Antony K. Cooper

Standards play an important role in achieving the objectives of a spatial data infrastructure. However, standards can be difficult to understand and implement for those with limited exposure to them. The South African Spatial Data Infrastructure (SASDI) aims to facilitate the capture, management, maintenance, integration, distribution and use of spatial information. To decide whether a SASDI data set is fit for a specific purpose, users need information about its quality. SANS 19157:2014, Geographic information – Data quality, specifies how the quality of geospatial data can be described and assessed. The Committee for Spatial Information (CSI), responsible for implementing SASDI, identified the need to train geospatial data custodians in implementing SANS 19157. While custodians were eager to learn, several barriers prohibited presentation of training in a ‘traditional’ classroom setting. These barriers included the costs and time to travel from remote areas of the country to a training venue and challenges with scheduling the training at a time suitable to all participants. Online training was therefore delivered − however, structured in a way to overcome general ‘online fatigue’ after the pandemic. In this paper we present our experiences in presenting training on SANS 19157 to professionals responsible for geospatial data sets. We also share the lessons learnt from the novel structure for online training.

Land includes not just the surface of the Earth but also the air above and the spaces below (including the oceans, lakes, rivers, and other bodies of water). Land availability can contribute to income, nutrition, health, education, and social services. Many people living on customary lands have insecure access to land, raising the risks of destruction of cultural land-use practices, forced evictions, social conflicts, and poverty. Despite a high-quality cadastre covering South Africa's surveyed or formalized areas, much of the rural population lives in former Bantustans, on land outside of the formal land tenure system. Secure tenure for all tenure types is essential to attaining at least 10 of the United Nations' 17 Sustainable Development Goals (SDGs). These SDGs are linked to people-to-land relationships; hence the value of land and its administration is widely acknowledged, and is reflected in the SDGs. SDG target 1.4 aims at tenure security for all. This target requires land administration systems that clarify and formalise land rights to promote secure land markets, poverty alleviation, and national development (Bexell and Jönsson, 2017; UN-Habitat, 2018). This demonstrates that the land administration is important for the global development agenda (Jahan et al., 2015; Koeva et al., 2020). Nations prosper with efficient land administration. Reliable land administration enables dynamic land markets and effective land use. It is important that all types of land ownership (public, private, communal, indigenous, customary, and informal) be considered. The modernization drive has dominated land tenure studies, and the standard responses to communal and customary land administration systems reflect this view of them as backwards and antithetical to progress.

The Digital Earth vision foresees the availability and accessibility of geospatial information to achieve the goals of sustainable development, economic growth and social well-being. In the case of urban areas, up-to-date geospatial information is essential for managing a city towards achieving these goals. The rapid shift from rural to urban areas globally puts pressure on local governments and they often struggle to find and organise the resources required to collect and maintain geospatial information that can help to address urban growth challenges. A spatial data infrastructure (SDI) can facilitate the availability and accessibility of geospatial information towards addressing national objectives, however, the involvement of local governments in an SDI can be a challenge. In this paper, we critique the role of municipalities against the backdrop of the developments of the South African SDI (SASDI) to date. The critique identifies five high-level shortcomings of the SASDI that have led to the limited participation of municipalities. Based on the shortcomings, we provide recommendations for capacitating municipalities through SASDI so that the Digital Earth vision can also be achieved for municipalities. These recommendations are aimed at involving the local sphere of government in a national SDI and are equally applicable to other countries.

In this study, we model the risk of robbery in the City of Tshwane in South Africa. We use the collective knowledge of two prominent spatial theories of crime (social disorganization theory, and crime pattern theory) to guide the selection of data and employ rudimentary geospatial techniques to create a crude model that identifies the risk of future robbery incidents in the city. The model is validated using actual robbery incidences recorded for the city. Overall the model performs reasonably well with approximately 70% of future robbery incidences accurately identified within a small subset of the overall model. Developing countries such as South Africa are in dire need of crime risk intensity models that are simple, and not data intensive to allocate scarce crime prevention resources in a more optimal fashion. It is anticipated that this model is a first step in this regard.

Publicly-available mobile data can be used to derive fine grain commuting and travel patterns. These types of data include geocoded or geotagged discrete units of communication: messages, posts, tweets, status updates, check-ins, images and the like on a variety of social networking services. Clearly, there are ethical issues concerning the use of such data, particularly the invasion of privacy. A review of the literature has been done to explore these issues.

