A grounding-based ontology of data quality measures

Mocnik, Franz-Benjamin; Mobasheri, Amin; Griesbaum, Luisa; Eckle, Melanie; Jacobs, Clemens; Klonner, Carolin

doi:10.5311/josis.2018.16.360

Cited by 24 publications

(25 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Generally, approaches for data quality are assessed in two class as intrinsic and extrinsic. In the intrinsic assessment, there is no use of an external reference map unlike extrinsic data quality assessment [75]. SMD was assessed in several aspects in this study in order to understand data bias, anomalies and trends.…”

Section: Discussionmentioning

confidence: 99%

Citizens’ Spatial Footprint on Twitter—Anomaly, Trend and Bias Investigation in Istanbul

Gulnerman

Karaman

Pekaslan

et al. 2020

IJGI

View full text Add to dashboard Cite

Social media (SM) can be an invaluable resource in terms of understanding and managing the effects of catastrophic disasters. In order to use SM platforms for public participatory (PP) mapping of emergency management activities, a bias investigation should be undertaken with regard to the data related to the study area (urban, regional or national, etc.) to determine the spatial data dynamics. Thus, such determinations can be made on how SM can be used and interpreted in terms of PP. In this study, the city of Istanbul was chosen for social media data research area, as it is one of the most crowded cities in the world and expecting a major earthquake. The methodology for the data investigation is: 1. Obtain data and engage sampling, 2. Identify the representation and temporal biases in the data and normalize it in response to representation bias, 3. Identify general anomalies and spatial anomalies, 4. Manipulate the trend of the dataset with the discretization of anomalies and 5. Examine the spatiotemporal bias. Using this bias investigation methodology, citizen footprint dynamics in the city were determined and reference maps (most likely regional anomaly maps, representation maps, time-space bias maps, etc.) were produced. The outcomes of the study can be summarized in four steps. First, highly active users generate the majority of the data and removing this data as a general approach within a pseudo-cleaning process means concealing a large amount of data. Second, data normalization in terms of activity levels, changes the anomaly outcome resulting from diverse representation levels of users. Third, spatiotemporally normalized data present strong spatial anomaly tendency in some parts of the central area. Fourth, trend data is dense in the central area and the spatiotemporal bias assessments show the data density varies in terms of the time of day, day of week and season of the year. The methodology proposed in this study can be used to extract the unbiased daily routines of the social media data of the regions for the normal days and this can be referred for the emergency or unexpected event cases to detect the change or impacts.applications and is referred to as Volunteered Geographic Information (VGI) [4,5]. The users can be thought of as unconscious volunteers for social media (SM) VGI, as deliberate volunteers for peer production VGI and as public participators for in citizen science based VGI [6][7][8]. The way of producing these forms of VGI is referred to as neo-geography in that it adopts neo-geographers (i.e., volunteers) who contributes to mapping activity without being expert [9]. This inexperience with regards to data production is questioned in the context of data quality [10][11][12], demographic bias (such as, gender, socioeconomic and educational aspects) [13,14] sampling bias (referring to volunteer sampling) and its impact on the generated data [15,16].In their very first form, citizen science projects in the very first forms were carried out with the use of paper maps [1]. However, with the...

show abstract

Section: Discussionmentioning

confidence: 99%

Citizens’ Spatial Footprint on Twitter—Anomaly, Trend and Bias Investigation in Istanbul

Gulnerman

Karaman

Pekaslan

et al. 2020

IJGI

View full text Add to dashboard Cite

show abstract

“…Data quality is a major issue for OSM and VGI in general, because a holistic and deep understanding of the data creating process is needed [5][6][7]. The characteristics of VGI to be collected and maintained by a community implies that the advancement of the dataset and its underlaying folksonomy, that is, the semantics of the tags used in OSM, are not controlled by a single person or authority but rather the result of a community-driven process [8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Open source data mining infrastructure for exploring and analysing OpenStreetMap

Mocnik

Mobasheri

Zipf

2018

Open geospatial data, softw. stand.

