World Bank SDI Report - Introduction

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This article is a part of World Bank SDI Report.

Contents

Introduction

In August 2002 the US Office of Management and Budget issued a revised Circular A-16 which affirmed a coordinated approach to electronically develop the National Spatial Data Infrastructure (SDI) of the USA. The circular described the National SDI as the technology, policies, standards, human resources, and related activities necessary to acquire, process, distribute, use, maintain, and preserve spatial data[1]. In subsequent years, recognition of the economic and social need for countries to develop and implement policies for the efficient and coordinated management of their spatial data and services has become global and SDI's at the national, regional and global level have evolved with increasing resolve and commitment (Jackson et al., 2011[2]). Many countries worldwide, including those in the developed and developing worlds, are now engaged in the development of SDI. That is, the development of services that support public service delivery, ultimately to promote economic development, stimulate improved governance, and foster environmental sustainability through the application of geographical information (GI). Consequently, SDIs are increasingly being developed as spatial tools to support development and many are now at an advanced stage of implementation (Ibid.).

SDI can be seen as a framework comprising both technical and human dimensions. Technically, it includes the raw materials; the data, catalogues and dictionaries that facilitate discovery in addition to the software systems through which data is processed. However, SDIs also encompass policy issues, management and operational practices, as well as user inputs and requirements which must be considered. All of these aspects are connected in order to use geographical information in an efficient and flexible way. During the course of SDI evolution, the concept has come to be understood according to a set of guiding principles: It is accepted that SDI should enable the storage, availability and maintenance of spatial data; it should enable the combination of data from different sources and facilitate the sharing of this data between users and applications; data should be shared across different levels of jurisdiction and, the use of data should be allowed under controlled, but open conditions.

The development of SDI is occurring at all geographical scales from local community level projects through to state/provincial, regional, national and global programs. In the European Union (EU), in response to the INSPIRE Directive, scores of agencies are now in the process of coordinating their SDI activities. In Canada, GeoConnections[3], a national partnership program led by Natural Resources Canada, provides guidance and motivation for partnering agencies at all levels of government to join the Web-based Canadian NSDI. In the US, the Federal Geographic Data Committee (FGDC)[4], an interagency committee presently under the supervision of the Executive Office of Management and Budget (OMB), promotes the coordinated development and interoperability of geospatial data on a national basis, administering both the National Map and Geospatial One-Stop. In Australia and New Zealand, the inter-governmental council (ANZLIC)[5] is responsible for the coordination of spatial information management, working with other agencies such as Geoscience Australia, to provide a range of national fundamental datasets and manage the gateway to the Australia Spatial Data Directory (ASDD). Evidently, SDI is now moving to underpin a burgeoning information society and empower communities at all levels to become spatially enabled through the application of geospatial data (Rajabifard, 2006)[6].

The establishment of SDIs in developing countries has particular significance for international development given their relevance to monitoring and evaluating development outcomes. In this respect, as spatial data accessed through SDIs can support development, SDIs play an important role in meeting the ICT4D (Information and Communications Technology for Development) agenda; an international development movement which aims to apply digital technologies to the problems of the developing world (Unwin, 2009)[7]

Current ICT4D literature emphasises "simple" ICTs - such as mobile phones, laptop computers - and "simple" applications - such as email or SMS - and the developmental benefits these can bring, especially to individuals. However, there is also an important role for more "complex" ICT systems, such as Geographic Information Systems (GIS) (the software systems through which geospatial data is processed)[8], which can be used by governments and professional organisations, in realising developmental outcomes through the application of geospatial data. In practise, spatial or location-based data provides the framework within which most economic, environmental or social data is constructed as the spatial and temporal dimensions pervade most human activity. Whether the ICT technology employed is simple or complex the space and time dimensions need to be represented in order to effectively monitor, model or predict most human-based activity.

Despite significant advancements in ICT, GI and GIS, the development of SDIs requires effective treatment of data and handling of processes. ICTs play a critical role in the development of SDIs, especially innovative applications, including for example, the use of GIS on mobile phones (location-based services) as they can help to reduce, though not eliminate, the dislocation in human development caused by geographic distance (the "death of distance"), for instance in health, education, agriculture and rural development. On the other hand, our dependence on ICTs makes local geography ever more important; for instance, distance from a base station may determine mobile coverage while distance from a local exchange affects the quality of broadband access that is available in a particular locality.

