An Experimental Study of Cryptocurrency Market Dynamics

As cryptocurrencies gain popularity and credibility, marketplaces for cryptocurrencies are growing in importance. Understanding the dynamics of these markets can help to assess how viable the cryptocurrnency ecosystem is and how design choices affect market behavior. One existential threat to cryptocurrencies is dramatic fluctuations in traders' willingness to buy or sell. Using a novel experimental methodology, we conducted an online experiment to study how susceptible traders in these markets are to peer influence from trading behavior. We created bots that executed over one hundred thousand trades costing less than a penny each in 217 cryptocurrencies over the course of six months. We find that individual "buy" actions led to short-term increases in subsequent buy-side activity hundreds of times the size of our interventions. From a design perspective, we note that the design choices of the exchange we study may have promoted this and other peer influence effects, which highlights the potential social and economic impact of HCI in the design of digital institutions.Comment: CHI 201

Spatial isomorphism

This research continues with current innovative geocomputational research trends that aim to provide enhanced spatial analysis tools. The coupling of case-based reasoning (CBR) with GIS provides the focus of this paper. This coupling allows the retrieval, reuse, revision and retention of previous similar spatial cases. CBR is therefore used to develop more complex spatial data modelling methods (by using the CBR modules for improved spatial data manipulation) and provide enhanced exploratory geographical analysis tools (to find and assess certain patterns and relationships that may exist in spatial databases). This paper details the manner in which spatial similarity is assessed, for the purpose of re-using previous spatial cases. The authors consider similarity assessment a useful concept for retrieving and analysing spatial information as it may help researchers describe and explore a certain phenomena, its immediate environment and its relationships to other phenomena. This paper will address the following questions: What makes phenomena similar? What is the definition of similarity? What principles govern similarity? and How can similarity be measured? Generally, phenomena are similar when they share common attributes and circumstances. The degree of similarity depends on the type and number of commonalties they share. Within this research, similarity is examined from a spatial perspective. Spatial similarity is broadly defined by the authors as the spatial matching and ranking according to a specific context and scale. More specifically, similarity is governed by context (function, use, reason, goal, users frame-of mind), scale (coarse or fine level), repository (the application, local domain, site and data specifics), techniques (the available technology for searching, retrieving and recognising data) and measure and ranking systems. The degree of match is the score between a source and a target. In spatial matching a source and a target could be a pixel, region or coverage. The principles that govern spatial similarity are not just the attributes but also the relationships between two phenomena. This is one reason why CBR coupled with a GIS is fortuitous. A GIS is used symbiotically to extract spatial variables that can be used by CBR to determine similar spatial relations between phenomena. These spatial relations are used to assess the similarity between two phenomena (for example proximity and neighborhood analysis). Developing the concept of spatial similarity could assist with analysing spatial databases by developing techniques to match similar areas. This would help maximise the information that could be extracted from spatial databases. From an exploratory perspective, spatial similarity serves as an organising principle by which spatial phenomena are classified, relationships