Applications Development

Applications Development

DOI: 10.4018/978-1-7998-1916-5.ch013


The chapter synthetizes the applications development process. In recent years there has been a proliferation of geospatial information systems software, accompanied by an ever-increasing capacity and range of functionality, making selection decisions more complex. A geospatial information system is more than a collection of software and hardware. Rather, the system is an integrated information management solution that includes data, personnel, procedures, standards. When developing a planning support system, it is important to recognize that there will be a range of different user groups with different needs, expectations, and expertise.
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The scope and ambition of the 2030 Agenda requires the design, implementation and monitoring of evidence-based policies using the best data and information available from multiple sources – including administrative data across the national statistical system, and data ecosystems more broadly. In this context, the Data Commons Framework introduced in the previous section can help us to understand the nature of the many data interoperability challenges that need to be addressed to support evidence-based decision making to achieve the SDGs.

Sharing data and information in the sustainable development field necessitates having a common understanding of the semantics used by all groups of stakeholders involved, ensuring that statistical data can be presented online on interactive maps combined with data gleaned from satellite and other observational and sensory sources and sometimes further reinforced by perceptions data generated by citizens themselves (so-called citizen generated data, or CGD). Common ways of organizing data, and information are needed to enable the exchange of knowledge between policy makers and development practitioners.

Another component to realizing effective data sharing, and particularly common semantics, is the use of industry standards. Across a number of sectors, there are both information models and accepted terminologies/coding systems, which provide the semantic foundation for the sharing of information. Key to this sharing is the ability to not only share labels, but to maintain consistency of meaning, particularly across organizations or national boundaries.

From a data perspective, the SDG data ecosystem is characterized by several tensions:

  • Between global and local data needs – for instance between globally comparable statistics and disaggregated data that is compiled for local decision-making;

  • Between top-down data producers (such as UN agencies or multilateral and bilateral Development entities) and bottom-up ones such as small civil society organizations or local companies;

  • Between structured data exchange processes, such those based on the Statistical Data and Metadata eXchange (SDMX) suite of standards, and more organic processes, such as informal incountry data sharing between development actors; and

  • Between data producers and users from sectoral (health, education, etc.) and cross-cutting (gender, human-rights, partnerships) domains.

Within this complex matrix of processes and different levels of capacity and resources available for investment in data, coordination is key.

Resolving the problem requires a coordinated approach and set of common guidelines across governments that consider interoperability from the outset when it comes to the procurement of IT solutions. This requires ensuring that data management and governance principles become integral components of organizational strategies and business processes. At a more systemic level, it may also mean taking a leaf out of the book of international standard development organizations such as the World Wide Web Consortium (W3C), the International Organization for Standardization (ISO), the Open Geospatial Consortium (OGC), and others.

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