National Spatial Data Clearinghouses: Worldwide development and impact
Bibliographic record
Abstract
The concept of a clearinghouse originates from the financial world.With respect to the financial transactions between banks, the clearinghouse keeps the data on mutual indebted amounts.At the end of each day, banks are informed about the final amounts to be transferred between banks.Every day there is a 'clearing' between them (Bogearts, 1997).The first clearinghouse was the London Banker's Clearinghouse, which was established in 1773.The New York Clearinghouse Association described its clearinghouse role in 1853 as to simplify the chaotic exchange between New York City banks (The Clearinghouse Payments Company, 2005).Even today, this clearinghouse regards itself as the place where payments meet, mix and move expeditiously to their final destination.In 1994, the US Federal Geographic Data Committee (FGDC) established the National Geospatial Data Clearinghouse.This aimed to facilitate efficient access to the overwhelming quantity of spatial data (from federal agencies) and coordinate its exchange, with the objective of minimizing duplication (in the collection of expensive spatial data) and assisting partnerships where common needs exist (Rhind, 1999; FGDC, 2000; Crompvoets et al., 2004).A national clearinghouse for spatial data can be considered as the access network of a national SDI, which focuses on the facilitation of spatial data discovery, access and related services.It is not a national repository where datasets are simply stored.It can be seen as a one-stop shop for all national spatial data, sourced from governmental agencies and/or industrial bodies (Crompvoets et al., 2004).National clearinghouse implementation can vary enormously.The way in which a national clearinghouse is set up depends on technological, legal, economic, institutional, and cultural factors within the territory.These factors determine to what extent the clearinghouse retains control over data.In addition to the national clearinghouse, clearinghouses at a local, state, international, and even global level exist.However, the national clearinghouse differs due to the fact that it is embedded in the National SDI.In April 2005, 83 national clearinghouses were established on the Internet.A few examples of current national clearinghouses are: MIDAS (MetaInformacni Databazovy System), Czech Republic; geodata-info.dk,Denmark; India NSDI Portal, India; Spatial Data Catalogue, Malawi; Russian GIS Resources, Russia; Geocat.ch,Switzerland; and the Clearinghouse Nacional de Datos Geograficos del Uruguay, Uruguay.Those listed, share the same objective, that of discovering and accessing spatial data, through the available metadata.National clearinghouses are evolving worldwide.These developments have contributed to the realisation of national SDIs.A body of literature has been compiled on national experiences (e.g.Spatial Applications Division, Catholic University of Leuven 2003, conference papers of Global Spatial Data Infrastructure Association 2002-2005).So far, the majority of this literature focuses on the technical aspects of clearinghouses, and does not take into account the evolutionary nature of these electronic facilities.It is important to have a longitudinal perspective when establishing and maintaining clearinghouses.A detailed study of developments of all national clearinghouses worldwide could be an appropriate starting point.This could identify the critical factors behind the success or failure of a clearinghouse.In this way, knowledge could be used for the support of future implementation strategies.Factors for consideration could be societal, for instance legal, economic, technological, historical, cultural, demographic, environmental and institutional characteristics of a country, or clearinghouse-internal, such as the network architecture, availability of view services, type of search mechanisms and funding stability.However it is worth noting that, simply consolidating the best practices of a few well-operating national spatial data clearinghouses (Australia, Canada and USA), gives no guarantee of sustainability for other national clearinghouses.Such best practices cannot necessarily be applied equally in other countries OUTLINE OF THE THESISThe core of this thesis (chapters 2-6) is based on a series of five papers that have been published in, or submitted to internationally reviewed journals.The content is the work of the author of this thesis Joep Crompvoets.In a few cases, the analyses have been assisted by the co-authors given the extensive nature of the study.The chapters themselves cover the worldwide development, impact assessment of national spatial data clearinghouses, and explorations of societal impact on national clearinghouses.Each chapter focuses on a sub-objective as earlier discussed.Figure 1.1 outlines the relationship between the chapters.Chapter 2 assesses systematically and presents the worldwide status of national spatial data clearinghouses in December 2001.The examination of the clearinghouse status and the spatial distribution is based on a web survey, which comprises an inventory of all established clearinghouses and measurements of several key characteristics.The main differences in clearinghouse implementation are presented and briefly discussed using the clearinghouse characteristics.Chapter 3 assesses and presents the development of all national clearinghouses throughout the world, with reference to the concepts, definitions and history of both SDI and clearinghouses.The development assessment is mainly based on a longitudinal web survey, undertaken in April 2000, 2001, 2002 and December 2000, 2001, 2002.The main results are presented using the main SDI-components.Additionally, these developments are discussed and critical (clearinghouse-internal) factors are identified.Chapter 4 assesses and presents the impact of clearinghouses on society, and in particular the GI-community, with reference to the economic, social and environmental dimensions of sustainable development.The comprehensive and systematic impact assessment is based on a survey, undertaken among coordinators of (almost) all known clearinghouses of the world, using indicators to assess the relevance, efficiency and effectiveness of clearinghouses (November 2003 -April 2004).Complementary analyses are performed as a means of understanding the significance of these impacts.Additionally, the main impact results are discussed.Chapter 5 explores and presents the societal impact on the establishment of national clearinghouses with reference to the economic, educational, technological, environmental, cultural, demographic, institutional, health care and legal characteristics of a country in 2002.This societal impact assessment is based on ANOVA and data mining techniques.The main result is the identification of critical (societal) factors that could impact on the establishment of national clearinghouses.Additionally, the significance of these critical factors for establishment strategies is discussed.Chapter 6 explores and presents the societal impact on the success of national clearinghouses for the situation of 2002.This societal impact study is primarily based on a clearinghouse suitability index and statistics (e.g.partial least squares regression).The main result is the identification of critical (societal) factors for success.In addition, the significance of these critical factors for implementation strategies is discussed.Finally, Chapter 7 concludes the thesis with the main conclusions and recommendations for further research.Kindly note that the figures presented between chapters 2 and 3 are slightly inconsistent.The main reason is that a couple of clearinghouses, not yet discovered in December 2001, were included at a later stage of the study.Moreover, Timor-Leste became an independent nation in 2002. Chapter 2Finally, in the future, it is highly probable that many national clearinghouses will give access to spatial data itself and provide complementary services such as online mapping.However, a concern could be the low frequency of web updates of several clearinghouses due to poor management.Therefore, special attention has to be given to keep clearinghouse managers motivated for having a well-managed clearinghouse.Based on the twelve characteristics used, we can conclude that Australia, Canada, Portugal and USA have the best existing national clearinghouses.Additionally, this web survey shows that not only the richest countries have good clearinghouses.Examples of relatively poorer countries with suitable national clearinghouse are El Salvador, Nicaragua and Uruguay.Based on the above research, for all countries, it seems that one of the keys for successful clearinghouse implementation is high political support and interest by means of funding and long-term strategy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.019 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.019 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.010 | 0.015 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.028 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".