identified and generalisations made from previous bona fide experiences or knowledge. This paper will investigate the spatial similarity concept.UnpublishedAamodt, A. & E. Plaza, 1994 Case-based Reasoning: Foundational Issues, Methodological Variations and System Approaches. Artificial Intelligence Communications, Vol.7, No.1. Agouris, P., Stefanidis, A., & M. J. Egenhofer, 1997 I. Q. Image Query by Sketch http://www.spatial.maine.edu/~peggy/IQ.html. Black, W. Hutchinson, G. & T. K. Siang 1997 System Design and Implementation of a Spatial Similarity System: BLASH. Information Science Dept, INFO408 Report, University of Otago, Dunedin, New Zealand, 54 pages. Bruns, T. & M. Egenhofer 1996 Similarity of Spatial Scenes, in: M.-J. Kraak & M. Molenaar (eds.), Seventh International Symposium on Spatial Data Handling, Delft, The Netherlands Taylor & Francis, pp. 173-184. Buttenfield, B. P., & R. B. McMaster (editors), 1991. Map Generalization: Making Rules for Knowledge Representation. New York: Longmont Scientific and Technical. Cain, D. H., K. Ritters, & K. Orvis. 1997. A Multi-Scale Analysis Of Landscape Statistics. Landscape Ecology, 12(4), p. 199-212. Cao, C., & N. S.-N. Lam. 1997. Understanding the scale and resolution effects in remote sensing and GIS. In Scale in remote sensing and GIS, D. A. Quattrochi & M. F. Goodchild, eds., Lewis Publishers, p. 57-72. Carbonell, J. G. 1986 Derivational analogy. a theory of reconstructive problem solving and expertise acquisition. In R.S. Michalski, J.G. Carbonnel, and T.M. Mitchell, eds. Machine Learning : An Artificial Intelligence Approach, Vol. 2, pages 371--392. Morgan Kaufman Publishers, Los Altos, California. Cobb, M. A., Chung, M. J., Foley III, H., Petry, F. E., Shaw, K. B. & H. V. Miller, 1998, A Rule-based Approach for the Conflation of Attributed vector Data. GeoInformatica: An International Journal on Advances of Computer Science for Geographical Information Systems, Vol. 2, Number 1, 7-37. Cullinan, V. I., & J. M. Thomas. 1992. A comparison of quantitative methods for examining landscape pattern and scale. Landscape Ecology, 7(3), p. 211-227. Dubitzky W. Carville F. & J. Hughes 1993 Case-level Knowledge Modelling in CBR, Irish Journal of Psychology, 14:3 :478-479. Ehleringer, J. R., & C. B. Field (editors), 1993. Scaling Physiological Processes, Leaf to Globe. New York: Academic Press, Inc. Ellison T. M. 1997, Induction and Inherent Similarity. SimCat 97 An Interdisciplinary Workshop on Similarity And Categorisation, November, Department of Artificial Intelligence, University of Edinburgh, pp. 83-90. Elmasri, R. & Navathe, S. B. 1994, Fundamentals of Database Systems. The Benjamin/Cummings Publishing Company, Redwood City, C.A. Fayyad, U. M. 1997 Editorial. Data Mining and Knowledge Discovery, 1(1): 5-10. Flewelling, D. M. 1997, Comparing Subsets from Digital Spatial Archives: Point Set Similarity. Ph.D., University of Maine, Orono, Maine. Flickner, M., Sawhney, H., Niblack, W., Ashley, J., Huang, Q., Dom, B., Gorkani, M., Hafner, J., Lee, D., Petkovic, D., Steele, D. & P. Yanker, 1995 Query by Image and Video Content: The QBIC System. IEEE Computer 28(9): 23-32. Gentner, D. & Forbus, K. D. 1991 MAC/FAC: A model of similarity-based retrieval. In Proceedings of the 13th Annual Conference of the Cognitive Science Society, p 504-509, Chicago. Gentner, D. (1983). Structure-mapping: A theoretical framework for analogy. Cognitive Science, 7(2):155-170. Goodchild, M. F, Egenhofer, M. J. & R. Fegeas., 1998, Interoperating GISs. Report of a Specialist Meeting held under the auspices of the Varenius Project. http://www.ncgia.ucsb.edu/conf/interop97/interop_toc.html Grimnes M. and Aamodt A., 1996, A two layer case-based reasoning architecture for medical image understanding. In Advances in Case-Based Reasoning, Third European Workshop, EWCBR-96 Smith and Faltings (eds.), pp. 164-178. Grimnes M. 1996 Personal communication. Gudivada, V. 1995 On Spatial Similarity Measures for Multimedia Applications, in: