Data on population at building level is required for various purposes. However, to protect privacy, government population data is aggregated. Population estimates at finer scales can be obtained through areal interpolation, a process where data from a first spatial unit system is transferred to another system. Areal interpolation can be conducted with ancillary data that guide the redistribution of population. For population estimation at the building level, common ancillary data include three-dimensional data on buildings, obtained through costly processes such as LiDAR. Meanwhile, volunteered geographic information (VGI) is emerging as a new category of data and is already used for purposes related to urban management. The objective of this paper is to present an alternative approach for building level areal interpolation that uses VGI as ancillary data. The proposed method integrates existing interpolation techniques, i.e., multi-class dasymetric mapping and interpolation by surface volume integration; data on building footprints and points-of-interest (POIs) extracted from OpenStreetMap (OSM) are used to refine population estimates at building level. A case study was conducted for the city of Hamburg and the results were compared using different types of POIs. The results suggest that VGI can be used to accurately estimate population distribution, but that further research is needed to understand how POIs can reveal population distribution patterns.

The main objective of this research is to, first, monitor urban sprawl in the metropolis of Tehran and, second, to assess the CA–Markov model in the simulation of land-use change. Land-use changes generally occur in developing countries through new building construction. The rapid pace of this development has brought forward a number of research activities involving new applications for modelling this phenomenon. In this research, urban sprawl is examined in the urban fringe of Tehran and, subsequently, the CA–Markov model is implemented in order to evaluate the model. The CA–Markov model is a spatially explicit model for land-change modelling, which has not been implemented very frequently to track urban expansion. This model, however, which is an integrated module of both cellular automata and Markov Chain models, predicts forthcoming changes over time, based on the past use of land in the research area. Urban expansion between 1986 and 2006 was simulated through employing the model in the metropolis of Tehran in order to calibrate and customise the model through a training phase. Consequently, the optimal rules to achieve the most accurate results were retrieved and input to simulate future land-use situations, i.e. 2016 and 2026. The achieved results represent a big conversion of green areas and open lands to built-up areas.

This chapter discusses the broad and complex topic of data quality in citizen science – a contested arena because different projects and stakeholders aspire to different levels of data accuracy. In this chapter, we consider how we ensure the validity and reliability of data generated by citizen scientists and citizen science projects. We show that this is an essential methodological question that has emerged within a highly contested field in recent years. Data quality means different things to different stakeholders. This is no surprise as quality is always a broad spectrum, and nearly 200 terms are in use to describe it, regardless of the approach. We seek to deliver a high-level overview of the main themes and issues in data quality in citizen science, mechanisms to ensure and improve quality, and some conclusions on best practice and ways forwards. We encourage citizen science projects to share insights on their data practice failures. Finally, we show how data quality assurance gives credibility, reputation, and sustainability to citizen science projects.

A large number of applications have been launched to gather geo-located information from the public. This article introduces an approach toward generating land-use patterns from volunteered geographic information (VGI) without applying remote-sensing techniques and/or engaging official data. Hence, collaboratively collected OpenStreetMap (OSM) data sets are employed to map land-use patterns in Vienna, Austria. Initially the spatial pattern of the landscape was delineated and thereafter the most relevant land type was assigned to each land parcel through a hierarchical GIS-based decision tree approach. To evaluate the proposed approach, the results are compared with the Global Monitoring for Environment and Security Urban Atlas (GMESUA) data. The results are compared in two ways: first, the texture of the resulting land-use patterns is analyzed using texture-variability analysis. Second, the attributes assigned to each land segment are evaluated. The achieved land-use map shows kappa indices of 91, 79, and 76% agreement for location in comparison with the GMESUA data set at three levels of classification. Furthermore, the attributes of the two data sets match at 81, 67, and 65%. The results demonstrate that this approach opens a promising avenue to integrate freely available VGI to map land-use patterns for environmental planning purposes.