Assessing Spatial Data Infrastructures Using Management Model - Literature review Example

Title: Assessing Spatial Data Infrastructures using Management Model Name: Course: Date: Executive summary Evaluation and comparison of various spatial data infrastructure performance keeps on evolving. Spatial data infrastructure systems keep on changing as authorities introduce constant reforms and the society’s perception of land value. Therefore, internationally there is no specific standard of evaluating and comparing spatial data infrastructure. Spatial data infrastructure helps to integrate geospatial information on land resources. Spatial data infrastructure uses technology to provide infrastructure for sharing spatial data. This helps to reduce the duplication of geospatial data collection by both users and producers. Spatial data infrastructures differ from one country to the other. Spatial data infrastructure differs depending on a country’s cultural, economic and political circumstances. Therefore an assessment framework is required in comparing and evaluating different spatial data in various countries (Mundy, 2010, p.23). This paper will use Management Model evaluation framework to evaluate and compare land administration systems between Australia and Switzerland. Management Model evaluation framework explains that land is a natural resource which requires sustainable use and development in order to benefit the general population. Land administrators and land managers are responsible for sustainable land development and sustainable use. Management Model evaluation framework can be seen as a management model that links the managerial, operational, policy and external aspects of land use and management. Management model evaluation framework is based on management of organization theories (Dessers, 2013, p.42). This paper will discuss the importance of using Management Model evaluation framework for the comparisons as well as the system attributes. Background Onsrud (2007, p.56) explains that the comparison and evaluation of spatial data infrastructure (SDI) can help to understand better issues in land management. Comparing and evaluating private and public administration systems is helpful in improving institutional structures and improving processes. Land administration systems have evolved over time creating complexity in understanding land use and development. Land administration systems use spatial data infrastructure to understand land use and development. Spatial data infrastructure involves development of a framework that describe institutional arrangements, policies and technologies that facilitate the creation, use and exchange of geospatial data and related resource information across a community in a particular region. Spatial data infrastructure is a concept that provides an environment that enables nations and organizations to interact with technologies that foster use, management and production of geographical information and data. Such a framework is slowly implemented to ensure that sharing of geospatial information is well implemented at the regional level as well the global level (De Smith et al, 2007, p.11). Spatial Data Infrastructures use geographical information systems for exchanging information between users and producers. Spatial Data Infrastructures frameworks ensure that geospatial standards and data are created in such a way that they develop policies and authoritative datasets that support it. The effectiveness of a spatial data infrastructure can be evaluated based on management model. The environment in which organizations operate consists of specific standards, policies and technologies that regulate the use, access and dissemination of spatial data (Masser, 2010, p.3). The standards are created by use of an evaluation and assessment framework such as Management model evaluation framework. Factors that influence the development of spatial data infrastructure include organizational factors, internal and external factors within a community or country. Internal factors include technological factors, economic, human factors and partnership aspects. External factors include cultural differences, political and development issues. Organization factors include policies, conceptual aspects and security and protection issues (Sadahiro, 2008, p.5) Figure 1.0 below shows the factors influencing regional spatial data infrastructure development. Political Development issues Cultural Human factors Technology PARTICIPATION Partnerships Economic Security and protection Policies Conceptual Figure 1.0. Showing factors influencing spatial data development in a particular region. Evaluation and Framework for Evaluation criteria Evaluation is mostly concerned with questions such as are we doing things right, are we doing the right thing in the first place, what can we learn from similar situations, and what lessons can be derived from past experiences. Such questions form an integral part in management tasks and help to steer the management in the right direction of evaluating a system or an organization. The main objectives of an evaluation is to verify the efficiency of a project, project objectives, impacts, and to prepare a satisfactory report for future reference. An evaluation needs to be broken down into phase for easier handling. According to World Bank Staff (2014), there are four elements of evaluating the performance of a system: 1) Clearly defined objectives (in order to know where to go) The targets for the whole system need to be defined. The targets can be based on economic basis, legal basis, political basis, cultural heritage basis as well as historical and social aspect basis. 