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Practitioners bemoan lack of data as one of the biggest obstacles to progress towards global sustainable development goals. This paper explores a scaled-up participatory method developed by YouthMappers, for creating missing geospatial data derived from remotely sensed imagery in order to contribute to persistent data needs in the context of the United Nations Sustainable Development Goals (SDGs). We explore the application of this method to a case related to SDG 3 on Health. We document how our approach centered on creating a global academic network designed to engage and empower university students and their faculty mentors to participate in broader efforts to create open, free spatial data on open platforms to inform humanitarian and development objectives outlined by the funding agency, the United States Agency for International Development (USAID). This approach expressly links supply and demand for geospatial knowledge by connecting specific needs for geographic information to specific development objectives in targeted places where USAID works to end extreme poverty. We discuss the rationale and context for the methodology as it draws from and builds upon prominent literature of participatory GIS (PGIS) and volunteered geographic information (VGI). We demonstrate how the mapping of building and road infrastructure in Mozambique and Kenya was carried out in order to provide information for an insecticide spray campaign to prevent malaria and protect public health. Throughout these efforts, steps are taken to ensure spatial data quality and to offer opportunities for youth volunteer embeddedness in mapping tasks and themes in places where students otherwise would not engage with real world data or connect with peers from different countries. We reflect on the opportunities and challenges for how this scaled-up “remote participatory sensing” approach to spatial data creation can inform development projects in the context of the SDGs.

Modern Chinese cities are defined from the administrative view and classified into several administrative categories, which makes it inconsistent between Chinese cities and their counterparts in western countries. Without easy access to fine-scale data, researchers have to rely heavily on statistical and aggregated indicators available in officially released yearbooks, to understand Chinese city system. Not to mention the data quality of yearbooks, it is problematic that a large number of towns or downtown areas of counties are not addressed in yearbooks. To address this issue, as a following study of Long et al. (2016), we have redefined the Chinese city system, using percolation theory in the light of newly emerging big/open data. In this paper, we propose our alternative definition of a city with road/street junctions, and present the methodology for extracting city system for the whole country with national wide road junctions. A city is defined as “a spatial cluster with a minimum of 100 road/street junctions within a 300 m distance threshold”. Totally we identify 4629 redefined cities with a total urban area of 64,144 km2 for the whole China. We observe total city number increases from 2273 in 2009 to 4629 in 2014. We find that expanded urban area during 2009 and 2014, comparing with urban areas in 2009 are associated with 73.3% road junction density, 25.3% POI density and 5.5% online comment density. In addition, we benchmark our results with the conventional Chinese city system by using yearbooks.

In recent years a mix of Open Data and commercial sources have been used to build geodemographic classifications of neighbourhoods. In this paper we argue that geodemographics are coming to embody new thinking about the emergent mixed Big Data economy. This has implications for openness and full scientific reproducibility of classifications, as well as the engagement of stakeholders in the process of building classifications. We propose and implement an operational framework for blending open and other data sources that can stimulate development of classifications that are more timely and data rich yet sufficiently open to peer scrutiny. We illustrate these ideas and challenges by describing the creation and content of the London Workplace Zone Classification.