[ { "key": "577BHE35", "version": 25, "library": { "type": "group", "id": 247955, "name": "Spatiotemporal solar experiments", "links": { "alternate": { "href": "https://www.zotero.org/groups/spatiotemporal_solar_experiments", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/247955/items/577BHE35", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/spatiotemporal_solar_experiments/items/577BHE35", "type": "text/html" } }, "meta": { "createdByUser": { "id": 1261598, "username": "Bibliobomb", "name": "Alex Hernandez", "links": { "alternate": { "href": "https://www.zotero.org/bibliobomb", "type": "text/html" } } }, "creatorSummary": "Min et al.", "parsedDate": "2013-10", "numChildren": 0 }, "data": { "key": "577BHE35", "version": 25, "itemType": "journalArticle", "title": "A general method of spatio-temporal clustering analysis", "creators": [ { "creatorType": "author", "firstName": "Deng", "lastName": "Min" }, { "creatorType": "author", "firstName": "Liu", "lastName": "QiLiang" }, { "creatorType": "author", "firstName": "Wang", "lastName": "JiaQiu" }, { "creatorType": "author", "firstName": "Shi", "lastName": "Yan" } ], "abstractNote": "Spatio-temporal clustering has been a hot topic in the field of spatio-temporal data mining and knowledge discovery. It can be employed to uncover and interpret developmental trends of geographic phenomenon in the real world. However, existing spatio-temporal clustering methods seldom consider both spatiotemporal autocorrelations and heterogeneities among spatio-temporal entities, and the coupling in space and time has not been well highlighted. In this paper, a unified framework for the clustering analysis of spatio-temporal data is proposed, and a novel spatio-temporal clustering algorithm is developed by means of a spatio-temporal statistics methodology and intelligence computation technology. Our method is applied successfully to finding spatio-temporal cluster in China's annual temperature database for the period 1951-1992.", "publicationTitle": "Science China-Information Sciences", "publisher": "", "place": "", "date": "OCT 2013", "volume": "56", "issue": "10", "section": "", "partNumber": "", "partTitle": "", "pages": "102315", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "Sci. China-Inf. Sci.", "DOI": "10.1007/s11432-011-4391-8", "citationKey": "", "url": "http://dx.doi.org/10.1007/s11432-011-4391-8", "accessDate": "", "PMID": "", "PMCID": "", "ISSN": "1674-733X", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "English", "libraryCatalog": "ISI Web of Knowledge", "callNumber": "", "rights": "", "extra": "WOS:000327826500015", "tags": [ { "tag": "space-time interaction" }, { "tag": "spatio-temporal autocorrelation" }, { "tag": "spatio-temporal clustering" }, { "tag": "spatio-temporal data mining" }, { "tag": "spatio-temporal heterogeneity" } ], "collections": [], "relations": {}, "dateAdded": "2014-02-11T22:47:39Z", "dateModified": "2014-02-11T23:10:10Z" } }, { "key": "IP2GS7SV", "version": 24, "library": { "type": "group", "id": 247955, "name": "Spatiotemporal solar experiments", "links": { "alternate": { "href": "https://www.zotero.org/groups/spatiotemporal_solar_experiments", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/247955/items/IP2GS7SV", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/spatiotemporal_solar_experiments/items/IP2GS7SV", "type": "text/html" } }, "meta": { "createdByUser": { "id": 1261598, "username": "Bibliobomb", "name": "Alex Hernandez", "links": { "alternate": { "href": "https://www.zotero.org/bibliobomb", "type": "text/html" } } }, "creatorSummary": "Li et al.", "parsedDate": "2013-06-01", "numChildren": 0 }, "data": { "key": "IP2GS7SV", "version": 24, "itemType": "journalArticle", "title": "An efficient measure of compactness for two-dimensional shapes and its application in regionalization problems", "creators": [ { "creatorType": "author", "firstName": "Wenwen", "lastName": "Li" }, { "creatorType": "author", "firstName": "Michael F.", "lastName": "Goodchild" }, { "creatorType": "author", "firstName": "Richard", "lastName": "Church" } ], "abstractNote": "A