2) Clearly defined strategies (in order to know how to get there) Strategies define the way forward so at to satisfy and attain the set objectives. 3) Development of outcomes and indicators for monitoring (in order to know if you are on track) A good evaluation framework defines the results arising from strategies and objectives. Indicators should be well defined and monitored for relevant feedback on strategies and objectives. 4) Assessment of performance (in order to gain input for further improvement) Assessment of performance is the process of taking into account the indicators and outcomes so as to ascertain the reliability and performance of a systems. Variations from the set down objectives and strategies is assed in order to analyze whether the initial strategies and objectives have been satisfied. The four elements of evaluation must be seen as a cyclical process that allows regular assessment of system performance and review on set down objectives and strategies. The overall target strategies can be assed annually while the set down objectives can be assessed every four years (Albert & Dobbs, 2013, p.61). Figure 2.0 below shows the cyclical nature of evaluation elements. Figure 2.0 showing the cyclical nature of evaluation elements In assessing the spatial data infrastructure the evaluation framework must integrate the relevant stakeholders in land administration. The stakeholders operate in an organization that is viewed to have three organizational levels that is policy level, management level and operational levels. The policy level is involved with setting objectives. Relevant stakeholders in the policy level include the government or the executive board. The management level involves the definition of strategies and relevant stakeholders include administrative institutions or organizations (Anttiroiko, 2008, p.12). The operational level deals with operations that define outcomes and indicators. The main stakeholders on this level are various operational units within an administrative unit (Figure 3.0). Figure 3.0 below shows the relationship between organizational levels and evaluation elements. The organizational levels give the basis for defining actual areas or fields of evaluation (Figure 4.0). The performance area acts as a representation of the whole systems and defines how strategies and objectives to be satisfied. Such factors as human resources, technology and capacity building need to be analyzed on how they will affect all the organizational levels (Sadahiro, 2008, p.10). Figure 4.0 illustrates areas for evaluating an administration system Management Model Evaluation Framework Attributes. The main criteria for the Management model land administration systems evaluation framework is consideration of national geographic information strategy elements, institutional context, main data providers and historic and geographical context. Attributes of Management Model Evaluation Framework can be analyzed using the SWOT (Strength, Weaknesses, Opportunities and Threats) analysis (Sadahiro, 2008, p.14). Strengths Comprehensive land information review on regular basis. The presence of Government department which has good decision making and financial capabilities Strong Academic sector and good cooperation between the private and public sector. Information is readily about spatial data is readily available through access network on various websites in the world. Weaknesses Strategies formulated may not fully consider land ownership as it only considers public and private land sectors. Freedom of data methodology may lead to lack of proper data interoperability and modelling. The framework does not have an independent board which can coordinate and promote spatial information. Opportunities Strengthens the political support from the general population as policy makers get involved in developing land and creating sustainable land use for the community. Good land governance can be enhanced because the framework provides an accountable and responsible method of developing sustainable land use. Threats It can lead to loss of political support if the policies made negatively affect the community The framework does not bring diverging interest groups together such as private land sector owners and the government depending on the approach used Comparison of Spatial Data Infrastructures in Australia and Switzerland Switzerland and Australia are both well-developed countries which are governed by a federal government structure. However, their organizational and cultural frameworks differ from each other. Australian Spatial Data Infrastructure Australia is the 6th largest country in the world but one of the least populated country in the world and one