{"id":71,"date":"2024-12-09T10:31:52","date_gmt":"2024-12-09T15:31:52","guid":{"rendered":"https:\/\/www.golive.clarku.edu\/faculty\/profiles\/robert-pontius\/"},"modified":"2026-04-25T18:07:41","modified_gmt":"2026-04-25T22:07:41","slug":"robert-pontius","status":"publish","type":"cu_faculty","link":"https:\/\/www.clarku.edu\/faculty\/profiles\/robert-pontius\/","title":{"rendered":"Robert Pontius"},"content":{"rendered":"

Robert Gilmore Pontius Jr<\/a> is a Professor in Clark University\u2019s Graduate School of Geography<\/a>. Dr. Pontius specializes in Geographic Information Science (GIS) with particular expertise in statistics, simulation modeling, and land change science. He derives quantitative methods that are in the GIS software TerrSet<\/em><\/a>, which Clark Center for Geospatial Analytics produces. Pontius works to improve the methods for carbon offset projects to reduce greenhouse gas emissions from deforestation and forest degradation. The United States National Science Foundation continues to fund his research through more than two decades via the Plum Island Ecosystems<\/a> site of the Long-Term Ecological Research network. The National Aeronautics and Space Administration also funds his research in the Brazilian Cerrado Biome<\/a> via the Land-Cover and Land-Use Change program. Pontius is a member of the Scientific Advisory Board of MapBiomas<\/a> and a Fulbright Scholar of Brazil. He has summarized his novel quantitative methods in his 2022 book entitled Metrics That Make a Difference: How to Analyze Change and Error<\/a>. His publications<\/a> have received more than 20 thousand citations<\/a>. He joined Clark University as an Assistant Professor in 1998 and has been a full professor in Clark University’s Graduate School of Geography since 2011. His two most popular courses are GIS & Land Change Models and GIS & Map Comparison. Students in his courses have made tutorial videos<\/a> that international audiences have found helpful to understand his quantitative methods. Pontius earned a Bachelor of Science in Mathematics at University of Pittsburgh in 1984, a Masters of Applied Statistics at The Ohio State University in 1989, and a Doctorate in Environmental Science at the State University of New York\u2019s College of Environmental Science and Forestry in 1994. Pontius has also had a career as a professional juggler, winning the People\u2019s Choice Award at the International Jugglers Association, as his stage character Doctor Stardust<\/a>.<\/p>\n","protected":false},"author":0,"featured_media":1948,"parent":0,"template":"","meta":{"cu_faculty_f180_userid":"C11301315","cu_faculty_first_name":"Robert","cu_faculty_last_name":"Pontius","cu_faculty_employment_status":"Full Time","cu_faculty_rank":"Professor","cu_faculty_position":"Professor","cu_faculty_phone":"","cu_faculty_email":"rpontius@clarku.edu","cu_faculty_location":"","cu_faculty_about":"

Robert Gilmore Pontius Jr<\/a> is a Professor in Clark University\u2019s Graduate School of Geography<\/a>. Dr. Pontius specializes in Geographic Information Science (GIS) with particular expertise in statistics, simulation modeling, and land change science. He derives quantitative methods that are in the GIS software TerrSet<\/em><\/a>, which Clark Center for Geospatial Analytics produces. Pontius works to improve the methods for carbon offset projects to reduce greenhouse gas emissions from deforestation and forest