SPIE, pp. 363-372. Gudivada, V. and V. Raghavan 1995 Design and Evaluation of Algorithms for Image Retrieval by Spatial Similarity, ACM Transactions on Information Systems, 13 (2): 115-144. Hampton J. A. 1997, Similarity and Categorization. SimCat 97 An Interdisciplinary Workshop on Similarity And Categorisation, November, Department of Artificial Intelligence, University of Edinburgh, pp. 103-110. Higham, E. C. Holt, A. & G.W. Kearsley, 1996 Tourist Flow Reasoning: The Spatial Similarities of Tourist Movements. In the Proceedings of the 8th Annual Colloquium of the Spatial Information Research Centre, Otago University, Dunedin, New Zealand, pp 69-78. Holt, A. 1996a Allowing the Environment to Model Itself. Environmental Perspectives, A Quarterly Newsletter published by the Environmental Policy & Management Research Centre, Issue 10: 6-7. Holt, A. 1996b Incorporating A New Computational Reasoning Approach to Spatial Modelling. In the proceedings of The 1st International Conference on GeoComputation, University of Leeds, Leeds, England. 1: 427-442. Holt, A. 1997 GeoComputation-neologism, or gambit towards progressive research? Environmental Perspectives, A Quarterly Newsletter published by the Environmental Policy & Management Research Centre, Issue 15:5-6. Holt, A. & G. L. Benwell, (1996), Case-based Reasoning and Spatial Analysis. Journal of the Urban and Regional Information Systems Association, 8(1) :27-36. Holt, A. & G. L. Benwell 1997 Using Spatial Similarity for Exploratory Spatial Data Analysis: Some Directions. The 2nd International Conference on GeoComputation, University of Otago, Dunedin, New Zealand, pp. 279-288. Holt, A. & G. L. Benwell (In Press) Applying Case-based Reasoning to Spatial Phenomena, The International Journal of Geographical Information Science. 30 pages. (accepted for publication). Holt, A., Higham, E. C. & G.W. Kearsley, 1996 Elucidating International Tourist Movements: An Intelligent Approach. In the proceedings of Tourism Down Under II: A Tourism Research Conference, Centre for Tourism, University of Otago, Dunedin, pp. 167-180. Holt, A., Higham, E. C. & G.W. Kearsley, 1997 Predicting International Tourist Flows: Using a Spatial Reasoning System. The Pacific Tourism Review: An Interdisciplinary Journal. 1:4 30 pages. Holt, A. & R. E. MacLaury, (In Press) Spatial Vantages: Understanding Spatial Similarities. Language Sciences: A Special Issue on Vantage Theory, ed. N. Love. Hudson, J., 1992. Scale in space and time. In R. F. Abler, M. G. Markus, & J. M. Olson (Editors), Geography's Inner Worlds: Pervasive Themes in Contemporary American Geography. New Brunswick, NJ: Rutgers University Press, pp. 280-300. Indurkhya, B., 1992 Metaphor and cognition: An interactionist approach. Kluwer Academic Publishers, Dordrecht. Indurkhya, B., 1991 On the role of interpretive analogy in learning. NGC, 8(4):385-402. Jagadish, H. V. 1991 A retrieval technique for similar shapes. In the proceedings of the 10th ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems, pages 208-217, Denver, Colorado. Jagadish, H. V., Mendelzon, A. O. & T. Milo. 1995 Similarity_based queries. PODS. Jain, A. K. & R. Hoffmann, 1988 Evidence-based recognition of 3D Objects. IEEE Transactions On Pattern Analysis And Machine Learning, Vol. 10, No. 6. pp. 783-801. Jeffery, N. Teather, D. & Teather, B. A., 1997, Case-Based Training Using Similarity and Categorization from a Multiple Correspondence Analysis. SimCat 97 An Interdisciplinary Workshop on Similarity And Categorisation, November, Department of Artificial Intelligence, University of Edinburgh, pp. 123-130. Jin, J. S., Greenfield H., & Kurniawati R., 1997 CBIR-VU: a new scheme for processing visual data in multimedia systems. Lecture Notes in Computer Science: Visual Information Systems, Leung C. H. C., Springer Verlag, pp40-65. Jones, E. K. & A. Roydhouse, 1994 Spatial Representations of Meteorological Data for Intelligent Retrieval. The Sixth Annual Colloquium of the Spatial Research Centre, Proceedings. Eds. G.L. Benwell