of the most urbanized country. About eighty five percent of the Australian population reside in the urban areas along the southeastern and eastern coast lines. Australia consist of two territories and six states. Australian SDI is coordinated by the Australian and New Zealand Land Information Council (ANZLIC) with the aim of availing spatial data to all. The main members in the ANZLIC include members who represent the Australian government, New Zealand government, territorial and State governments. The main stakeholders of the Australian spatial Data Infrastructure (ASDI) include ANZLIC, Intergovernmental Committee for Surveying and Mapping (ICSM), Spatial Information Council, and Public Sector Mapping Agencies Australia (Masser, 2010, p.16). Switzerland Spatial Data Information Switzerland is relatively small country. The Swiss government has mandated the GIS Coordination Group (COGIS) to manage the national SDI. The COGIS uses an e-geographical initiative to enable cooperation of information research between the private industry and the public agencies (Masser, 2010, p.16). Both Switzerland and Australia have well established standards, rules and regulation regarding cadastral data capture and almost all spatial data is digitized. However, in Switzerland data capturing in lower federal systems are not certified therefore are not well standardized and documented. In Australia, the ANZLIC is well represented at the federal level and therefore, even lower levels in the federal governments are well standardized and documented (Masser, 2010, p.17). The Australian government has a spatial data directory and environmental data directory which consists of spatial data information. In Switzerland, the spatial data information is embedded in a catalogue known as Geocat.ch which cannot be able to support a large capacity of information such as photogrammetry. However, information is very accurate as it comprises of a small geographical area. Table 1.0 comparison of Switzerland and Australian national SDIs Indicator Switzerland Australia Data and Metadata Data Management Availability Standardized update cycles Consistency Provision of Large datasets Good Good Fair Not so good Good Good Fair Not good Data Capture Process Well documented Verifiability Accessibility standardized Fair Fair Fair Fair Fair Fair Fair Fair Data Quality and accuracy Very good Good Data Sharing and Participation Very Good Good Coordinating arrangements Definition documentation Fair fair Good Good Interoperability standards Good Fair Conceptual model and data format Model Format Good Good Fair Fair Conclusion The importance of evaluating and comparing spatial data infrastructure is identification of good practices that promote data accuracy, availability and accessibility. Performance indicators measure key variables that give a basis for an evaluation framework. This paper argues that that management model gives the indication that proper management of various land administrators can give a good way of evaluating land administration systems. Coordination between the government and the private sector can increase the efficiency of evaluating land administration systems through creating a cooperative spatial data infrastructure. The private industry is a key player in land administration system (Mundy, 2010, p.23). The private sector needs to be consulted when collection data for spatial documentation. Switzerland although it has a smaller geographical region its consultation with the private industry provides a quality and accurate data sets. Australia involves a lot of government agencies in developing data sets. The management model accommodates the people as major influencers in developing spatial data infrastructure. The management model evaluation framework assesses the efficiency and effectiveness a good spatial data infrastructure that requires good management practices (Onsrud, 2007, p.56) Referencing List Albert, D. P. & Dobbs, G. R. 2013, Emerging methods and multidisciplinary applications in geospatial research. Hershey, PA, Information Science Reference. Anttiroiko, A.-V 2008, Electronic government: concepts, methodologies, tools and applications. Hershey PA, Information Science Reference. De Smith, M. J., Goodchild, M. F., & Longley, P. A. 2007, Geospatial analysis: a comprehensive guide to principles, techniques and software tools. Leicester, Matodor. Dessers, E 2013, Spatial data infrastructures at work: analysing the spatial enablement of public sector processes, Leuven, Leuven University Press. Masser, I 2010, Building European spatial data infrastructures. Redlands, Calif, ESRI Press. Mundy, P 2010, Count me in: surveying for tenure security and urban land management. Nairobi, UN HABITAT. Onsrud H. J, 2007, Research and theory in advancing spatial data infrastructure concepts. Redlands, Calif, ESRI Press. Sadahiro, Y 2008, Spatial data infrastructure for urban regeneration. Tokyo, Springer. World Bank Staff, 2014, Doing Business 2015 going beyond efficiency: comparing business regulations for domestic firms in 189 economies: a World Bank Group flagship report. Washington, World Bank Publications. http://dx.doi.org/10.1596/978-1-4648-0351-2. Read More