degradation. The United States National Science Foundation continues to fund his research through more than two decades via the Plum Island Ecosystems<\/a> site of the Long-Term Ecological Research network. The National Aeronautics and Space Administration also funds his research in the Brazilian Cerrado Biome<\/a> via the Land-Cover and Land-Use Change program. Pontius is a member of the Scientific Advisory Board of MapBiomas<\/a> and a Fulbright Scholar of Brazil. He has summarized his novel quantitative methods in his 2022 book entitled Metrics That Make a Difference: How to Analyze Change and Error<\/a>. His publications<\/a> have received more than 20 thousand citations<\/a>. He joined Clark University as an Assistant Professor in 1998 and has been a full professor in Clark University's Graduate School of Geography since 2011. His two most popular courses are GIS & Land Change Models and GIS & Map Comparison. Students in his courses have made tutorial videos<\/a> that international audiences have found helpful to understand his quantitative methods. Pontius earned a Bachelor of Science in Mathematics at University of Pittsburgh in 1984, a Masters of Applied Statistics at The Ohio State University in 1989, and a Doctorate in Environmental Science at the State University of New York\u2019s College of Environmental Science and Forestry in 1994. Pontius has also had a career as a professional juggler, winning the People\u2019s Choice Award at the International Jugglers Association, as his stage character Doctor Stardust<\/a>.<\/p>","cu_faculty_degrees":"Ph.D. in Environmental Science,<\/span> State University of New York \/ College of Environmental Science and Forestry, 1994\nMasters in Applied Statistics,<\/span> The Ohio State University, 1989\nB.S. in Mathematics and Economics,<\/span> University of Pittsburgh, 1984","cu_faculty_cv":"https:\/\/faculty180.interfolio.com\/public\/download.php?key=SDRwNCtxSUpsamxBQ213WS9ucHFuNnMwT0hzQU11b2RPQkJ2cWc3amxyUmNRdVVXTkF4MU10Q1FvdGN3MEZPOXNVQUlMWW1pZU1Bek9VaktrU2ZuVUVWeHJQUXBHam5KMHY5OFBzQlZkckswcXo3WFo3R21zaE5OeW9vU29aQXQ%3D","cu_faculty_links":"{\"Professional Website URL\":\"https:\\\/\\\/wordpress.clarku.edu\\\/rpontius\\\/\",\"ORCID ID\":\"https:\\\/\\\/orcid.org\\\/0000-0001-7287-5875\",\"Google Scholar\":\"https:\\\/\\\/scholar.google.com\\\/citations?user=Lfmhbd8AAAAJ&hl=en\",\"Research Gate\":\"https:\\\/\\\/www.researchgate.net\\\/profile\\\/Robert-Pontius?ev=hdr_xprf\",\"Other\":\"https:\\\/\\\/www.linkedin.com\\\/in\\\/robert-gilmore-pontius-jr-8b213113\\\/\"}","cu_faculty_scholarly_interests":"Geographic Information Science, Land Change Science, Simulation Modeling, Statistics","cu_faculty_scholarly_works":"[{\"activityid\":12413,\"fields\":{\"Type\":\"Articles in Refereed Journals\",\"Title\":\"<p><span style="font-size:12pt;">Dyna<\\\/span><span style="font-size:12pt;">mic<\\\/span><span style="font-size:12pt;">PATCH: Method and Software for Spatially Explicit Dynamic Patch Transition Characterization<\\\/span><\\\/p>\",\"Journal Title\":\"Landscape Ecology\",\"Series Title\":\"\",\"Month \\\/ Season\":\"July\",\"Year\":2025,\"Publisher\":\"\",\"Publisher City and State\":\"\",\"Publisher Country\":\"\",\"Volume\":\"40\",\"Issue Number \\\/ Edition\":\"132\",\"Page Number(s) or Number of Pages\":\"\",\"ISSN\":\"\",\"DOI\":\"\",\"CoAuthor\":null,\"URL\":\"https:\\\/\\\/link.springer.com\\\/article\\\/10.1007\\\/s10980-025-02120-1\",\"Description\":\"<p>Context<\\\/p>\\n<p>Decades