With the increasing complexity of ICT combined with a growing awareness of the material and opportunity costs of poor GIS management practices, developing countries are now seeking advice on how to develop sound and robust SDIs. As well as the technical challenges of developing SDIs, there is a need for improved standardisation, organisation, storage, management and sharing of GI and GIS. In this context, there is a common need for knowledge on the development of SDIs at the national level.

Background to the Project

In recent years the World Bank has received an increasing number of enquiries from developing countries for advice on and support for establishing SDIs. In response, the Bank initiated in 2009 the project Using Geographic Information Systems and Spatial Data Infrastructure for Monitoring Development Outcomes. The project built on an emerging area of Bank expertise in the analysis of spatial data, notably in the realms of the 2009 World Development Report: Reshaping Economic Geography[9] and the CAPRA (Central America Probabilistic Risk Assessment) project[10]. The former of these projects highlights the importance of taking a geographical view of human development and the fact that scale and location matter, hence the emphasis on GI, GIS and SDIs. The greater use of GIS tools within the Bank specifically will enable powerful economic analysis and more imaginative visual display of data. This is exemplified, for instance, by the Online Atlas of the Millennium Development Goals[11] and the Data Visualiser [12]. It is estimated that more than 80 per cent of data items have a spatial component (Huxhold, 1991)[13]. When data is analysed in a GIS, hidden patterns of dependency and autocorrelation can be revealed, therefore providing a valuable tool for international development practitioners and stakeholders globally.

Funding support for the World Bank Project was provided by the Korean Trust Fund on ICT4D; a partnership between the national Government of the Republic of Korea and the former Global Information and Communications Technology (GICT) Department of the World Bank, designed to advance the ICT4D agenda. The US$15 million Fund is administered by infoDev (a global partnership program of the World Bank) and supports projects that demonstrate cutting-edge ICT4D solutions for economic and social growth and poverty reduction[14].

The broader aim of the project is to provide a global analysis of the capabilities of using GIS for tracking the achievement of the Millennium Development Goals (MDGs) (see 1.2) and also for providing technical assistance in two countries that made specific requests, Jordan and Uganda. The project intended to develop approaches to improve the ability of developing countries to manage an emerging new tool – geographic information systems – that could be used in support of their development programmes as well as the Bank's operations. Although the project focused on one specific application -- monitoring development outcomes and the achievement of the MDGs -- it is anticipated that the knowledge gained from the study should have much wider application, for instance in climate change adaptation, food security, transport management, innovation clusters etc. This Manual for developing SDIs was a specific intended outcome of the project: SDI development requires a high level of coordination amongst different ministries and private entities that manage different data sets, but the Manual is an essential step towards using GIS to affect development outcomes.

The project combined three main elements:

  • learning from Best Practice: case study reports on national SDI development in the Republic of Korea and Brazil;
  • learning by doing: technical assistance reports on proposed SDI development in Jordan and Uganda; and
  • diffusion of findings: the development of this global report, which provides a Manual for the development of spatial data infrastructure at the national level, backed up by a public website and other information resources.

The case study reports for Korea and Brazil provide an analysis of the potential of spatial data for modeling and monitoring development outcomes and how this can be improved through standardization The findings of those reports, as well as those for the technical assistance reports for Jordan and Uganda are encompassed within this handbook, forming the foundations, framework and in some cases substantive text to sections of the handbook, as well as appearing in full on the website.

Spatial Data Infrastructure and the Millennium Development Goals

SDI for Monitoring Development Outcomes

As infrastructure, the application areas for SDI are wide ranging, with SDI underpinning land information systems, national cadastres, environmental management, the electoral process, education, healthcare, e-government services, and transportation planning-to mention only a few public services. Common to programme planning across many of these services is the setting of quantitative and qualitative objectives and regularly measuring progress monitoring.

In Malawi, for example, the National Education Sector Plan has set a target of having female student enrolment in primary education reach 50.5%, reflective of the gender split for all children of the primary school cohort. Prior to 1994 (when free primary education was introduced), females were under-enrolled as some families favoured sending boys to school.