measure of shape compactness is a numerical quantity representing the degree to which a shape is compact. Ways to provide an accurate measure have been given great attention due to its application in a broad range of GIS problems, such as detecting clustering patterns from remote-sensing images, understanding urban sprawl, and redrawing electoral districts to avoid gerrymandering. In this article, we propose an effective and efficient approach to computing shape compactness based on the moment of inertia (MI), a well-known concept in physics. The mathematical framework and the computer implementation for both raster and vector models are discussed in detail. In addition to computing compactness for a single shape, we propose a computational method that is capable of calculating the variations in compactness as a shape grows or shrinks, which is a typical application found in regionalization problems. We conducted a number of experiments that demonstrate the superiority of the MI over the popular isoperimetric quotient approach in terms of (1) computational efficiency; (2) tolerance of positional uncertainty and irregular boundaries; (3) ability to handle shapes with holes and multiple parts; and (4) applicability and efficacy in districting/zonation/regionalization problems.", "publicationTitle": "International Journal of Geographical Information Science", "publisher": "", "place": "", "date": "JUN 1 2013", "volume": "27", "issue": "6", "section": "", "partNumber": "", "partTitle": "", "pages": "1227-1250", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "Int. J. Geogr. Inf. Sci.", "DOI": "10.1080/13658816.2012.752093", "citationKey": "", "url": "http://dx.doi.org/10.1080/13658816.2012.752093", "accessDate": "", "PMID": "", "PMCID": "", "ISSN": "1365-8816", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "English", "libraryCatalog": "ISI Web of Knowledge", "callNumber": "", "rights": "", "extra": "WOS:000319497900010", "tags": [ { "tag": "aggregation" }, { "tag": "automated zoning procedure" }, { "tag": "census" }, { "tag": "compactness" }, { "tag": "districting" }, { "tag": "geographic Information Science" }, { "tag": "geography" }, { "tag": "moment of inertia" }, { "tag": "pattern recognition" }, { "tag": "perimeter" }, { "tag": "raster data modelling" }, { "tag": "region" }, { "tag": "regionalization" }, { "tag": "scale" }, { "tag": "shape analysis" }, { "tag": "shape index" }, { "tag": "vector data modelling" } ], "collections": [], "relations": {}, "dateAdded": "2014-02-11T22:37:24Z", "dateModified": "2014-02-11T23:09:34Z" } }, { "key": "EGGD3K9T", "version": 23, "library": { "type": "group", "id": 247955, "name": "Spatiotemporal solar experiments", "links": { "alternate": { "href": "https://www.zotero.org/groups/spatiotemporal_solar_experiments", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/247955/items/EGGD3K9T", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/spatiotemporal_solar_experiments/items/EGGD3K9T", "type": "text/html" } }, "meta": { "createdByUser": { "id": 1261598, "username": "Bibliobomb", "name": "Alex Hernandez", "links": { "alternate": { "href": "https://www.zotero.org/bibliobomb", "type": "text/html" } } }, "creatorSummary": "Folch and Spielman", "parsedDate": "2014-01-02", "numChildren": 0 }, "data": { "key": "EGGD3K9T", "version": 23, "itemType": "journalArticle", "title": "Identifying regions based on flexible user-defined constraints", "creators": [ { "creatorType": "author", "firstName": "David C.", "lastName": "Folch" }, { "creatorType": "author", "firstName": "Seth E.", "lastName": "Spielman" } ], "abstractNote": "The identification of regions is both a computational and conceptual challenge. Even with growing computational power, regionalization algorithms must rely on heuristic approaches in order to find solutions. Therefore, the constraints and evaluation criteria that define a region must be translated into an algorithm that can efficiently and effectively navigate the solution space to find the best solution. One limitation of many