of research have used spatial pattern metrics at time points, such as those in FRAGSTATS, to measure the net change of landscape configuration and composition during time intervals. However, metrics at time points fail to quantify a time interval\\u2019s gross change, which can be substantially larger than net change between two time points. The field of landscape ecology has lacked a conceptual framework and accompanying software to characterize patterns of gross change during time intervals in terms of patches that are spatially explicit with a typology that is mutually exclusive and collectively exhaustive.<\\\/p>\\n<p>Objectives<\\\/p>\\n<p>We present a new method and software named <strong>Dynamic<\\\/strong> <strong>PA<\\\/strong>tch <strong>T<\\\/strong>ransition <strong>CH<\\\/strong>aracterization in explicit space (DynamicPATCH). DynamicPATCH reads a time series of raster maps of a Boolean variable to map, characterize, and quantify gross patch dynamics in ways that existing metrics miss. We design the method to compare time intervals that vary in their durations.<\\\/p>\\n<p>Methods<\\\/p>\\n<p>Our method identifies eight patch-based transition types that are mutually exclusive and collectively exhaustive: Disappearing, Appearing, Splitting, Merging, Perforating, Filling, Contracting, and Expanding. The method quantifies each type\\u2019s gross gain and gross loss in area. The method also shows how each type contributes to the gross increase and gross decrease in the number of patches during each time interval. We illustrate our method with a simple example as well as a case study on the dynamics of Marshes and Ponds in the Plum Island Ecosystems site of the National Science Foundation\\u2019s Long Term Ecological Research network (PIE-LTER) across the years 1938, 1971, and 2013.<\\\/p>\\n<p>Results<\\\/p>\\n<p>Our method reveals dramatic gross changes in the size and number of patches of Pond and Marsh at PIE-LTER, which traditional methods that report only net changes fail to capture. The transition types Perforating and Contracting account for 89% of gross loss in Marsh size across the temporal extent. Annual gross increase in the number of Marsh patches during the second time interval is 3.8 times greater than during the first time interval, with 40% of gross increase contributed by Appearing and 60% by Splitting. Gross gain of Pond is more than twice the size of gross loss of Pond, with Merging accounting for 47% of gross gain, followed by Appearing and Expanding. Appearing and Disappearing account for most gross changes in the number of Ponds during the temporal extent, which is ten times greater than the net change in the number of Ponds.<\\\/p>\\n<p>Conclusions<\\\/p>\\n<p>DynamicPATCH offers a new operationalized approach to characterize the spatial-temporal patterns of patch dynamics. DynamicPATCH can be applied to various landscapes to pursue important research agendas in landscape ecology, such as landscape fragmentation, habitat connectivity, process-pattern relationships, and more. Users can obtain the open-source Python package at https:\\\/\\\/github.com\\\/zay1996\\\/DynamicPATCH.