To redress this inequity, the Ministry of Education, Science, and Technology have implemented a series of programmes designed to bolster the enrolment of girls in primary school. Initially, the Ministry tracked nationally-aggregated statistics from enrolment summaries submitted by schools across the country. The schools were not geocoded and so interventions and programme planning to continue to encourage female enrolment remained nationally-focused. From 2000, more rigour was applied to analyzing these statistics and the schools were geocoded and approximate catchment areas were delineated around each school.

A simple national thematic map of female enrolment by school catchment area revealed significant intra-national variation: some parts of the country had already achieved parity while in others female under-enrolment persisted. That then led to the development of regionally-targeted interventions to supplement national-level campaigns. By 2007, females comprised 51.0% of primary school students. Malawi's experience in achieving gender parity in primary school enrolment cleanly illustrates the oft-cited adage that you can't manage what you can't measure and is but one of countless stories where policy development and interventions are guided by empirical evidence.

These kinds of monitoring and evaluation (M&E) exercises became de rigueur by the mid-1990s and are now standard in most bi- and multi-lateral development initiatives. They are referred to as indicator or tracking projects, that often employ metrics and scorecards, and whose programme management approach is sometimes referred to as results-based management.[15] Status reports that rely on such indicators are regularly published. The focus is on measuring progress towards medium- and long-term results and change rather than on activities and inputs. These types of M&E methodologies assist decision-making, deliver on accountability relationships, help to mitigate risk, and, ultimately, achieve SMART results (i.e., Specific, Measurable, Achievable, Relevant, and Time-bound). In the international development arena, they are important for transparency and accountability, especially to ensure that donor funds are delivering a reasonable quality and quantity of economic and social development relative to the public resources donated[16].

The Millennium Development Goals

Ratified by the General Assembly, September 2000, the United Nations Millennium Declaration (Resolution 55/2[17]), set forth specific objectives to end the scourge of poverty. The Millennium Development Goals (hereafter, MDGs) are just that: eight (8) development goals that, ideally, every member State of the UN will achieve by the target. The eight goals encompass 21 quantifiable targets. Progress towards those targets, and, by extension, the goals themselves is measured through 60 indicators/initiatives[18].


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Millennium Development Goals (MDGs)
Goals and Targets
(from the Millennium Declaration)
Indicators for monitoring progress
Goal 1: Eradicate extreme poverty and hunger
Target 1.A: Halve, between 1990 and 2015, the proportion of people whose income is less than one dollar a day 1.1 Proportion of population below $1 (PPP) per daya
1.2 Poverty gap ratio
1.3 Share of poorest quintile in national consumption
Target 1.B:Achieve full and productive employment and decent work for all, including women and young people
1.4 Growth rate of GDP per person employed
1.5 Employment-to-population ratio
1.6 Proportion of employed people living below $1 (PPP) per day
1.7 Proportion of own-account and contributing family workers in total employment
Target 1.C: Halve, between 1990 and 2015, the proportion of
people who suffer from hunger
1.8 Prevalence of underweight children under-five years of age
1.9 Proportion of population below minimum level of dietary energy consumption
Goal 2: Achieve universal primary education
Target 2.A: Ensure that, by 2015, children everywhere, boys and girls alike, will be able to complete a full course of primary schooling 2.1 Net enrolment ratio in primary education
2.2 Proportion of pupils starting grade 1 who reach last grade of primary
2.3 Literacy rate of 15-24 year-olds, women and men
Goal 3: Promote gender equality and empower women
Target 3.A: Eliminate gender disparity in primary and secondary education, preferably by 2005, and in all levels of education no later than 2015 3.1 Ratios of girls to boys in primary, secondary and tertiary education
3.2 Share of women in wage employment in the non-agricultural sector
3.3 Proportion of seats held by women in national parliament
Goal 4: Reduce child mortality 
Target 4.A: Reduce by two-thirds, between 1990 and 2015, the under-five mortality rate
 