existing regionalization algorithms is a requirement that the number of regions be selected a priori. The recently introduced max-p algorithm does not have this requirement, and thus the number of regions is an output of, not an input to, the algorithm. In this paper, we extend the max-p algorithm to allow for greater flexibility in the constraints available to define a feasible region, placing the focus squarely on the multidimensional characteristics of the region. We also modify technical aspects of the algorithm to provide greater flexibility in its ability to search the solution space. Using synthetic spatial and attribute data, we are able to show the algorithm's broad ability to identify regions in maps of varying complexity. We also conduct a large-scale computational experiment to identify parameter settings that result in the greatest solution accuracy under various scenarios. The rules of thumb identified from the experiment produce maps that correctly assign areas to their true' region with 94% average accuracy, with nearly 50% of the simulations reaching 100% accuracy.", "publicationTitle": "International Journal of Geographical Information Science", "publisher": "", "place": "", "date": "JAN 2 2014", "volume": "28", "issue": "1", "section": "", "partNumber": "", "partTitle": "", "pages": "164-184", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "Int. J. Geogr. Inf. Sci.", "DOI": "10.1080/13658816.2013.848986", "citationKey": "", "url": "http://dx.doi.org/10.1080/13658816.2013.848986", "accessDate": "", "PMID": "", "PMCID": "", "ISSN": "1365-8816", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "English", "libraryCatalog": "ISI Web of Knowledge", "callNumber": "", "rights": "", "extra": "WOS:000326615500009", "tags": [ { "tag": "algorithms" }, { "tag": "clusters" }, { "tag": "functional regions" }, { "tag": "max-p" }, { "tag": "neighborhoods" }, { "tag": "number" }, { "tag": "regionalization" }, { "tag": "regions" }, { "tag": "search" }, { "tag": "tabu search" } ], "collections": [], "relations": {}, "dateAdded": "2014-02-11T22:29:04Z", "dateModified": "2014-02-11T23:08:56Z" } }, { "key": "2SX2URTR", "version": 22, "library": { "type": "group", "id": 247955, "name": "Spatiotemporal solar experiments", "links": { "alternate": { "href": "https://www.zotero.org/groups/spatiotemporal_solar_experiments", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/247955/items/2SX2URTR", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/spatiotemporal_solar_experiments/items/2SX2URTR", "type": "text/html" } }, "meta": { "createdByUser": { "id": 1261598, "username": "Bibliobomb", "name": "Alex Hernandez", "links": { "alternate": { "href": "https://www.zotero.org/bibliobomb", "type": "text/html" } } }, "creatorSummary": "Strauss et al.", "parsedDate": "2013-02", "numChildren": 0 }, "data": { "key": "2SX2URTR", "version": 22, "itemType": "journalArticle", "title": "High resolution climate data for Austria in the period 2008-2040 from a statistical climate change model", "creators": [ { "creatorType": "author", "firstName": "F.", "lastName": "Strauss" }, { "creatorType": "author", "firstName": "H.", "lastName": "Formayer" }, { "creatorType": "author", "firstName": "E.", "lastName": "Schmid" } ], "abstractNote": "Climate change data for Austria have been produced for the period from 2008 to 2040, with a temporal/spatial resolution of 1 d and 1 km2. The climate change data are based on historical daily weather station data from 1975 to 2007, and linear regression modelling with repeated bootstrapping. The spatial resolution is based on 60 climate clusters which represent homogenous climates with respect to mean annual precipitation sums and mean annual temperatures from the period 1961 to 1990. For each climate cluster, a regression model fit has been performed and extrapolated for the period 20082040. The integral parts of our regression model are: (1) the extrapolation of the observed linear temperature trend from 1975 to 2007, by using an average national trend of approximately 0.05 degrees C per year derived