Geospatial Digital Rights Management

This Chapter introduces the need for general Digital Rights Management (DRM) requirements. Further, it intertwines DRM with its spatial counterpart, Geospatial DRM (GeoDRM). However, unlike DRM, GeoDRM is far much complicated due to issues such as the development of Web Mapping technology among other issues. The Chapter discusses the ability of GeoDRM to mitigate transgression of Intellectual Property Rights (IPR). Highlighting economical and environmental wellbeing and other benefits of Spatial Data Infrastructure (SDI) geared towards global sustainable developments, the Chapter focuses on challenges of National Spatial Data Infrastructures (NSDIs) and Regional SDIs and the need to harmonize their standards for the upward mobility of global SDI (GSDI). Emphasizing the undisputed need for Local, Regional and Global Spatial Data Infrastructures (SDIs), in the presence of various Geo-communities and different GeoDRM models, the Chapter concludes that capacity building need to be urgently but carefully harnessed across all levels in order to develop cohesive GeoDRM policies.

Gathering Road Safety Critical Information From Users

Traffic roads are a core element of GIS and many volunteered systems like openstreetmaps have the goal to make road data publicly available. Road users collecting geographical information and sharing them according some rules are a great opportunity to make our roads a safer place. Traffic accidents are a major cause of death and with increase in urbanization and motorization the risk is expected to rise higher. Research regarding road safety is mostly reactive; sections of the road where a lot of accidents has already happened are investigated and possibly causes are identified and then improved. This means, that people have to die in order to make those road sections safe. The system described in this chapter is a proactive method and can be operated by the community or some responsible authority. The gathered data is also very useful for different research areas like social sciences or civil engineering.

Leveraging Volunteered Geographic Information to Improve Disaster Resilience

Volunteered Geographic Information (VGI) has emerged as an important additional source of information for improving the resilience of cities and communities in the face of natural hazards and extreme weather events. This chapter summarizes the existing research in this area and offers an interdisciplinary perspective of the challenges to be overcome, by presenting AGORA: A Geospatial Open collaboRative Architecture for building resilience against disasters and extreme events. AGORA structures the challenges of using VGI for disaster management into three layers: acquisition, integration and application. The chapter describes the research challenges involved in each of these layers, as well as reporting on the results achieved so far and the lessons learned in the context of flood risk management in Brazil. Furthermore, the chapter concludes by setting out an interdisciplinary research agenda for leveraging VGI to improve disaster resilience.

Mapping Regional Landscape by Using OpenstreetMap (OSM)

A forest patterns map over a large extent at high spatial resolution is a heavily computation task but is critical to most regions. There are two major difficulties in generating the classification maps at regional scale: large training points sets and expensive computation cost in classifier modelling. As one of the most well-known Volunteered Geographic Information (VGI) initiatives, OpenstreetMap contributes not only on road network distributions, but the potential of justify land cover and land use. Google Earth Engine is a platform designed for cloud-based mapping with a strong computing power. In this study, we proposed a new approach to generating forest cover map and quantifying road-caused forest fragmentations by using OpenstreetMap in conjunction with remote sensing dataset stored in Google Earth Engine. Additionally, the landscape metrics produced after incorporating OpenStreetMap (OSM) with the forest spatial pattern layers from our output indicated significant levels of forest fragmentation in Yucatan peninsula.

Emotional and Subjective Volunteered Geographical Information

Crowdsroucing of emotional information can take many forms, from social networks data mining to large-scale surveys. The author presents the case-study of emotional mapping in Ostrava´s district Ostrava-Poruba, Czech Republic. Together with the local administration, the author crowdsourced the emotional perceptions of the location from almost 400 citizens, who created 4,051 spatial features. Additional to the spatial data there were 1,244 comments and suggestions for improvements in the district. Furthermore, the author is looking for patterns and hot-spots within the city and if there are any relevant linkages between certain emotions and spatial locations within the city.

Improving Accessibility Through VGI and Crowdsourcing

Accessibility is an important element in the life of those who have certain limitations, such as the physically disabled and visually impaired people. However, one of the greatest challenges for this group is to find paths and areas adapted to their limitations while performing their daily activities, since not all environments they explore have these characteristics. Volunteered Geographic Information (VGI) and the crowdsourcing technique appear to be quite useful to develop solutions to overcome these challenges, since these techniques are naturally cheap as they rely on human sensors as the main agent of information delivery. In this chapter, we discuss how these techniques can help mitigate accessibility problems and present some existing research and applications in the field.

Quality of Urban Life Index From Location-Based Social Networks Data

Several indicators are developed to support the decision-making processes in public policy for urban planning. Some of them seek to measure the quality of urban life. For example, the city of Belo Horizonte developed and uses an index called Quality of Urban Life Index, which identifies inequalities within the city, and therefore, those areas that need more investment. This index is calculated by measuring the availability of various kinds of services (e.g. education, infrastructure) and their accessibility (based on travel time and mobility data). For that, data from several government sources must be collected and used, which can delay updates of index values. In this chapter, the authors describe how data from Location-Based Social Networks (LBSN) can be used to calculate urban indicators, and hence, how they could be used as an alternative data source for estimating quality of urban life with faster results to support urban planning policies.

Traditional vs. Machine-Learning Techniques for OSM Quality Assessment

Nowadays an ever-increasing number of applications require complete and up-to-date spatial data, in particular maps. However, mapping is an expensive process and the vastness and dynamics of our world usually render centralized and authoritative maps outdated and incomplete. In this context crowd-sourced maps have the potential to provide a complete, up-to-date, and free representation of our world. However, the proliferation of such maps largely remains limited due to concerns about their data quality. While most of the current data quality assessment mechanisms for such maps require referencing to authoritative maps, we argue that such referencing of a crowd-sourced spatial database is ineffective. Instead we focus on the use of machine learning techniques that we believe have the potential to not only allow the assessment but also to recommend the improvement of the quality of crowd-sourced maps without referencing to external databases. This chapter gives an overview of these approaches.

Quality Evaluation of Volunteered Geographic Information

A large amount of crowd-sourced geospatial data have been created in recent years due to the interactivity of Web 2.0 and the availability of Global Positioning System (GPS). This geo-information is typically referred to as volunteered geographic information (VGI). OpenStreetMap (OSM) is a popular VGI platform that allows users to create or edit maps using GPS-enabled devices or aerial imageries. The issue of quality of geo-information generated by OSM has become a trending research topic because of the large size of the dataset and the inapplicability of Linus' Law in a geospatial context. This chapter systematically reviews the quality evaluation process of OSM, and demonstrates a case study of London, Canada for the assessment of completeness, positional accuracy and attribute accuracy. The findings of the quality evaluation can potentially serve as a guide of cartographic product selection and provide a better understanding of the development of OSM quality over geographic space and time.

Quality Assessment of Volunteered Geographic Information for Educational Planning

Volunteered Geographic Information is the term used to describe the process of collecting spatial data using a network of volunteers. The approach collects spatial data to build maps which are often freely accessible. The maps and the underlying data can be used by the public, companies and government agencies for a variety of tasks such as route finding. Given that untrained volunteers may collect the spatial data, questions regarding the quality of VGI have been raised. Several studies have emerged to assess the quality (positional, semantic and thematic accuracy) of VGI by comparing the data to ground truth. This approach fails to capture the quality of VGI for domain specific tasks. In this chapter we examine the quality of VGI for an educational planning task in Islamabad, Pakistan, and show that while the data may be suitable for route finding tasks, they are insufficient for educational planning alone.