and N.C. Sutherland. Dunedin, New Zealand. pp.45-58. Jurisica, I. 1994 How to Retrieve Relevant Information?. In Russell Greiner (Ed.): Proceedings of the AAAI Fall Symposium Series on Relevance, New Orleans, Louisiana. Kasabov, N. & A. Ralescu 1993 The Basics of Fuzzy Systems: Fuzzy System Applications. A tutorial at The First New Zealand International Two-stream Conference on Artificial Neural Networks and Expert Systems 49 Pages. Keane M. 1997, Dynamic Similarity: The Zany World of Processing Similarity. SimCat 97 An Interdisciplinary Workshop on Similarity And Categorisation, November, Department of Artificial Intelligence, University of Edinburgh, Keynote Address. Knauff, M. 1993 Introduction. Voß, A. (Ed.) Similarity Concepts and Retrieval Methods. FABEL-Report No. 13 Druck: Gesellschaft fur Mathematik und Datenverarbeitung mbH (GMD), Sankt Augustin. Knauff M. & C. Schlieder, 1993 Similarity assessment and case representation in case_based design. In M. M. Richter, S. Wess, K._D. Althoff, and F. Maurer, editors, First European Workshop on Case_Based Reasoning (EWCBR'93) Vol.1, p 37-42. Kolodner J. 1993 Case-Based Reasoning. San Mateo, Morgan Kaufmann, Publishers. Lees, B. G. 1997 Data Questions in GeoComputation. The 2nd International Conference on GeoComputation, University of Otago, Dunedin, New Zealand, pp. 289-296. Lilburne, L. (Ed), 1997. Proceedings of the workshop: Modelling the environment - the scaling problem. Landcare Research NZ Ltd., Lincoln. New Zealand, 74 pages. Lilburne, L. 1998. Scale issues in environmental data modelling. Internal Report, Landcare Research NZ Ltd., Lincoln, New Zealand. MacLaury R. E. 1997, Vantage Theory in Cognitive Science: An Anthropological Model of Categorization and Similarity Judgement. SimCat 97 An Interdisciplinary Workshop on Similarity And Categorisation, November, Department of Artificial Intelligence, University of Edinburgh, pp.157-164. O'Hara, S, 1992 A model of the `redescription' process in the context of geometric proportional analogy problems. In K. P. Jantke, editor, Proceedings of the International Workshop on Analogical and Inductive Inference, pages 268-293. SpringerVerlag. O'Hara, S. & B. Indurkhya, 1993 Incorporating (re_) interpretation in case_based reasoning. In M. M. Richter, S. Wess, K._D. Althoff, and F. Maurer, editors, EWCBR p 154-159, Kaiserslautern. Openshaw, S. & R. J. Abrahart 1996 GeoComputation. In the proceedings of The 1st International Conference on GeoComputation, University of Leeds, Leeds, England. 1: 665-666. Ortony, A. 1979 Beyond literal similarity. Psychological Review, 86:161-180. Osborne, H. & D. Bridge 1997, Models of Similarity for Case-Based Reasoning. SimCat 97 An Interdisciplinary Workshop on Similarity And Categorisation, November, Department of Artificial Intelligence, University of Edinburgh, pp. 173-180. Papadias, D. & M. J. Egenhofer, 1997, Algorithms for Hierarchical Spatial Reasoning. GeoInformatica: An International Journal on Advances of Computer Science for Geographical Information Systems, Vol. 1, Number 3, 251-274. Papadias, D. & Delis, B. 1997 Relation-based Similarity. Proceedings of the 5th ACM Workshop on GIS, Las Vegas, ACM Press. Pawlak, Z., Grzymala-Busse, J. W., Slowinski, R. & W. Ziarko, 1995, Rough Sets. CACM 38(11): 88-95. Quattrochi, D. A., & M. F. Goodchild (editors), 1997. Scaling in Remote Sensing and GIS. Boca Raton, FL: CRC/Lewis Publishers, Inc. Rodriguez A. R. 1997, Combining Different Domain Models into a Contextual Similarity Function. SimCat 97 An Interdisciplinary Workshop on Similarity And Categorisation, November, Department of Artificial Intelligence, University of Edinburgh, pp. 219-226. Savitsky, B. & Anselin, L. 1997. Scale. http://www.ncgia.ucsb.edu/other/ucgis/research_priorities/paper6.html. Schank, R. 1982, Dynamic memory: A theory of leaning in computers and people. Cambridge University press. New York. Seixas J. & J. Aparico 1994 A Framework for Spatial Reasoning the Task of Image