from a homogenized dataset, and (2) the repeated bootstrapping of historical temperature residuals, and of the observations for some other weather parameters, such as solar radiation, precipitation, relative humidity and wind speed. Thus, we ensure consistent physical, spatial and temporal correlations. Precipitation scenarios have been developed to account for any possible wider range of precipitation patterns. These scenarios include increased/decreased annual precipitation sums, as well as unchanged annual precipitation sums, but with different seasonal distributions. These climate change data are available at: http://www.landnutzung.at/Klima_Daten.html Copyright (C) 2012 Royal Meteorological Society", "publicationTitle": "International Journal of Climatology", "publisher": "", "place": "", "date": "FEB 2013", "volume": "33", "issue": "2", "section": "", "partNumber": "", "partTitle": "", "pages": "430-443", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "Int. J. Climatol.", "DOI": "10.1002/joc.3434", "citationKey": "", "url": "http://dx.doi.org/10.1002/joc.3434", "accessDate": "", "PMID": "", "PMCID": "", "ISSN": "0899-8418", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "English", "libraryCatalog": "ISI Web of Knowledge", "callNumber": "", "rights": "", "extra": "WOS:000313753900013", "tags": [ { "tag": "Austria" }, { "tag": "bootstrapping" }, { "tag": "linear regression" }, { "tag": "regional climate change data" }, { "tag": "statistical climate change model" } ], "collections": [], "relations": {}, "dateAdded": "2014-02-11T22:25:50Z", "dateModified": "2014-02-11T23:08:14Z" } }, { "key": "N7ZGHMP6", "version": 21, "library": { "type": "group", "id": 247955, "name": "Spatiotemporal solar experiments", "links": { "alternate": { "href": "https://www.zotero.org/groups/spatiotemporal_solar_experiments", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/247955/items/N7ZGHMP6", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/spatiotemporal_solar_experiments/items/N7ZGHMP6", "type": "text/html" } }, "meta": { "createdByUser": { "id": 1261598, "username": "Bibliobomb", "name": "Alex Hernandez", "links": { "alternate": { "href": "https://www.zotero.org/bibliobomb", "type": "text/html" } } }, "creatorSummary": "Iyigun et al.", "parsedDate": "2013", "numChildren": 0 }, "data": { "key": "N7ZGHMP6", "version": 21, "itemType": "journalArticle", "title": "Clustering current climate regions of Turkey by using a multivariate statistical method", "creators": [ { "creatorType": "author", "firstName": "Cem", "lastName": "Iyigun" }, { "creatorType": "author", "firstName": "Murat", "lastName": "Türkeş" }, { "creatorType": "author", "firstName": "İnci", "lastName": "Batmaz" }, { "creatorType": "author", "firstName": "Ceylan", "lastName": "Yozgatligil" }, { "creatorType": "author", "firstName": "Vilda", "lastName": "Purutçuoğlu" }, { "creatorType": "author", "firstName": "Elçin", "lastName": "Koç" }, { "creatorType": "author", "firstName": "Muhammed", "lastName": "Öztürk" } ], "abstractNote": "In this study, the hierarchical clustering technique, called Ward method, was applied for grouping common features of air temperature series, precipitation total and relative humidity series of 244 stations in Turkey. Results of clustering exhibited the impact of physical geographical features of Turkey, such as topography, orography, land–sea distribution and the high Anatolian peninsula on the geographical variability. Based on the monthly series of nine climatological observations recorded for the period of 1970–2010, 12 and 14 clusters of climate zones are determined. However, from the comparative analyses, it is decided that 14 clusters represent the climate of Turkey more realistically. These clusters are named as (1) Dry Summer Subtropical Semihumid Coastal Aegean Region; (2) Dry-Subhumid Mid-Western Anatolia Region; (3 and 4) Dry Summer Subtropical Humid Coastal Mediterranean region [(3) West coast Mediterranean and (4) Eastern Mediterranean sub-regions]; (5) Semihumid Eastern Marmara Transition Sub-region; (6) Dry Summer