Spatial data infrastructure No mapping agency can expect to capture and process entirely by itself, all the geospatial data needed for its products. The agency needs data sets from elsewhere and workflows and protocols for creating its various products. Unsurprisingly, such workflows and inter-institutional arrangements have evolved into broader collaborations, particularly as spatial data infrastructures (SDIs). An SDI is an evolving concept about facilitating and coordinating the exchange and sharing of geospatial data and services between stakeholders from different levels in the geospatial data community. The Commission on SDI & Standards (and its predecessors) of the International Cartographic Association (ICA) has developed formal models of an SDI, using the viewpoints of the Reference Model for Open Distributed Processing (RM-ODP) and the Unified Modeling Language (UML) for the detailed modelling. The Commission described an SDI from the Enterprise Viewpoint (purpose, scope and policies for an SDI), Information Viewpoint (semantics of information and information processing in an SDI) and Computational Viewpoint (functional decomposition of the SDI into a set of services that interact through interfaces) (Hjelmager et al., 2008, Cooper et al., 2012). The ICA Commission did not investigate the Engineering and Technology Viewpoints of an SDI, because they are implementation-specific and the Commission has aimed at providing technology-independent models for an SDI. Other researchers have used the Engineering and Technology Viewpoints for specific SDIs.SDI stakeholdersThe Enterprise Viewpoint model (Hjelmager et al., 2008) included six types of stakeholders in an SDI: Policy Maker, Producer, Provider, Broker, Value-added Reseller and End User. Any one person, group, committee or organisation can have multiple stakeholder roles, which could be simultaneously. Each stakeholder can have an active or passive relationship with any activities or components in an SDI. A stakeholder in an SDI could also be considered to be an actor (Oliveira and Lisboa-Filho, 2015). Various trends are increasing the number of stakeholders in the SDI, their associated diversity and heterogeneity, and the resources at their disposal.Subsequently, the ICA Commission assessed whether or not these SDI models catered for volunteered geographical information (VGI), that is, user-generated geospatial content, or user-generated content with geospatial components. The models are sufficiently robust to do so, though it was then realised that the SDI stakeholders needed to be described in more detail as subtypes of the general roles of the six types of SDI stakeholders. The Commission then identified 24 subtypes for the stakeholders, with several of these subtypes having further sub-subtypes. For example, a Producer could have a subtype Status, which in turn could have the subtypes Official Mapping Agency or Commercial Mapping Agency (Cooper et al., 2011). However, a better term than subtype might be specialization, special case, attribute, activity or role. Collectively, these Enterprise, Information and Computational Viewpoints and the detailed stakeholders form what is known colloquially in the literature as the ICA model or ICA’s formal model of an SDI, such as by (Oliveira et al., 2016). The ICA SDI model is not meant to be a prescriptive model of an SDI, but rather a descriptive one. The ICA SDI model is also implementation-independent and hence somewhat Issues with the stakeholders While all the stakeholder subtypes in an SDI were given definitions, some of the types and subtypes were confused with one another by other researchers. Further, it became clear that not all stakeholders have benevolent relationships with SDIs, whether the stakeholders be malevolent, or just too idle or incompetent to help the SDI succeed. Hence, it is necessary to update the ICA SDI stakeholder model.We have reviewed the literature and several authors have proposed improvements to the ICA SDI stakeholder model and have highlighted parts of the model that are not well understood. Further, the ICA Commission itself has also revisited the model at previous meetings. The key contributions concerning the stakeholder model are summarised in this paper. They include: Making it clear that any contributor to an SDI should control their assets in the SDI and can withdraw them when they see fit. Catering explicitly for a contact or representative for any community. Including an educator role, and by extension, a researcher role. Including a role for those who fund the SDI itself and fund all of the relevant stakeholders so that they can function effectively. Catering for how stakeholders could be assembled together, such as in special-interest groups. Catering for oversight explicitly, such as through an ombud. Making it clear that a subtype of a stakeholder is not necessarily subordinate to that stakeholder. Characterising the maturity of a stakeholder. Including a subtype for public-sector producers that are not official mapping agencies. Whether or not agency, contracting or representation roles or functions should be deemed to be separate stakeholders in the model. Improving the End User, which has only two subtypes. Catering for shifts in the roles and characteristics of SDI stakeholders over time. Including service producers explicitly. Catering for representatives of the SDI who liaise with other organisations or other SDIs. Including explicitly some of the roles within the Secretariat, such as systems administration, technical support and quality assurance. Some attributes could be common across all or most of the six types of stakeholders in an SDI, such as their motivation for contributing to, or using, any particular SDI; their authority or ability to contribute; whether or not they are liable for their contributions and any consequences thereof; and the ethics related to the contribution, including invasion of privacy,...