Citizens are increasingly becoming an important source of geographic information, sometimes entering domains that had until recently been the exclusive realm of authoritative agencies. This activity has a very diverse character as it can, amongst other things, be active or passive, involve spatial or aspatial data and the data provided can be variable in terms of key attributes such as format, description and quality. Unsurprisingly, therefore, there are a variety of terms used to describe data arising from citizens. In this article, the expressions used to describe citizen sensing of geographic information are reviewed and their use over time explored, prior to categorizing them and highlighting key issues in the current state of the subject. The latter involved a review of 100 Internet sites with particular focus on their thematic topic, the nature of the data and issues such as incentives for contributors. This review suggests that most sites involve active rather than passive contribution, with citizens typically motivated by the desire to aid a worthy cause, often receiving little training. As such, this article provides a snapshot of the role of citizens in crowdsourcing geographic information and a guide to the current status of this rapidly emerging and evolving subject.

- To implement the analysis of soil erosion with the USLE in a GIS environment, a new workflow has been developed with the ArcGIS Model Builder. The aim of this four-part framework is to accelerate data processing and to ensure comparability of soil erosion risk maps. The first submodel generates the stream network with connected catchments, computes slope conditions and the LS factor in USLE based on the DEM. The second submodel integrates stream lines, roads, catchment boundaries, land cover, land use, and soil maps. This combined dataset is the basis for the preparation of other USLE-factors. The third submodel estimates soil loss, and creates zonal statistics of soil erosion. The fourth submodel classifies soil loss into categories enabling the comparison of modelled and observed soil erosion. The framework was applied in a small forested catchment in Hungary. Although there is significant deviation between the erosion of different land covers, the predicted specific soil loss does not increase above the tolerance limit in any area unit. The predicted surface soil erosion in forest subcompartments mostly depends on the slope conditions.

This study evaluates fear of crime perception and official crime statistics in a spatial context, by applying digital sketch maps and statistical GIS methods. The study aims to determine explanatory motives of fear of crime by comparing results of selected large, medium and small sized Hungarian cities. Fear of crime information of residents were collected by using a web application, which gave the possibility to mark regions on a map, where respondents have a sense of safety or feel fear. These digital sketch maps were processed by GIS tools, and were converted to grid data, in order to calculate comparable explanatory variables for fear of crime analysis. The grid-based normalised model reflected some similarities and differences between the observed cities. According to the outcomes, examples were found both in coincidences and opposite correlations of crime statistics and perception of unsafe places, highlighting the importance of locality in fear of crime research. Additionally, the results mirrored that the size of the city or the respondent's sex does not significantly influence the overall judgment of places, rather the absolute number of safe markings and the local number of registered crime events could affect local results.Although numerous studies have proved that various spatial factors can trigger fear of crime, the fact is that crime rates in certain areas are not necessarily as high as expected [16,17], consequently, additional explanatory factors should be considered as well. Doran and Lees [18], for example, found relationship between fear of crime and physical disorder. Later, Doran and Burgess [3] proved that the environment in general plays an important role in fear of crime. It was recently shown that even improvements in the environment can have a positive effect on reducing fear of crime [19]. Furthermore, a large amount of studies confirmed that fears of crime are concentrated in areas, which can be described by definite environmental characteristics. For example Lederer and Leitner [20] concluded that fear of being a victim of burglary can be assigned to well-defined geographical hot spots, as well as it is connected with definite statistical features and even with areas having less technical protection.A typical problem of mapping fear of crime, though, is the lack of data on location perceptions. In many countries police have not completed any survey on this topic, therefore, their own data surveys should be made to accumulate information from, e.g., questionnaires [21] or from digital sketch maps, which would conceivably quantify-in a specific way-the level and the area of fear of crime within a city [22][23][24][25][26]. Additionally, several contemporary research papers on fear of crime apply modern IT technologies to draw a more precise picture of the issue. For example Solymosi et al. [27] developed a FOCA application (fear of crime application), which investigates fear of crime as a dynamic phenomenon by tracing the participants' activities, and in this way, the avoidance of ce...

The concept of Volunteered Geographic Information (VGI) is often exemplified by the mapping of features in OpenStreetMap (OSM), yet there are many other sources of VGI available. Some VGI is very focused on the creation of map-based products, while in other applications location is simply one attribute that is routinely collected, due to the proliferation of Global Positioning System (GPS) enabled devices, e.g. mobile phones and tablets. This chapter aims to provide an overview of the variety of sources of VGI currently available, categorised according to whether they can contribute to framework data (i.e. the type of data that are commonly part of the spatial data infrastructure of national mapping agencies and governments) or not and whether the data have been actively or passively collected. A range of examples are presented to illustrate the different types of VGI in each of these main categories. Finally, the chapter discusses some of the main issues surrounding the use of VGI and points to chapters in the book where these issues are described in more detail.

Environmental noise affects life and health within urban environments through interfering with sleep, rest, study and personal communication. Noise mapping is an important issue of local authorities but due to its requirements (staff, costs and frequency), the available data are limited or outdated. Our aim was to involve people with smartphones in the mapping process and to determine the accuracy of the measurements performed with these devices in a natural environment. The main questions were whether the measured data were dependent on the type of applied software and smartphones. We tested three software (Noise Watch, Noise Meter and Sound Level Meter) and 12 different smartphones. We evaluated the measurements with hypothesis testing and correlation analysis. Although the accuracy of smartphones was below the professional equipment, measurements can be conducted easily due to their availability; thus, a reliability analysis is important. We found that comparison between professional devices and smartphones in a laboratory was misleading as it lacks the environmental factors biasing the measurements. The best method to compare the measurements carried out with smartphones and professional Noise Meters was to use large number of measuring points in a heterogenic outdoor environment where the noise ranged from the low to large values. We revealed that both the applied software and smartphones have relevant effect on the measurements, and, although it is possible to use these devices for noise mapping, one should consider not to apply different software and smartphones. Accordingly, crowdsourcing is not a reliable data collection method because: (1) measurements should be supervised, (2) smartphones’ accuracy should be tested and (3) measurement circumstances should be the same. If any of these requirements are violated, the quality of the resulting maps can be questioned.