Interpretation. EGIS, http://www.odyssey.ursus.maine.edu/gisweb/spatdb/egis/eg94015.html Sivapalan, M., & J. D. Kalma, 1995 Scale problems in hydrology: Contributions of the Robertson Workshop. Hydrological Processes 9(3/4):243-250. Tversky, A. 1977 Features of Similarity. Psychological Review 84(4): 327_352. Tversky, A. & D. H. Krantz, 1970 The dimensional representation and the metric structure of similarity data. Journal of Mathematical Psychology, Vol. 7. p572-597. Voß, A. 1993 Similarity Concepts and Retrieval Methods. FABEL-Report No. 13 Druck: Gesellschaft fur Mathematik und Datenverarbeitung mbH (GMD), Sankt Augustin. Wallace, D. Fraser, W. & Nicol L. 1997 System Design and Implementation of a Spatial Similarity System: WADAL incorporating GeoMatch. Information Science Dept, INFO408 Report, University of Otago, Dunedin, New Zealand, 62 pages. Wallach, M. A. 1958. On Psychological similarity. Psychological Review, 65(2):103-116. Watson, I.D. 1994 The Case for Case-Based Reasoning. In, Proc. Information Technology Awareness in Engineering Conference, 21-22 November 1994, London. Watson, I. D. 1997 Applying Case-based Reasoning: Techniques for Enterprise Systems. Morgan Kaufmann Publishers, Inc. California 289 pages. Wong, D., & C. Amrhein (eds), 1996 The Modifiable Areal Unit Problem. Special issue of Geographical Systems 3:2-3. Zadeh, L. 1965 Fuzzy sets. Information and Control, Vol.8. pp 338-353

Spatial isomorphism

This research continues with current innovative geocomputational research trends that aim to provide enhanced spatial analysis tools. The coupling of case-based reasoning (CBR) with GIS provides the focus of this paper. This coupling allows the retrieval, reuse, revision and retention of previous similar spatial cases. CBR is therefore used to develop more complex spatial data modelling methods (by using the CBR modules for improved spatial data manipulation) and provide enhanced exploratory geographical analysis tools (to find and assess certain patterns and relationships that may exist in spatial databases). This paper details the manner in which spatial similarity is assessed, for the purpose of re-using previous spatial cases. The authors consider similarity assessment a useful concept for retrieving and analysing spatial information as it may help researchers describe and explore a certain phenomena, its immediate environment and its relationships to other phenomena. This paper will address the following questions: What makes phenomena similar? What is the definition of similarity? What principles govern similarity? and How can similarity be measured? Generally, phenomena are similar when they share common attributes and circumstances. The degree of similarity depends on the type and number of commonalties they share. Within this research, similarity is examined from a spatial perspective. Spatial similarity is broadly defined by the authors as the spatial matching and ranking according to a specific context and scale. More specifically, similarity is governed by context (function, use, reason, goal, users frame-of mind), scale (coarse or fine level), repository (the application, local domain, site and data specifics), techniques (the available technology for searching, retrieving and recognising data) and measure and ranking systems. The degree of match is the score between a source and a target. In spatial matching a source and a target could be a pixel, region or coverage. The principles that govern spatial similarity are not just the attributes but also the relationships between two phenomena. This is one reason why CBR coupled with a GIS is fortuitous. A GIS is used symbiotically to extract spatial variables that can be used by CBR to determine similar spatial relations between phenomena. These spatial relations are used to assess the similarity between two phenomena (for example proximity and neighborhood analysis). Developing the concept of spatial similarity could assist with analysing spatial databases by developing