Subtropical Semihumid/Semiarid Continental Mediterranean region; (7) Semihumid Cold Continental Eastern Anatolia region; (8) Dry-subhumid/Semiarid Continental Central Anatolia Region; (9 and 10) Mid-latitude Humid Temperate Coastal Black Sea Region [(9) West Coast Black Sea and (10) East Coast Black Sea sub-regions]; (11) Semihumid Western Marmara Transition Sub-region; (12) Semihumid Continental Central to Eastern Anatolia Sub-region; (13) Rainy Summer Semihumid Cold Continental Northeastern Anatolia Sub-region; and (14) Semihumid Continental Mediterranean to Eastern Anatolia Transition Sub-region. We believe that this study can be considered as a reference for the other climate-related researches of Turkey, and can be useful for the detection of Turkish climate regions, which are obtained by a long-term time course dataset having many meteorological variables.", "publicationTitle": "Theoretical and Applied Climatology", "publisher": "", "place": "", "date": "2013", "volume": "114", "issue": "1", "section": "", "partNumber": "", "partTitle": "", "pages": "", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "", "DOI": "10.1007/s00704-012-0823-7", "citationKey": "", "url": "http://dx.doi.org/10.1007/s00704-012-0823-7", "accessDate": "", "PMID": "", "PMCID": "", "ISSN": "0177-798X", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "eng", "libraryCatalog": "Primo", "callNumber": "", "rights": "", "extra": "", "tags": [], "collections": [], "relations": {}, "dateAdded": "2014-02-11T19:27:55Z", "dateModified": "2014-02-11T23:07:17Z" } }, { "key": "PNQ52U7D", "version": 20, "library": { "type": "group", "id": 247955, "name": "Spatiotemporal solar experiments", "links": { "alternate": { "href": "https://www.zotero.org/groups/spatiotemporal_solar_experiments", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/247955/items/PNQ52U7D", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/spatiotemporal_solar_experiments/items/PNQ52U7D", "type": "text/html" } }, "meta": { "createdByUser": { "id": 1261598, "username": "Bibliobomb", "name": "Alex Hernandez", "links": { "alternate": { "href": "https://www.zotero.org/bibliobomb", "type": "text/html" } } }, "creatorSummary": "Cadavid et al.", "parsedDate": "1998", "numChildren": 0 }, "data": { "key": "PNQ52U7D", "version": 20, "itemType": "journalArticle", "title": "Spatiotemporal correlations and turbulent photospheric flows from soho /mdi velocity data", "creators": [ { "creatorType": "author", "firstName": "A. C.", "lastName": "Cadavid" }, { "creatorType": "author", "firstName": "J. K.", "lastName": "Lawrence" }, { "creatorType": "author", "firstName": "A. A.", "lastName": "Ruzmaikin" }, { "creatorType": "author", "firstName": "S. R.", "lastName": "Walton" }, { "creatorType": "author", "firstName": "T.", "lastName": "Tarbell" } ], "abstractNote": "Time series of high-resolution and full-disk velocity images obtained with the Michelson Doppler Imager (MDI) instrument on board SOHO have been used to calculate the spacetime spectrum of photospheric velocity flow. The effects of different methods for filtering acoustic oscillations have been carefully studied. It is found that the spectra show contributions both from organized structures that have their origin in the convection zone and from the turbulent flow. By considering time series of different duration and cadence in solar regions with different line-of-sight projections, it is possible to distinguish the contributions of the spectra from the two different kinds of flows. The spectra associated with the turbulent velocity fields obey power laws characterized by two scaling parameters whose values can be used to describe the type of diffusion. The first parameter is the spectral exponent of the spatial correlation function and the second is a scaling parameter of the time correlation function. Inclusion of the time parameter is an essential difference between the present work and other solar studies. Within the confidence limits of the data, the values of the two parameters indicate that the turbulent part of the flow in