<\\\/p>\",\"Include description in output citation\":1,\"Origin\":\"Manual\"},\"facultyid\":\"C11301315\",\"status\":[{\"id\":12413,\"status\":\"Completed\\\/Published\",\"term\":\"Summer\",\"year\":2025,\"termid\":\"2024\\\/05\",\"listingorder\":6,\"completionorder\":6},{\"id\":12413,\"status\":\"Revise & Resubmit\",\"term\":\"Spring\",\"year\":2025,\"termid\":\"2024\\\/03\",\"listingorder\":3,\"completionorder\":3}],\"userid\":\"C11301315\",\"attachments\":[{\"attachmentid\":13565,\"mimetype\":\"custom\\\/url\",\"filename\":\"https:\\\/\\\/link.springer.com\\\/article\\\/10.1007\\\/s10980-025-02120-1\",\"filesize\":452448,\"downloadurl\":\"https:\\\/\\\/faculty180.interfolio.com\\\/public\\\/download.php?key=SDRwNCtxSUpsamxBQ213WS9ucHFuNnMwT0hzQU11b2RPQkJ2cWc3amxyUmNRdVVXTkF4MU10Q1FvdGN3MEZPOWxLQytxSXV3YUNZTmNTR0tYR29Id1crNS9XV2tLdDlTcUptNkhsdDRNWDA4eDZIUktad3dyRHoxR2FEb1drRnFTQVpwY3JnOEZMekpOeHdiWFhlSFcvaEpKamtKRjFLR1h0ZVlINjBFdzNrRjRiZ2hMb2NDcG55eFRmbDQzbHRx\"}],\"coauthors_list\":[\"Aiyin Zhang\",\"Robert G. Pontius Jr.\",\"Thomas Mumuni Bilintoh\",\"Florencia Sangermano\",\"John Rogan\"],\"sort_date\":\"2025-7-01\"},{\"activityid\":12415,\"fields\":{\"Type\":\"Articles in Refereed Journals\",\"Title\":\"<p><span style="font-size:11pt;">Accounting for alternation in temporal quality analysis in MapBiomas Brazil<\\\/span><\\\/p>\",\"Journal Title\":\"International Journal of Digital Earth\",\"Series Title\":\"\",\"Month \\\/ Season\":\"July\",\"Year\":2025,\"Publisher\":\"\",\"Publisher City and State\":\"\",\"Publisher Country\":\"\",\"Volume\":\"18\",\"Issue Number \\\/ Edition\":\"1\",\"Page Number(s) or Number of Pages\":\"\",\"ISSN\":\"\",\"DOI\":\"\",\"CoAuthor\":null,\"URL\":\"https:\\\/\\\/www.tandfonline.com\\\/doi\\\/full\\\/10.1080\\\/17538947.2025.2528604\",\"Description\":\"<p><span>Land use and land cover maps are an important resource for understanding interactions between humans and their environment across space and time with current mapping efforts often spanning upwards of 20 years. We present here a new method for a robust assessment of land cover transitions over time and apply this methodology to the yearly MapBiomas land use land cover maps of Brazil spanning 1985 to 2022. Based on a reference sample of 85,152 points, we find MapBiomas to have limited accuracy as an indicator of yearly land use change, but consistent over the full mapping period. Alternation, a newly defined error component, derives from where one location experiences multiple changes during the time series. It is the primary reason for differences in estimates of annual change and is 4.6 times more frequent in the MapBiomas product than reference data. Over the full time period analyzed, the annual land use change area decreases for both the MapBiomas classification and reference data. The total land use changes detected over the 37 year study period are 232 million hectares and 252 million hectares for the reference data and MapBiomas product, or 27% and 29% of the Brazilian territory respectively.<\\\/span><\\\/p>\",\"Include description in output citation\":1,\"Origin\":\"Manual\"},\"facultyid\":\"C11301315\",\"status\":[{\"id\":12415,\"status\":\"Revise & Resubmit\",\"term\":\"Summer\",\"year\":2025,\"termid\":\"2024\\\/05\",\"listingorder\":3,\"completionorder\":3},{\"id\":12415,\"status\":\"Completed\\\/Published\",\"term\":\"Summer\",\"year\":2025,\"termid\":\"2024\\\/05\",\"listingorder\":6,\"completionorder\":6}],\"userid\":\"C11301315\",\"attachments\":[{\"attachmentid\":13567,\"mimetype\":\"custom\\\/url\",\"filename\":\"https:\\\/\\\/www.tandfonline.com\\\/doi\\\/full\\\/10.1080\\\/17538947.2025.2528604\",\"filesize\":452448,\"downloadurl\":\"https:\\\/\\\/faculty180.interfolio.com\\\/public\\\/download.php?key=SDRwNCtxSUpsamxBQ213WS9ucHFuNnMwT0hzQU11b2RPQkJ2cWc3amxyUmNRdVVXTkF4MU10Q1FvdGN3MEZPOWxLQytxSXV3YUNZTmNTR0tYR29Id1crNS9XV2tLdDlTNGc1dno0aVlWMW9DQnNNcEtOSVQzM3hUcThpR2RQVlVHVHlaSiszQ0pWcGdhUmg1U3VvTU5Ibk1pVmFxT1hjc1FCOXZEWDhNRU5zZWx5NmhON0RrUG03TU1tV0ZLUnpFZkxGTitYamVXMm89\"}],\"coauthors_list\":[\"Ana Paula Matos\",\"Maria Hunter\",\"Robert G. Pontius Jr.