Self Cite

View full text Add to dashboard Cite

OpenStreetMap and other Volunteered Geographic Information datasets have been explored in the last years, with the aim of understanding how their meaning is rendered, of assessing their quality, and of understanding the community-driven process that creates and maintains the data. Research mostly focuses either on the data themselves while ignoring the social processes behind, or solely discusses the community-driven process without making sense of the data at a larger scale. A holistic understanding that takes these and other aspects into account is, however, seldom gained. This article describes a server infrastructure to collect and process data about different aspects of OpenStreetMap. The resulting data are offered publicly in a common container format, which fosters the simultaneous examination of different aspects with the aim of gaining a more holistic view and facilitates the results' reproducibility. As an example of such uses, we discuss the project OSMvis. This project offers a number of visualizations, which use the datasets produced by the server infrastructure to explore and visually analyse different aspects of OpenStreetMap. While the server infrastructure can serve as a blueprint for similar endeavours, the created datasets are of interest themselves too.

show abstract

“…This makes the quality of POIs unstable. Mocnick gives four measures to assess the data quality [62]. We use the data-based grounding measure by comparing POIs we used with Baidu Map and found that the data quality of POIs in rural area is lower than urban area.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

Improving the Accuracy of Fine-Grained Population Mapping Using Population-Sensitive POIs

Zhao

Zhang

et al. 2019

Remote Sensing

View full text Add to dashboard Cite

Many methods have been used to generate gridded population maps by downscaling demographic data. As one of these methods, the accuracy of the dasymetric model depends heavily on the covariates. Point-of-interest (POI) data, as important covariates, have been widely used for population estimation. However, POIs are often used indiscriminately in existing studies. A few studies further used selected categories of POIs identified based only on the nonspatial quantitative relationship between the POIs and population. In this paper, the spatial association between the POIs and population distribution was considered to identify the POIs with a strong spatial correlation with the population distribution, i.e., population-sensitive POIs. The ability of population-sensitive POIs to improve the fine-grained population mapping accuracy was explored by comparing the results of random forest dasymetric models driven by population-sensitive POIs, all POIs, and no POIs, along with the same sets of multisource remote sensing and social sensing data. The results showed that the model driven by population-sensitive POI had the highest accuracy. Population-sensitive POIs were also more effective in improving the population mapping accuracy than were POIs selected based only on their quantitative relationship with the population. The model built using population-sensitive POIs also performed better than the two popular gridded population datasets WorldPop and LandScan. The model we proposed in this study can be used to generate accurate spatial population distribution information and contributes to achieving more reliable analyses of population-related social problems.Dasymetric mapping, on the other hand, was used to generate the 1-km LandScan [5] and the 100-m WorldPop [18] annual population datasets.Dasymetric mapping is a well-known cartographic approach for generating high-resolution population maps [22][23][24]. The idea of dasymetric mapping is to generate a weighting layer using different kinds of covariates. This layer will then be used to redistribute the demographic data. Dasymetric mapping has been demonstrated to be more effective in generating more accurate population estimates than other approaches [25,26]. In the context of dasymetric mapping, the accuracy of the population estimates depends greatly on the covariates used [27]. Remote sensing products and geospatial big data are the most commonly used covariates in dasymetric mapping [1,3,18,[28][29][30][31][32][33][34][35]. Remotely sensed population-related products, such as land use/land cover (LULC) data and nighttime light (NTL) data, could show the actual surface conditions that reflect the physical factors that affect the population distribution. However, these data often lack semantic information. Semantic information can be used to describe the relationship between geographic attributes and geographic features, such as different human activities occurring at accurate geographic locations. With the development of location-aware devices and the emergence and p...

show abstract

A grounding-based ontology of data quality measures

Cited by 24 publications

References 0 publications

Citizens’ Spatial Footprint on Twitter—Anomaly, Trend and Bias Investigation in Istanbul

Citizens’ Spatial Footprint on Twitter—Anomaly, Trend and Bias Investigation in Istanbul

Open source data mining infrastructure for exploring and analysing OpenStreetMap

Improving the Accuracy of Fine-Grained Population Mapping Using Population-Sensitive POIs

Contact Info

Product

Resources

About