the scale range 16-120 Mm produces superdiffusive transport.", "publicationTitle": "The Astrophysical Journal Letters", "publisher": "", "place": "", "date": "1998", "volume": "509", "issue": "2", "section": "", "partNumber": "", "partTitle": "", "pages": "", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "", "DOI": "10.1086/306507", "citationKey": "", "url": "http://dx.doi.org/10.1086/306507", "accessDate": "", "PMID": "", "PMCID": "", "ISSN": "0004-637X", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "eng", "libraryCatalog": "Primo", "callNumber": "", "rights": "", "extra": "", "tags": [], "collections": [], "relations": {}, "dateAdded": "2014-02-11T18:43:09Z", "dateModified": "2014-02-11T23:06:13Z" } }, { "key": "E5BFGKJE", "version": 19, "library": { "type": "group", "id": 247955, "name": "Spatiotemporal solar experiments", "links": { "alternate": { "href": "https://www.zotero.org/groups/spatiotemporal_solar_experiments", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/247955/items/E5BFGKJE", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/spatiotemporal_solar_experiments/items/E5BFGKJE", "type": "text/html" } }, "meta": { "createdByUser": { "id": 1261598, "username": "Bibliobomb", "name": "Alex Hernandez", "links": { "alternate": { "href": "https://www.zotero.org/bibliobomb", "type": "text/html" } } }, "creatorSummary": "Liu et al.", "parsedDate": "2011", "numChildren": 0 }, "data": { "key": "E5BFGKJE", "version": 19, "itemType": "journalArticle", "title": "Classification of non-vegetated areas using Formosat-2 high spatiotemporal imagery: the case of Tseng-Wen Reservoir catchment area (Taiwan)", "creators": [ { "creatorType": "author", "firstName": "Cheng-chien", "lastName": "Liu" }, { "creatorType": "author", "firstName": "Chjeng-lun", "lastName": "Shieh" }, { "creatorType": "author", "firstName": "Jia-chin", "lastName": "Lin" }, { "creatorType": "author", "firstName": "An-ming", "lastName": "Wu" } ], "abstractNote": "K The occurrence of landslides in the catchment area is a potential threat to the water quality and the lifespan of a reservoir. Due to the limitations of spatial coverage in ground surveys and of temporal resolution in aerial photos, it is difficult to monitor such events in the entire catchment area at short intervals. Formosat-2 is the first commercial satellite dedicated to site surveillance with a high-spatial-resolution sensor placed in a daily revisit orbit (2 m in panchromatic and 8 m in multi-spectral). In this research, a new approach is proposed to identify the non-vegetated areas in the multi-temporal and multi-spectral images taken by Formosat-2 by integrating the Getis statistic, the spectral index and the unsupervised K -means classification. With this new approach, we analyse a total of 16 pairs of Formosat-2 images, taken in the catchment area of Tseng-Wen Reservoir from February to December 2006 at an interval of three to four weeks. The results show that newly developed non-vegetated areas are closely related to earthquakes and rainfall. Once the slump material is generated by an earthquake, a comparatively low amount of rainfall will trigger its flushing. However, once the slump material has gone, there are no significant changes in the non-vegetated areas, even with severe weather events such as typhoons or storms. This suggests that the most critical time for protecting the reservoir is right after an earthquake and before the next rain. If the slump material is not managed or removed during this crucial period of time, eventually it will fall into the reservoir. Since the catchment area of Tseng-Wen Reservoir is protected and restricted from access, most of the non-vegetated areas should be closely related to landslides caused by natural processes (such as rainfall or earthquake) rather than man-made processes (such as tree cutting or degradation of vegetation). This research demonstrates the potential of Formosat-2 imagery in monitoring the spatial and temporal variations of landslides in the catchment areas of reservoirs.", "publicationTitle": "International