\",\"Luis Baumann\",\"Leandro Parente\",\"Learte Guimar\\u00e3es Ferreira Jr\"],\"sort_date\":\"2025-7-01\"},{\"activityid\":12426,\"fields\":{\"Type\":\"Presentations\",\"Title of Presentation\":\"<span style="font-size:12pt;">The new method of Trajectory Analysis characterizes change in a time series of maps<\\\/span>\",\"Conference \\\/ Meeting Name\":\"Landscapes of Change: Dynamic Interactions between Nature and People\",\"Location of Conference \\\/ Meeting\":\"Raleigh, North Carolina\",\"Month \\\/ Season\":\"April\",\"Year\":2025,\"Sponsoring Organization\":\"International Association for Landscape Ecology\",\"CoAuthor\":null,\"URL\":\"https:\\\/\\\/www.ialena.org\\\/annual-meeting.html\",\"Description\":\"\",\"Include description in output citation\":0,\"Origin\":\"Manual\"},\"facultyid\":\"C11301315\",\"status\":[{\"id\":12426,\"status\":\"Completed\\\/Published\",\"term\":\"Spring\",\"year\":2024,\"termid\":\"2023\\\/03\",\"listingorder\":6,\"completionorder\":6}],\"userid\":\"C11301315\",\"attachments\":[],\"coauthors_list\":[\"Robert G. Pontius Jr.\"],\"sort_date\":\"2025-4-01\"},{\"activityid\":12427,\"fields\":{\"Type\":\"Presentations\",\"Title of Presentation\":\"Metrics That Make a Difference Workshop\",\"Conference \\\/ Meeting Name\":\"International Association for Landscape Ecology\",\"Location of Conference \\\/ Meeting\":\"Raleigh, North Carolina\",\"Month \\\/ Season\":\"April\",\"Year\":2025,\"Sponsoring Organization\":\"\",\"CoAuthor\":null,\"URL\":\"https:\\\/\\\/www.ialena.org\\\/annual-meeting.html\",\"Description\":\"\",\"Include description in output citation\":0,\"Origin\":\"Manual\"},\"facultyid\":\"C11301315\",\"status\":[{\"id\":12427,\"status\":\"Completed\\\/Published\",\"term\":\"Spring\",\"year\":2025,\"termid\":\"2024\\\/03\",\"listingorder\":6,\"completionorder\":6}],\"userid\":\"C11301315\",\"attachments\":[],\"coauthors_list\":[\"Robert G. Pontius Jr.\"],\"sort_date\":\"2025-4-01\"},{\"activityid\":12444,\"fields\":{\"Type\":\"Presentations\",\"Title of Presentation\":\"<span style="font-size:11pt;">The new method of Trajectory Analysis characterizes change in a time series of maps<\\\/span>\",\"Conference \\\/ Meeting Name\":\"International Association for Landscape Ecology\",\"Location of Conference \\\/ Meeting\":\"Raleigh, North Carolina\",\"Month \\\/ Season\":\"April\",\"Year\":2025,\"Sponsoring Organization\":\"\",\"CoAuthor\":null,\"URL\":\"\",\"Description\":\"\",\"Include description in output citation\":0,\"Origin\":\"Manual\"},\"facultyid\":\"C11301315\",\"status\":[{\"id\":12444,\"status\":\"Completed\\\/Published\",\"term\":\"Spring\",\"year\":2025,\"termid\":\"2024\\\/03\",\"listingorder\":6,\"completionorder\":6}],\"userid\":\"C11301315\",\"attachments\":[],\"coauthors_list\":[\"Robert G. Pontius Jr.