4.1 Under-five mortality rate
4.2 Infant mortality rate
4.3 Proportion of 1 year-old children immunised against measles
Goal 5: Improve maternal health 
Target 5.A: Reduce by three quarters, between 1990 and 2015, the maternal mortality ratio 5.1 Maternal mortality ratio
5.2 Proportion of births attended by skilled health personnel
Target 5.B: Achieve, by 2015, universal access to reproductive health
5.3 Contraceptive prevalence rate
5.4 Adolescent birth rate
5.5 Antenatal care coverage (at least one visit and at least four visits)
5.6 Unmet need for family planning
Goal 6: Combat HIV/AIDS, malaria and other diseases
Target 6.A: Have halted by 2015 and begun to reverse the spread of HIV/AIDS



 
6.1 HIV prevalence among population aged 15-24 years
6.2 Condom use at last high-risk sex
6.3 Proportion of population aged 15-24 years with comprehensive correct knowledge of HIV/AIDS
6.4 Ratio of school attendance of orphans to school attendance of non-orphans aged 10-14 years
Target 6.B:Achieve, by 2010, universal access to treatment for HIV/AIDS for all those who need it 6.5 Proportion of population with advanced HIV infection with access to antiretroviral drugs
Target 6.C: Have halted by 2015 and begun to reverse the incidence of malaria and other major diseases



 
6.6 Incidence and death rates associated with malaria
6.7 Proportion of children under 5 sleeping under insecticide-treated bed nets
6.8 Proportion of children under 5 with fever who are treated with appropriate anti-malarial drugs
6.9 Incidence, prevalence and death rates associated with tuberculosis
6.10 Proportion of tuberculosis cases detected and cured under directly observed treatment short course

----

Goal 7: Ensure environmental sustainability

Target 7.A: Integrate the principles of sustainable development into country policies and programmes and reverse the loss of environmental resources
Target 7.B:Reduce biodiversity loss, achieving, by 2010, a significant reduction in the rate of loss
7.1 Proportion of land area covered by forest
7.2 CO2 emissions, total, per capita and per $1 GDP (PPP)
7.3 Consumption of ozone-depleting substances
7.4 Proportion of fish stocks within safe biological limits
7.5 Proportion of total water resources used7.6Proportion of terrestrial and marine areas protected
7.7 Proportion of species threatened with extinction
Target 7.C: Halve, by 2015, the proportion of people without sustainable access to safe drinking water and basic sanitation 7.8 Proportion of population using an improved drinking water source
7.9 Proportion of population using an improved sanitation facility
Target 7.D: By 2020, to have achieved a significant improvement in the lives of at least 100 million slum dwellers 7.10 Proportion of urban population living in slumsb
Goal 8: Develop a global partnership for development
Target 8.A: Develop further an open, rule-based, predictable, non-discriminatory trading and financial system

Includes a commitment to good governance, development and poverty reduction – both nationally and internationally

Target 8.B: Address the special needs of the least developed countries

Includes: tariff and quota free access for the least developed countries' exports; enhanced programme of debt relief for heavily indebted poor countries (HIPC) and cancellation of official bilateral debt; and more generous ODA for countries committed to poverty reduction


Target 8.C: Address the special needs of landlocked developing countries and small island developing States (through the Programme of Action for the Sustainable Development of Small Island Developing States and the outcome of the twenty-second special session of the General Assembly)