Journal of Remote Sensing", "publisher": "", "place": "", "date": "2011", "volume": "32", "issue": "23", "section": "", "partNumber": "", "partTitle": "", "pages": "", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "", "DOI": "10.1080/01431161.2010.542200", "citationKey": "", "url": "http://dx.doi.org/10.1080/01431161.2010.542200", "accessDate": "", "PMID": "", "PMCID": "", "ISSN": "0143-1161", "archive": "", "archiveLocation": "", "shortTitle": "Classification of non-vegetated areas using Formosat-2 high spatiotemporal imagery", "language": "", "libraryCatalog": "Primo", "callNumber": "", "rights": "", "extra": "", "tags": [], "collections": [], "relations": {}, "dateAdded": "2014-02-11T18:41:30Z", "dateModified": "2014-02-11T23:05:33Z" } }, { "key": "Q3ZBDNTH", "version": 18, "library": { "type": "group", "id": 247955, "name": "Spatiotemporal solar experiments", "links": { "alternate": { "href": "https://www.zotero.org/groups/spatiotemporal_solar_experiments", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/247955/items/Q3ZBDNTH", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/spatiotemporal_solar_experiments/items/Q3ZBDNTH", "type": "text/html" } }, "meta": { "createdByUser": { "id": 1261598, "username": "Bibliobomb", "name": "Alex Hernandez", "links": { "alternate": { "href": "https://www.zotero.org/bibliobomb", "type": "text/html" } } }, "creatorSummary": "Mittal and Bhatia", "parsedDate": "2010", "numChildren": 0 }, "data": { "key": "Q3ZBDNTH", "version": 18, "itemType": "document", "title": "Wireless sensor networks for monitoring the environmental activities", "creators": [ { "creatorType": "author", "firstName": "R.", "lastName": "Mittal" }, { "creatorType": "author", "firstName": "M. P. S.", "lastName": "Bhatia" } ], "abstractNote": "The area of sensor network has a long history and many kind of sensor devices are used in various real life applications. Here, we introduce Wireless sensor network which when combine with other areas then plays an important role in analyzing the data of forest temperature, bioinformatics, water contamination, traffic control, telecommunication etc. Due to the advancement in the area of wireless sensor network and their ability to generate large amount of spatial/temporal data, always attract researchers for applying data mining techniques and getting interesting results. Wireless sensor networks in monitoring the environmental activities grows and this attract greater interest and challenge for finding out the patterns from large amount of spatial/temporal datasets. These datasets are generated by sensor nodes which are deployed in some tropical regions or from some wearable sensor nodes which are attached with wild animals in wild life centuries. Sensor networks generate continuous stream of data over time. So, Data mining techniques always plays a vital role for extracting the knowledge form large wireless sensor network data. In this paper, we present the detection of sensor data irregularities, Sensor data clustering, Pattern matching and their interesting results and with these results we can analyze the sensor node data in different ways.", "type": "", "date": "2010", "publisher": "", "place": "", "DOI": "", "citationKey": "", "url": "http://dx.doi.org/10.1109/ICCIC.2010.5705791", "accessDate": "", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "", "libraryCatalog": "Primo", "callNumber": "", "rights": "", "extra": "", "tags": [ { "tag": "Bioinformatics" }, { "tag": "Clustering" }, { "tag": "Computerised Monitoring" }, { "tag": "Data Mining" }, { "tag": "Data Mining Technique" }, { "tag": "Data Stream" }, { "tag": "Environmental Monitoring" }, { "tag": "Environmental Monitoring (geophysics)" }, { "tag": "Environmental Science Computing" }, { "tag": "Forest Temperature Data" }, { "tag": "Knowledge Extraction" }, { "tag": "Mining" }, { "tag": "Pattern Clustering" }, { "tag": "Pattern Matching" }, { "tag": "Sensor Data Clustering" }, { "tag": "Sensor Device" }, { "tag": "Sensor Node" }, { "tag": "Sensors" }, { "tag": "Spatial-temporal