\"],\"sort_date\":\"2025-4-01\"},{\"activityid\":12411,\"fields\":{\"Type\":\"Articles in Refereed Journals\",\"Title\":\"<span style="font-size:12pt;">A call to interpret disagreement components during classification assessment<\\\/span>\",\"Journal Title\":\"International Journal of Geographical Information Science\",\"Series Title\":\"\",\"Month \\\/ Season\":\"March\",\"Year\":2025,\"Publisher\":\"\",\"Publisher City and State\":\"\",\"Publisher Country\":\"\",\"Volume\":\"\",\"Issue Number \\\/ Edition\":\"\",\"Page Number(s) or Number of Pages\":\"18\",\"ISSN\":\"\",\"DOI\":\"10.1080\\\/13658816.2025.2469830\",\"CoAuthor\":null,\"URL\":\"https:\\\/\\\/www.tandfonline.com\\\/doi\\\/full\\\/10.1080\\\/13658816.2025.2469830\",\"Description\":\"This foresight manuscript proposes several ideas concerning how to conduct insightful classification assessment. Authors should report disagreement components that relate to the research question, without anointing results as acceptable or good. This manuscript reviews the citations of the 2011 manuscript entitled \\u2018Death to Kappa: Birth of Quantity Disagreement and Allocation Disagreement for Accuracy Assessment\\u2019, which gave two recommendations: (1) do not use Kappa and (2) use disagreement components. We analyzed 200 articles that cited the Death to Kappa manuscript. A quarter of the articles followed both recommendations, another quarter fol\\u00ad lowed only the first recommendation, another quarter followed only the second recommendation, and the last quarter followed neither recommendation. The attempt to replace Kappa with disagreement components has been partially effective, while Kappa continues to haunt several professions. We discuss misguided uses of Percent Correct and Kappa in Remote Sensing and Land Change Modeling. The concepts are general and thus relate to additional fields. Authors frequently use arbitrary thresholds of metrics to claim that results are acceptable. However, the notion that results can be acceptable or not is inherently unscientific. Scientists must use a metric that addresses a clear research question in which the scien\\u00adtists have no vested interest in the results.\",\"Include description in output citation\":1,\"Origin\":\"Manual\"},\"facultyid\":\"C11301315\",\"status\":[{\"id\":12411,\"status\":\"Completed\\\/Published\",\"term\":\"Spring\",\"year\":2025,\"termid\":\"2024\\\/03\",\"listingorder\":6,\"completionorder\":6}],\"userid\":\"C11301315\",\"attachments\":[{\"attachmentid\":10926,\"mimetype\":\"application\\\/pdf\",\"filename\":\"Pontius Jr et al. - 2025 - A call to interpret disagreement components during.pdf\",\"filesize\":1929516,\"downloadurl\":\"https:\\\/\\\/faculty180.interfolio.com\\\/public\\\/download.php?key=SDRwNCtxSUpsamxBQ213WS9ucHFuNnMwT0hzQU11b2RPQkJ2cWc3amxyUmNRdVVXTkF4MU10Q1FvdGN3MEZPOUNHVURvZmMzUmM4MitaQnZwaFYrVTFtMTY0N21pRUZCVHFUb2FUaEY4d0RoYktONFd4RVdtT01DUkZvUVBnSnlTdHZUYnk0dmNDRC85ZFFaaGloTUlzVW5iQnVIU0s4UDNUclZCc0dQOGV2RjFIRnB2MHhvS2toSC81NnhUN21ZSTJNYy9MWG80cTg9\"}],\"coauthors_list\":[\"Robert G. Pontius Jr.