Target 8.D: Deal comprehensively with the debt problems of developing countries through national and international measures in order to make debt sustainable in the long term
Some of the indicators listed below are monitored separately for the least developed countries (LDCs), Africa, landlocked developing countries and small island developing States.
Official development assistance (ODA)
8.1 Net ODA, total and to the least developed countries, as percentage of OECD/DAC donors' gross national income
8.2 Proportion of total bilateral, sector-allocable ODA of OECD/DAC donors to basic social services (basic education, primary health care, nutrition, safe water and sanitation)
8.3 Proportion of bilateral official development assistance of OECD/DAC donors that is untied
8.4 ODA received in landlocked developing countries as a proportion of their gross national incomes
8.5 ODA received in small island developing States as a proportion of their gross national incomes
Market access
8.6 Proportion of total developed country imports (by value and excluding arms) from developing countries and least developed countries, admitted free of duty
8.7 Average tariffs imposed by developed countries on agricultural products and textiles and clothing from developing countries
8.8 Agricultural support estimate for OECD countries as a percentage of their gross domestic product
8.9 Proportion of ODA provided to help build trade capacity
Debt sustainability
8.10 Total number of countries that have reached their HIPC decision points and number that have reached their HIPC completion points (cumulative)
8.11 Debt relief committed under HIPC and MDRI Initiatives
8.12 Debt service as a percentage of exports of goods and services
Target 8.E: In cooperation with pharmaceutical companies, provide access to affordable essential drugs in developing countries 8.13 Proportion of population with access to affordable essential drugs on a sustainable basis
Target 8.F: In cooperation with the private sector, make available the benefits of new technologies, especially information and communications 8.14 Telephone lines per 100 population
8.15 Cellular subscribers per 100 population
8.16 Internet users per 100 population
The Millennium Development Goals and targets come from the Millennium Declaration, signed by 189 countries, including 147 heads of State and Government, in September 2000 (http://www.un.org/millennium/declaration/ares552e.htm) and from further agreement by member states at the 2005 World Summit (Resolution adopted by the General Assembly - A/RES/60/1, http://www.un.org/Docs/journal/asp/ws.asp?m=A/RES/60/1). The goals and targets are interrelated and should be seen as a whole. They represent a partnership between the developed countries and the developing countries "to create an environment – at the national and global levels alike – which is conducive to development and the elimination of poverty. All indicators should be disaggregated by sex and urban/rural as far as possible.
aFor monitoring country poverty trends, indicators based on national poverty lines should be used, where available.
bThe actual proportion of people living in slums is measured by a proxy, represented by the urban population living in households with at least one of the four characteristics: (a) lack of access to improved water supply; (b) lack of access to improved sanitation; (c) overcrowding (3 or more persons per room); and (d) dwellings made of non-durable material.

Table 1.1: The Millennium Development Goals


As can be seen in Table 1.1, measurement is central to the monitoring and evaluation component of the results-based management methodology. Targets define the desired outcome while indicators point to the information required to determine if a programme is making a difference. Indicators are often expressed as ratios (e.g., infant deaths per 1000 live births), percentages (e.g., proportion of the population that has access to clean drinking water), or sometimes as absolute numbers (e.g., the number of women holding ministerial posts). Indicators are sometimes targeted (e.g., achieve 100% primary school enrolment) and sometimes they are directional (e.g., reduce schistosomiasis infections by 50%). They need to be quantifiable, calculated at a meaningful frequency to detect change (often this is yearly), at the appropriate spatial resolution and to be cost effective. For example, measuring the weekly national attenuation of HIV/AIDS as a possible response to some intervention is unrealistic: it is incommensurate with the stimulus-response timeframe, too costly, and unnecessary. Conversely, measuring the amount of deforestation using satellite imagery on a monthly basis is entirely feasible, useful, and more appropriate to detect change stemming from, say, new or strengthened enforcement measures against tree cutting to support charcoal production.

Careful review of the GIS examples which follow and the table of MDG indicators suggests that geospatial data can and does figure centrally in the measurement of these indicators. For example, indicator 7.1 tracks the proportion of land area covered by forest and indicator 7.10 tracks the proportion of the urban population living in slums – both technically possible goals using remote sensing and GIS and achievable, especially if applied within a geospatial data infrastructure and so an SDI enables indicator measurements for development outcomes.

What is noteworthy from an M&E perspective in the Malawi example is the efficient use of simple geocoding and mapping to identify regions of the country where female under-enrolment persisted. Rather than incur expenditure on interventions in parts of the country that had already achieved parity, investment could be targeted where necessary. A regularly maintained and authoritative source of geocoded primary schools and their catchment areas offered freely to education planners—national and local—formed the kernel of a mini-SDI being leveraged to monitor progress towards a meaningful development outcome.

SDIs facilitate the efficient measurement of many MDG indicators, including several of the indicators and targets in goal 7 to ensure environmental sustainability. The direct application of geospatial data and analytics is readily evident. At first glance, SDI may appear less relevant to the M&E of the remaining indicators that monitor progress towards goals 1 through 6 and goal 8. While it is true that many UN member States are tracking several indicators through survey-based approaches (such as demographic and health surveys, or DHSs), those surveys are, in turn, based on standard census enumeration areas, administrative areas, or on special-purpose geographies such as health districts, economic development zones, or school catchment areas. The delineation of these geographies and their capture in a GIS are datasets central to any national spatial data infrastructure[19].