Data" }, { "tag": "Spatiotemporal Phenomena" }, { "tag": "Traffic Control" }, { "tag": "Tropical Region" }, { "tag": "Water Contamination" }, { "tag": "Wearable Computers" }, { "tag": "Wearable Sensor Node" }, { "tag": "Wild Animal" }, { "tag": "Wild Life Century" }, { "tag": "Wireless Sensor Network" }, { "tag": "Wireless Sensor Networks" } ], "collections": [], "relations": {}, "dateAdded": "2014-02-11T18:45:08Z", "dateModified": "2014-02-11T23:04:52Z" } }, { "key": "NKE6I4CM", "version": 17, "library": { "type": "group", "id": 247955, "name": "Spatiotemporal solar experiments", "links": { "alternate": { "href": "https://www.zotero.org/groups/spatiotemporal_solar_experiments", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/247955/items/NKE6I4CM", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/spatiotemporal_solar_experiments/items/NKE6I4CM", "type": "text/html" } }, "meta": { "createdByUser": { "id": 1261598, "username": "Bibliobomb", "name": "Alex Hernandez", "links": { "alternate": { "href": "https://www.zotero.org/bibliobomb", "type": "text/html" } } }, "creatorSummary": "Jiang et al.", "parsedDate": "2011-08", "numChildren": 0 }, "data": { "key": "NKE6I4CM", "version": 17, "itemType": "journalArticle", "title": "A new hybrid method based on partitioning-based DBSCAN and ant clustering", "creators": [ { "creatorType": "author", "firstName": "Hua", "lastName": "Jiang" }, { "creatorType": "author", "firstName": "Jing", "lastName": "Li" }, { "creatorType": "author", "firstName": "Shenghe", "lastName": "Yi" }, { "creatorType": "author", "firstName": "Xiangyang", "lastName": "Wang" }, { "creatorType": "author", "firstName": "Xin", "lastName": "Hu" } ], "abstractNote": "Clustering problem is an unsupervised learning problem. It is a procedure that partition data objects into matching clusters. The data objects in the same cluster are quite similar to each other and dissimilar in the other clusters. Density-based clustering algorithms find clusters based on density of data points in a region. DBSCAN algorithm is one of the density-based clustering algorithms. It can discover clusters with arbitrary shapes and only requires two input parameters. DBSCAN has been proved to be very effective for analyzing large and complex spatial databases. However, DBSCAN needs large volume of memory support and often has difficulties with high-dimensional data and clusters of very different densities. So, partitioning-based DBSCAN algorithm (PDBSCAN) was proposed to solve these problems. But PDBSCAN will get poor result when the density of data is non-uniform. Meanwhile, to some extent. DBSCAN and PDBSCAN are both sensitive to the initial parameters. In this paper, we propose a new hybrid algorithm based on PDBSCAN. We use modified ant clustering algorithm (ACA) and design a new partitioning algorithm based on 'point density' (PD) in data preprocessing phase. We name the new hybrid algorithm PACA-DBSCAN. The performance of PACA-DBSCAN is compared with DBSCAN and PDBSCAN on five data sets. Experimental results indicate the superiority of PACA-DBSCAN algorithm. (C) 2011 Elsevier Ltd. All rights reserved.", "publicationTitle": "Expert Systems with Applications", "publisher": "", "place": "", "date": "AUG 2011", "volume": "38", "issue": "8", "section": "", "partNumber": "", "partTitle": "", "pages": "9373-9381", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "Expert Syst. Appl.", "DOI": "10.1016/j.eswa.2011.01.135", "citationKey": "", "url": "http://dx.doi.org/10.1016/j.eswa.2011.01.135", "accessDate": "", "PMID": "", "PMCID": "", "ISSN": "0957-4174", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "English", "libraryCatalog": "ISI Web of Knowledge", "callNumber": "", "rights": "", "extra": "WOS:000290237500039", "tags": [ { "tag": "Ant clustering algorithm" }, { "tag": "Clustering" }, { "tag": "Density-based clustering" }, { "tag": "algorithm" }, { "tag": "dbscan" }, { "tag": "k-harmonic means" }, { "tag": "pdbscan" } ], "collections": [], "relations": {}, "dateAdded": "2014-02-11T22:48:16Z", "dateModified": "2014-02-11T23:03:37Z" } } ]