techniques to match similar areas. This would help maximise the information that could be extracted from spatial databases. From an exploratory perspective, spatial similarity serves as an organising principle by which spatial phenomena are classified, relationships identified and generalisations made from previous bona fide experiences or knowledge. This paper will investigate the spatial similarity concept.UnpublishedAamodt, A. & E. Plaza, 1994 Case-based Reasoning: Foundational Issues, Methodological Variations and System Approaches. Artificial Intelligence Communications, Vol.7, No.1. Agouris, P., Stefanidis, A., & M. J. Egenhofer, 1997 I. Q. Image Query by Sketch http://www.spatial.maine.edu/~peggy/IQ.html. Black, W. Hutchinson, G. & T. K. Siang 1997 System Design and Implementation of a Spatial Similarity System: BLASH. Information Science Dept, INFO408 Report, University of Otago, Dunedin, New Zealand, 54 pages. Bruns, T. & M. Egenhofer 1996 Similarity of Spatial Scenes, in: M.-J. Kraak & M. Molenaar (eds.), Seventh International Symposium on Spatial Data Handling, Delft, The Netherlands Taylor & Francis, pp. 173-184. Buttenfield, B. P., & R. B. McMaster (editors), 1991. Map Generalization: Making Rules for Knowledge Representation. New York: Longmont Scientific and Technical. Cain, D. H., K. Ritters, & K. Orvis. 1997. A Multi-Scale Analysis Of Landscape Statistics. Landscape Ecology, 12(4), p. 199-212. Cao, C., & N. S.-N. Lam. 1997. Understanding the scale and resolution effects in remote sensing and GIS. In Scale in remote sensing and GIS, D. A. Quattrochi & M. F. Goodchild, eds., Lewis Publishers, p. 57-72. Carbonell, J. G. 1986 Derivational analogy. a theory of reconstructive problem solving and expertise acquisition. In R.S. Michalski, J.G. Carbonnel, and T.M. Mitchell, eds. Machine Learning : An Artificial Intelligence Approach, Vol. 2, pages 371--392. Morgan Kaufman Publishers, Los Altos, California. Cobb, M. A., Chung, M. J., Foley III, H., Petry, F. E., Shaw, K. B. & H. V. Miller, 1998, A Rule-based Approach for the Conflation of Attributed vector Data. GeoInformatica: An International Journal on Advances of Computer Science for Geographical Information Systems, Vol. 2, Number 1, 7-37. Cullinan, V. I., & J. M. Thomas. 1992. A comparison of quantitative methods for examining landscape pattern and scale. Landscape Ecology, 7(3), p. 211-227. Dubitzky W. Carville F. & J. Hughes 1993 Case-level Knowledge Modelling in CBR, Irish Journal of Psychology, 14:3 :478-479. Ehleringer, J. R., & C. B. Field (editors), 1993. Scaling Physiological Processes, Leaf to Globe. New York: Academic Press, Inc. Ellison T. M. 1997, Induction and Inherent Similarity. SimCat 97 An Interdisciplinary Workshop on Similarity And Categorisation, November, Department of Artificial Intelligence, University of Edinburgh, pp. 83-90. Elmasri, R. & Navathe, S. B. 1994, Fundamentals of Database Systems. The Benjamin/Cummings Publishing Company, Redwood City, C.A. Fayyad, U. M. 1997 Editorial. Data Mining and Knowledge Discovery, 1(1): 5-10. Flewelling, D. M. 1997, Comparing Subsets from Digital Spatial Archives: Point Set Similarity. Ph.D., University of Maine, Orono, Maine. Flickner, M., Sawhney, H., Niblack, W., Ashley, J., Huang, Q., Dom, B., Gorkani, M., Hafner, J., Lee, D., Petkovic, D., Steele, D. & P. Yanker, 1995 Query by Image and Video Content: The QBIC System. IEEE Computer 28(9): 23-32. Gentner, D. & Forbus, K. D. 1991 MAC/FAC: A model of similarity-based retrieval. In Proceedings of the 13th Annual Conference of the Cognitive Science Society, p 504-509, Chicago. Gentner, D. (1983). Structure-mapping: A theoretical framework for analogy. Cognitive Science, 7(2):155-170. Goodchild, M. F, Egenhofer, M. J. & R. Fegeas., 1998, Interoperating GISs. Report of a Specialist Meeting held under the auspices of the Varenius Project. http://www.ncgia.ucsb.edu/conf/interop97/interop_toc.html Grimnes M. and Aamodt A., 1996, A two layer case-based reasoning architecture for medical