A more recent example is the Mapping Malaria Risk in Africa (MARA) project, sponsored in part by Natural Resources Canada in collaboration with the Medical Research Council of South Africa. This project illustrates how SDI can be leveraged in arresting the transmission and endemicity footprint of malaria. In the implementation of public health policy, it is often the case that spatially targeted interventions have the potential to produce better results. National-level interventions/activities can spread resources so thinly across a country that they lack efficacy. More concentrated interventions in areas with at-risk populations may generate a higher net benefit. The human settlement pattern of villages, town, and cities is a fundamental geospatial dataset in any SDI as are environmental and climate-related datasets such as altitude, humidity and precipitation, diurnal temperature range, and land cover that are prerequisites to mapping the range of the anopheles mosquito and, thus, the malaria risk footprint when overlaid on the human settlement pattern. The MARA project aims to develop just such an infrastructure for monitoring and modelling at-risk malaria populations using standardized earth observation/remote sensing techniques and Canada's experience in geospatial data management and dissemination. As interventions are fielded, including both vector control (such as the application of larvacide) and risk mitigation (such as bed nets), the SDI enables the consistent measurement of the programme's efficacy.

As the international donor community has increasingly embraced results-based management approaches to development aid, the monitoring and evaluation of development outcomes has come to the fore, most notably in the form of the Millennium Development Goals. SDIs are an enabler of results-based management, facilitating the design of interventions and the tracking of change and hopefully progress resulting from on-the-ground programmes. The pre-MDG example of boosting female enrolment at the primary school level in Malawi is an excellent example of how even a very small, unsophisticated, but focused SDI can assist in identifying target areas and then monitoring progress stemming from subsequent interventions. The MARA programme to attenuate malaria likewise draws and relies upon an SDI, albeit a much more robust one. Not only is the infrastructure of the geospatial data and the geographic information science more rigorously leveraged for targeting interventions but the SDI also figures centrally in the continuous monitoring and evaluation of the programme's efficacy (see Figure 1.1). As these examples illustrate, SDI enables and underpins both operational tools in the design and fielding of development projects and the more strategic monitoring and evaluation tools to measure social and economic progress (or "development aid ROI") stemming from those interventions.

When considering the link between SDI and the MDGs, Bruce McCormack (World Bank SDI Specialist) suggests that:

  • though MDGs are designed as national level targets/goals there is usually also the possibility to represent spatially targeted goals at a sub-national level;
  • establishing a sound national SDI (which entails spatial disaggregation at a sub-national level) can create the basis for:
  • finding and visualising required data in relation to the MDGs;
  • tracking the spatial distribution sub-nationally of progress towards MDG achievement over both time and space in an integrated way;
  • achieving improved understanding of the underlying sub-national driving forces which result in movement towards (or away from) attainment of the MDGs;
  • identifying at a sub-national level priority areas for intervention in order to assist more rapid achievement of the MDGs, and
  • aiding communication of MDG attainment/non-attainment through use of GIS visualisation capabilities
  • multi-national scale SDI can become the basis for monitoring MDG attainment at a global scale.
Figure 1.1: Map of Uganda Showing distribution of Stable Malaria Transmission

Methodology

The information contained in this report is based on both quantitative and qualitative data, collected, collated and analysed by the contributors to the handbook. It draws heavily on the best practice reports from Korea and Brazil and on technical assistance reports for Uganda and Jordan.

The Brazilian case study report incorporates the results of primary and secondary sources, outlined below, and on the contributors first hand experiences in developing early GIS projects, which later became part of the initial Brazilian SDI. Two early studies by the authors, (Davis and Fonseca, 2006 and Camara et al., 2006[20]), helped frame the Brazilian transformation process from a series of loosely coupled GIS projects into a well-established SDI.

Primary Sources

Case Study Survey Data

Structured questionnaires/surveys, with a range of expert SDI stakeholders, were conducted in both Brazil and Korea in order to ascertain SDI expert opinion and perspectives.

