Volunteered Geographic Information (VGI) is currently a "hot topic" in the GIS community. The OpenStreetMap (OSM) project is one of the most popular and well supported examples of VGI. Traditional measures of spatial data quality are often not applicable to OSM as in many cases it is not possible to access ground-truth spatial data for all regions mapped by OSM. We investigate to develop measures of quality for OSM which operate in an unsupervised manner without reference to a "trusted" source of groundtruth data. We provide results of analysis of OSM data from several European countries. The results highlight specific quality issues in OSM. Results of comparing OSM with ground-truth data for Ireland are also presented.

This paper describes the results of an analysis of the OpenStreetMap (OSM) database for the United Kingdom (UK) and Ireland (correct to April 2011). 15; 640 OSM ways (polygons and polylines), resulting in 316; 949 unique versions of these objects, were extracted and analysed from the OSM database for the UK and Ireland. In our analysis we only considered “heavily edited” objects in OSM: objects which have been edited 15 or more times. Our results show that there is no strong relationship between increasing numbers of contributors to a given object and the number of tags (metadata) assigned to it. 87% of contributions/edits to these objects are performed by 11% of the total 4128 contributors. In 79% of edits additional spatial data (nodes) are added to objects. The results in this paper do not attempt to evaluate the OSM data as good/poor quality but rather informs potential consumers of OSM data that the data itself is changing over time. In developing a better understanding of the characteristics of “heavily edited” objects there may be opportunities to use historical analysis in working towards quality indicators for OSM in the future

In this article we describe the analysis of 25,000 objects from the OpenStreetMap (OSM) databases of Ireland, United Kingdom, Germany, and Austria. The objects are selected as exhibiting the characteristics of “heavily edited” objects. We consider “heavily edited” objects as having 15 or more versions over the object's lifetime. Our results indicate that there are some serious issues arising from the way contributors tag or annotate objects in OSM. Values assigned to the “name” and “highway” attributes are often subject to frequent and unexpected change. However, this “tag flip‐flopping” is not found to be strongly correlated with increasing numbers of contributors. We also show problems with usage of the OSM ontology/controlled vocabularly. The majority of errors occurring were caused by contributors choosing values from the ontology “by hand” and spelling these values incorrectly. These issues could have a potentially detrimental effect on the quality of OSM data while at the same time damaging the perception of OSM in the GIS community. The current state of tagging and annotation in OSM is not perfect. We feel that the problems identified are a combination of the flexibility of the tagging process in OSM and the lack of a strict mechanism for checking adherence to the OSM ontology for specific core attributes. More studies related to comparing the names of features in OSM to recognized ground‐truth datasets are required.

Background Sentiment analysis (SA) is a subfield of natural language processing whose aim is to automatically classify the sentiment expressed in a free text. It has found practical applications across a wide range of societal contexts including marketing, economy, and politics. This review focuses specifically on applications related to health, which is defined as “a state of complete physical, mental, and social well-being and not merely the absence of disease or infirmity.” Objective This study aimed to establish the state of the art in SA related to health and well-being by conducting a systematic review of the recent literature. To capture the perspective of those individuals whose health and well-being are affected, we focused specifically on spontaneously generated content and not necessarily that of health care professionals. Methods Our methodology is based on the guidelines for performing systematic reviews. In January 2019, we used PubMed, a multifaceted interface, to perform a literature search against MEDLINE. We identified a total of 86 relevant studies and extracted data about the datasets analyzed, discourse topics, data creators, downstream applications, algorithms used, and their evaluation. Results The majority of data were collected from social networking and Web-based retailing platforms. The primary purpose of online conversations is to exchange information and provide social support online. These communities tend to form around health conditions with high severity and chronicity rates. Different treatments and services discussed include medications, vaccination, surgery, orthodontic services, individual physicians, and health care services in general. We identified 5 roles with respect to health and well-being among the authors of the types of spontaneously generated narratives considered in this review: a sufferer, an addict, a patient, a carer, and a suicide victim. Out of 86 studies considered, only 4 reported the demographic characteristics. A wide range of methods were used to perform SA. Most common choices included support vector machines, naïve Bayesian learning, decision trees, logistic regression, and adaptive boosting. In contrast with general trends in SA research, only 1 study used deep learning. The performance lags behind the state of the art achieved in other domains when measured by F-score, which was found to be below 60% on average. In the context of SA, the domain of health and well-being was found to be resource poor: few domain-specific corpora and lexica are shared publicly for research purposes. Conclusions SA results in the area of health and well-being lag behind those in other domains. It is yet unclear if this is because of the intrinsic differences between the domains and their respective sublanguages, the size of training datasets, the lack of domain-specific sentiment lexica, or the choice of algorithms.

a b s t r a c tIn this article we analyse the growth of OpenStreetMap (OSM) representations for three street networks in Ireland. In each case we demonstrate the growth to be governed by two elementary spatial processes of densification and exploration which are responsible for increasing the local density of the network and expanding the network into new areas respectively. We also examine summary statistics describing each network topology and show these to be a consequence of the same processes. This represents the discovery of a novel link between different aspects of the growth.

OpenStreetMap (OSM) is a very well known and popular Volunteered Geographic Information (VGI) project on the Internet. In January 2013 OSM gained it's one millionth registered member. Several studies have shown that only a small percentage of these registered members carry out the large majority of the mapping and map editing work. In this paper we discuss results from a social-network based analysis of seven major cities in OSM in an effort to understand if there is quantitative evidence of interaction and collaboration between OSM members in these areas. Are OSM contributors working on their own to build OSM databases in these cities or is there evidence of collaboration between OSM contributors. We find that in many cases high frequent contributors ("senior mappers") perform very large amounts of mapping work on their own but do interact (edit/update) contributions from lower frequency contributors.

The initial step in most object-based classification methodologies is the application of a segmentation algorithm to define objects. Modelling the human visual process of object segmentation is a challenging task. Many theories in cognitive psychology propose that the human visual system (HVS) initially segments scenes into areas of uniform visual properties or primitive objects. If an accurate primitive-object segmentation algorithm is ever to be realized, a procedure must be in place to evaluate potential solutions. The most commonly used strategy to evaluate segmentation quality is a comparison against ground truth captured by human interpretation. A cognitive experiment reveals that ground truth captured in such a manner is at a larger scale than the desired primitive-object scale. To overcome this difficulty we consider the possibility of evaluating segmentation quality in an unsupervised manner without ground truth. Two requirements for any method which attempts to perform segmentation evaluation in such a manner are proposed, and the importance of these is illustrated by the poor performance of a metric which fails to meet them both. A novel metric, known as the spatial unsupervised (SU) metric, which meets both the requirements is proposed. Results demonstrate the SU metric to be a more reliable metric of segmentation quality compared to existing methods.

Multiple domination models for placement of electric vehicle charging stations in road networks, Computers and Operations Research (2018),