image understanding. In Advances in Case-Based Reasoning, Third European Workshop, EWCBR-96 Smith and Faltings (eds.), pp. 164-178. Grimnes M. 1996 Personal communication. Gudivada, V. 1995 On Spatial Similarity Measures for Multimedia Applications, in: SPIE, pp. 363-372. Gudivada, V. and V. Raghavan 1995 Design and Evaluation of Algorithms for Image Retrieval by Spatial Similarity, ACM Transactions on Information Systems, 13 (2): 115-144. Hampton J. A. 1997, Similarity and Categorization. SimCat 97 An Interdisciplinary Workshop on Similarity And Categorisation, November, Department of Artificial Intelligence, University of Edinburgh, pp. 103-110. Higham, E. C. Holt, A. & G.W. Kearsley, 1996 Tourist Flow Reasoning: The Spatial Similarities of Tourist Movements. In the Proceedings of the 8th Annual Colloquium of the Spatial Information Research Centre, Otago University, Dunedin, New Zealand, pp 69-78. Holt, A. 1996a Allowing the Environment to Model Itself. Environmental Perspectives, A Quarterly Newsletter published by the Environmental Policy & Management Research Centre, Issue 10: 6-7. Holt, A. 1996b Incorporating A New Computational Reasoning Approach to Spatial Modelling. In the proceedings of The 1st International Conference on GeoComputation, University of Leeds, Leeds, England. 1: 427-442. Holt, A. 1997 GeoComputation-neologism, or gambit towards progressive research? Environmental Perspectives, A Quarterly Newsletter published by the Environmental Policy & Management Research Centre, Issue 15:5-6. Holt, A. & G. L. Benwell, (1996), Case-based Reasoning and Spatial Analysis. Journal of the Urban and Regional Information Systems Association, 8(1) :27-36. Holt, A. & G. L. Benwell 1997 Using Spatial Similarity for Exploratory Spatial Data Analysis: Some Directions. The 2nd International Conference on GeoComputation, University of Otago, Dunedin, New Zealand, pp. 279-288. Holt, A. & G. L. Benwell (In Press) Applying Case-based Reasoning to Spatial Phenomena, The International Journal of Geographical Information Science. 30 pages. (accepted for publication). Holt, A., Higham, E. C. & G.W. Kearsley, 1996 Elucidating International Tourist Movements: An Intelligent Approach. In the proceedings of Tourism Down Under II: A Tourism Research Conference, Centre for Tourism, University of Otago, Dunedin, pp. 167-180. Holt, A., Higham, E. C. & G.W. Kearsley, 1997 Predicting International Tourist Flows: Using a Spatial Reasoning System. The Pacific Tourism Review: An Interdisciplinary Journal. 1:4 30 pages. Holt, A. & R. E. MacLaury, (In Press) Spatial Vantages: Understanding Spatial Similarities. Language Sciences: A Special Issue on Vantage Theory, ed. N. Love. Hudson, J., 1992. Scale in space and time. In R. F. Abler, M. G. Markus, & J. M. Olson (Editors), Geography's Inner Worlds: Pervasive Themes in Contemporary American Geography. New Brunswick, NJ: Rutgers University Press, pp. 280-300. Indurkhya, B., 1992 Metaphor and cognition: An interactionist approach. Kluwer Academic Publishers, Dordrecht. Indurkhya, B., 1991 On the role of interpretive analogy in learning. NGC, 8(4):385-402. Jagadish, H. V. 1991 A retrieval technique for similar shapes. In the proceedings of the 10th ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems, pages 208-217, Denver, Colorado. Jagadish, H. V., Mendelzon, A. O. & T. Milo. 1995 Similarity_based queries. PODS. Jain, A. K. & R. Hoffmann, 1988 Evidence-based recognition of 3D Objects. IEEE Transactions On Pattern Analysis And Machine Learning, Vol. 10, No. 6. pp. 783-801. Jeffery, N. Teather, D. & Teather, B. A., 1997, Case-Based Training Using Similarity and Categorization from a Multiple Correspondence Analysis. SimCat 97 An Interdisciplinary Workshop on Similarity And Categorisation, November, Department of Artificial Intelligence, University of Edinburgh, pp. 123-130. Jin, J. S., Greenfield H., & Kurniawati R., 1997 CBIR-VU: a new scheme for processing visual data in multimedia systems. Lecture Notes in Computer