The Korean case study was conducted by post and email in July 2010. Participants were selected based on their knowledge, expertise, and interest in SDIs. Over 30 experts with ten plus years of GIS experience, covering both public and private sectors, participated. The survey considered three main areas of interest: evaluation of Korean SDI in general, best practices of Korean SDI and future directions for SDIs in developing countries[21].

The Brazilian case study consulted stakeholders in Brazilian GIS and SDI projects including those of the Brazilian national program, INDE (Infraestrutura de Dados Espaciais).

Conference Workshop and Online Survey data

In part preparation for this Report the World Bank held a 90-minute workshop titled 'Developing a How To' Guide on SDIs for Resource Scarce Countries'' at the Global Spatial Data Infrastructure (GSDI) Association's 2010 conference in Singapore[22]. An on-line survey accessible through the Conference web site was undertaken in order to provide input. The survey also contributed to the wider World Bank project, Using Geographic Information Systems and Spatial Data Infrastructure for Monitoring Development Outcomes[23].

An aim of the survey and workshop was to obtain examples of SDI best practice, identify who should be the audience for this handbook, get input regarding the content of the guide whilst also acquiring background information about the respondents. The workshop was led by Dr. Tim Kelly, Lead ICT Policy Specialist with the World Bank and Bruce McCormack, the Bank's Senior SDI Advisor.

Secondary Sources

Relevant literature, studies and current website content was also consulted and analysed in the writing of this handbook. Additionally, recent legislation that created the Korean and Brazilian national SDIs was analysed.

Aims and Structure of the Manual

Aims

The aims of the Manual are:

  • to build a Manual for non-technical stakeholders, building on existing literature, especially the 'SDI Cookbook' (Nebert, 2004[24]).
  • to provide a guide for policy makers and higher level administrators which identifies relevant issues and provides guidance regarding the establishment of an SDI.
  • to provide a tool to World Bank operatives and managers in countries working to improve their national capacity for sharing geographical data on a common data infrastructure.
  • to present, in an informal manner, responses to a range of questions which the policy makers and administrators might be expected to ask.
  • To provide the most up-to-date information and guidance on developing SDIs for developing countries where there are currently no spatial data and provide material of use where SDI development has commenced.
  • In general, the Manual aims to deal specifically with providing the know-how for the development and implementation of SDIs in developing countries as a means to monitoring development outcomes, specifically the Millennium Development Goals.

Manual structure

The manual is divided into two parts; 'Part 1: The Theory and Practice of SDIs' and 'Part 2: How To Develop an SDI'. These divisions can be losely characterised as 'theory' and 'practice'.

Part 1 [The Theory and Practice of SDI] is divided into three chapters:

  • Chapter 1 (Introduction) -this page- provides the background to the wider World Bank Project, describes the research methods utilised and outlines the structure of the report. This chapter also places the report in the wider context of global development and discusses SDIs in the context of the Millennium Development Goals.
  • Chapter 2 (GIS and Spatial Data Infrastructures) deals more specifically with the theory of SDIs and outlines the basic concepts and issues involved in creating national SDIs.
  • Chapter 3 (Worldwide SDI Development and Outreach) continues to provide context to SDIs by examining the wider global development of SDIs from both a historical and contextual perspective.

Part 2 [How to Develop and SDI] covers the practicalities of developing SDIs. Based on the results of the four case study reports for Korea, Brazil, Uganda and Jordan it functions as a 'How To' guide for non-technical practitioners and stakeholders of SDIs. Part 2 is divided into 3 chapters:

  • Chapter 4 (SDI Implementation) provides the substantive material of the report and outlines the issues and practices invloved in developing an SDI for monitoring development outcomes.
  • Chapter 5 (Learning from Experience) presents the lessons learned from the case studies and, finally,
  • Chapter 6 (Emerging SDI Environment) considers emerging and future trends in GI and GIS and how these might influence the future direction of SDI development and the potential issues for SDI practitioners and stakeholders.

The further sources of information provides more resources and useful links related to this report.

