes toponymy in editions caused errors in the OSM features (i.e., 58% of road axes without information). As such, we concluded that collaborative data complements the reference cartography by measuring the heterogeneity of information in various regions and filtering the OSM data, despite its being useful for certain analyses.

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Urban Geo Big Data

Cited by 9 publications

References 30 publications

QPEC: QGIS Toolkit for Evaluating Geospatial Data Positional Accuracy according to the Brazilian Cartographic Accuracy Standard

QPEC: QGIS Toolkit for Evaluating Geospatial Data Positional Accuracy according to the Brazilian Cartographic Accuracy Standard

Multidimensional Visualization and Processing of Big Open Urban Geospatial Data on the Web

The quality of OpenStreetMap in a large metropolis in northeast Brazil: Preliminary assessment of geospatial data for road axes

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