Science: Visual Information Systems, Leung C. H. C., Springer Verlag, pp40-65. Jones, E. K. & A. Roydhouse, 1994 Spatial Representations of Meteorological Data for Intelligent Retrieval. The Sixth Annual Colloquium of the Spatial Research Centre, Proceedings. Eds. G.L. Benwell and N.C. Sutherland. Dunedin, New Zealand. pp.45-58. Jurisica, I. 1994 How to Retrieve Relevant Information?. In Russell Greiner (Ed.): Proceedings of the AAAI Fall Symposium Series on Relevance, New Orleans, Louisiana. Kasabov, N. & A. Ralescu 1993 The Basics of Fuzzy Systems: Fuzzy System Applications. A tutorial at The First New Zealand International Two-stream Conference on Artificial Neural Networks and Expert Systems 49 Pages. Keane M. 1997, Dynamic Similarity: The Zany World of Processing Similarity. SimCat 97 An Interdisciplinary Workshop on Similarity And Categorisation, November, Department of Artificial Intelligence, University of Edinburgh, Keynote Address. Knauff, M. 1993 Introduction. Voß, A. (Ed.) Similarity Concepts and Retrieval Methods. FABEL-Report No. 13 Druck: Gesellschaft fur Mathematik und Datenverarbeitung mbH (GMD), Sankt Augustin. Knauff M. & C. Schlieder, 1993 Similarity assessment and case representation in case_based design. In M. M. Richter, S. Wess, K._D. Althoff, and F. Maurer, editors, First European Workshop on Case_Based Reasoning (EWCBR'93) Vol.1, p 37-42. Kolodner J. 1993 Case-Based Reasoning. San Mateo, Morgan Kaufmann, Publishers. Lees, B. G. 1997 Data Questions in GeoComputation. The 2nd International Conference on GeoComputation, University of Otago, Dunedin, New Zealand, pp. 289-296. Lilburne, L. (Ed), 1997. Proceedings of the workshop: Modelling the environment - the scaling problem. Landcare Research NZ Ltd., Lincoln. New Zealand, 74 pages. Lilburne, L. 1998. Scale issues in environmental data modelling. Internal Report, Landcare Research NZ Ltd., Lincoln, New Zealand. MacLaury R. E. 1997, Vantage Theory in Cognitive Science: An Anthropological Model of Categorization and Similarity Judgement. SimCat 97 An Interdisciplinary Workshop on Similarity And Categorisation, November, Department of Artificial Intelligence, University of Edinburgh, pp.157-164. O'Hara, S, 1992 A model of the `redescription' process in the context of geometric proportional analogy problems. In K. P. Jantke, editor, Proceedings of the International Workshop on Analogical and Inductive Inference, pages 268-293. SpringerVerlag. O'Hara, S. & B. Indurkhya, 1993 Incorporating (re_) interpretation in case_based reasoning. In M. M. Richter, S. Wess, K._D. Althoff, and F. Maurer, editors, EWCBR p 154-159, Kaiserslautern. Openshaw, S. & R. J. Abrahart 1996 GeoComputation. In the proceedings of The 1st International Conference on GeoComputation, University of Leeds, Leeds, England. 1: 665-666. Ortony, A. 1979 Beyond literal similarity. Psychological Review, 86:161-180. Osborne, H. & D. 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Black carbon and other light-absorbing impurities in snow in the Chilean Andes

Vertical profiles of black carbon (BC) and other light-absorbing impurities were measured in seasonal snow and permanent snowfields in the Chilean Andes during Austral winters 2015 and 2016, at 22 sites between latitudes 18°S and 41°S. The samples were analyzed for spectrally-resolved visible light absorption. For surface snow, the average mass mixing ratio of BC was 15 ng/g in northern Chile (18–33°S), 28 ng/g near Santiago (a major city near latitude 33°S, where urban pollution plays a significant role), and 13 ng/g in southern Chile (33–41°S). The regional average vertically-integrated loading of BC was 207 µg/m 2 in the north, 780 µg/m 2 near Santiago, and 2500 µg/m 2 in the south, where the snow season was longer and the snow was deeper. For samples collected at locations where there had been no new snowfall for a week or more, the BC concentration in surface snow was high (~10–100 ng/g) and the sub-surface snow was comparatively clean, indicating the domin