Footnotes

  1. http://www.whitehouse.gov/omb/circulars/a016/print/a016_rev.html#background
  2. Jackson, M. J. (2011). Evolving Institutional SDI’s to interoperate with crowd-sourced and informal data sources. International Conference on Data Flow From Space to Earth: Applications and Interoperability, Venice, Italy, 21-23March, 2011. Video presentation and slides available at http://www.space.corila.it/Program.htm
  3. http://www.geoconnections.org/en/index.html
  4. http://www.fgdc.gov/
  5. http://www.anzlic.org.au/
  6. Rajabifard, A., Binns, A. and Williamson, I.P., 2006, ‘Virtual Australia - An enabling platform to improve opportunities in the spatial information industry ’, Journal of Spatial Science, Special Edition, 51 (1)
  7. Unwin, T., Ed. (2009). ICT4D: Information and Communication Technology for Development, . Cambridge, Cambridge University Press.
  8. See World Bank SDI Report - GIS and Spatial Data Infrastructures#Basic Concepts for full definition of terms.
  9. World Bank, 2009, World Development Report 2009: Reshaping Economic Geography, Washington: World Bank, available at: http://go.worldbank.org/K2CBHVB7H0
  10. CAPRA is an information platform to enhance decision-making in risk management across various sectors, such as emergency management, territorial planning, public investment and the financial sectors. Through the application of probabilistic risk assessment principles to threats like hurricanes, earthquakes, volcanic activity, floods, tsunamis and landslides, CAPRA allows to measure and compare different types of risks, and to develop sector-specific applications for risk management. (http://web.worldbank.org/WBSITE/EXTERNAL/COUNTRIES/LACEXT/EXTLACREGTOPURBDEV/0,,contentMDK:22277760~pagePK:34004173~piPK:34003707~theSitePK:841043,00.html). Accessed 25th March 2011
  11. http://www.app.collinsindicate.com/mdg/en/
  12. http://devdata.worldbank.org/DataVisualizer/
  13. Huxhold, W.E., 1991, An Introduction to Urban Geographic Information Systems, Oxford University Press
  14. http://www.infodev.org/en/Page.ktf.html
  15. http://www.acdi-cida.gc.ca/acdi-cida/ACDI-CIDA.nsf/eng/NAT-92213444-N2H; Benh, Robert D. (2003) “Why Measure Performance? Different Purposes Require Different Measures,” Public Administration Review, 65(5): 586-606; Mayne, John (2002) “Reporting on Outcomes: Setting Performance Expectations and Telling Performance Stories,” Canadian Journal of Program Evaluation, 19(1): 31-60; and Patton, Michael (1997) Utilization-Focused Evaluation. SAGE: Thousand Oaks, CA.
  16. Mayne, John (2007) Best Practices in Results-Based Management: A Review of Experience - A Report for the United Nations Secretariat. New York, NY; Paris Declaration on Aid Effectiveness - Ownership, Harmonisation, Alignment, Results and Mutual Accountability.Paris, 2005.
  17. http://www.un.org/millennium/declaration/ares552e.htm
  18. See: http://unstats.un.org/unsd/mdg/Host.aspx?Content=Indicators/OfficialList.htm
  19. UN Economic Commission for Africa (2007) Determination of Fundamental Datasets for Africa. Addis Ababa, Ethiopia; FAO (2006) A Geospatial Framework for the Analysis of Poverty and Environment Links, Rome, Italy; and FAO (2005) An Inventory and Comparison of Globally Consistent Geospatial Databases and Libraries, Rome, Italy.
  20. Davis, C.A. and Fonseca, F., 2006, ‘Considerations from the development of a local spatial development infrastructure’, Information Technology for Development 12(4), 273-290; Camera, G., Fonseca, F., Monteiro, A.M. and Onsrud, O., 2006, ‘Networks of innovation and the establishment of a spatial data infrastructure in Brazil’, Information Technology for Development 12(4), 255-272
  21. See Annex A for full Korean methodology
  22. See http://www.gsdi.org/gsdiconf/gsdi12/
  23. See http://www.infodev.org/en/Article.479.html
  24. Nebert, D. D., Ed. (2004). Developing Spatial Data Infrastructures: The SDI Cookbook, GSDI-Technical Working Group. Available at http://www.gsdi.org/docs2004/Cookbook/cookbookV2.0.pdf
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