\",\"Thomas Francis\",\"Marco Millones\"],\"sort_date\":\"2025-3-01\"},{\"activityid\":12412,\"fields\":{\"Type\":\"Articles in Refereed Journals\",\"Title\":\"<span style="font-size:12pt;">Best Practices for Applying and Interpreting the Total Operating Characteristic<\\\/span>\",\"Journal Title\":\"ISPRS International Journal of Geo-Information\",\"Series Title\":\"\",\"Month \\\/ Season\":\"March\",\"Year\":2025,\"Publisher\":\"\",\"Publisher City and State\":\"\",\"Publisher Country\":\"\",\"Volume\":\"14\",\"Issue Number \\\/ Edition\":\"\",\"Page Number(s) or Number of Pages\":\"17\",\"ISSN\":\"\",\"DOI\":\"10.3390\\\/ijgi14040134\",\"CoAuthor\":null,\"URL\":\"https:\\\/\\\/www.mdpi.com\\\/2220-9964\\\/14\\\/4\\\/134\",\"Description\":\"The Total Operating Characteristic (TOC) is an improvement on the quantitative method called the Relative Operating Characteristic (ROC), both of which plot the association between a binary variable and a rank variable. TOC curves reveal the sizes of the four entries in the confusion matrix at each threshold, which make TOC curves more easily interpretable than ROC curves. The TOC has become popular, especially to assess the fit of simulation models to predict land change. However, the literature has shown variation in how authors apply and interpret the TOC, creating some misleading conclusions. Our manuscript lists best practices when applying and interpreting the TOC to help scientists learn from TOC curves. An example illustrates these practices by applying the TOC to measure the ability to predict the gain of crop in Western Bahia, Brazil. The application compares four ways to design the rank variable based on the distance to either pixels or patches of either the presence or change of crop. The results show that the gain of crop during the validation time interval is more strongly associated with the distance to patches rather than pixels of crop. The Discussion Section reveals that if authors show the TOC curves, then readers can interpret the results in ways that the authors might have missed. The Conclusion encourages scientists to follow best practices to learn the wealth of information that the TOC reveals.\",\"Include description in output citation\":1,\"Origin\":\"Manual\"},\"facultyid\":\"C11301315\",\"status\":[{\"id\":12412,\"status\":\"Completed\\\/Published\",\"term\":\"Spring\",\"year\":2025,\"termid\":\"2024\\\/03\",\"listingorder\":6,\"completionorder\":6}],\"userid\":\"C11301315\",\"attachments\":[{\"attachmentid\":10927,\"mimetype\":\"application\\\/pdf\",\"filename\":\"Honnef and Pontius Jr - 2025 - Best Practices for Applying and Interpreting the T.pdf\",\"filesize\":4503809,\"downloadurl\":\"https:\\\/\\\/faculty180.interfolio.com\\\/public\\\/download.php?key=SDRwNCtxSUpsamxBQ213WS9ucHFuNnMwT0hzQU11b2RPQkJ2cWc3amxyUmNRdVVXTkF4MU10Q1FvdGN3MEZPOUNHVURvZmMzUmMvdENrL1RmZStsM3gvMHR4dVJGRWRwb1VlZENKdXVwNEErUDkvcDQ0THloRUIyT2N0NDgwNC9OY3lUQWxhcDJGRk8wVmZ0S0NVNUZhRzV1enYxZ0tUdGJwVUF4eXk2K0JBR00xYWovTkVOSFpyVnJXWVJna0FpYkdaS0ZjWjZTaVk9\"}],\"coauthors_list\":[\"Tanner Honnef\",\"Robert G. Pontius Jr.\"],\"sort_date\":\"2025-3-01\"},{\"activityid\":12414,\"fields\":{\"Type\":\"Articles in Refereed Journals\",\"Title\":\"<p><span style="font-size:12pt;">A GIS-based method to analyze trajectories of gross changes through multiple temporal resolutions with an application to Brazilian soybean cultivation<\\\/span><\\\/p>\",\"Journal Title\":\"Transactions in GIS\",\"Series Title\":\"\",\"Month \\\/ Season\":\"December\",\"Year\":2025,\"Publisher\":\"\",\"Publisher City and State\":\"\",\"Publisher Country\":\"\",\"Volume\":\"29\",\"Issue Number \\\/ Edition\":\"8\",\"Page Number(s) or Number of Pages\":\"\",\"ISSN\":\"\",\"DOI\":\"\",\"CoAuthor\":null,\"URL\":\"https:\\\/\\\/onlinelibrary.wiley.com\\\/doi\\\/10.1111\\\/tgis.70169\",\"Description\":\"<p>Our manuscript presents Trajectory Analysis, which is a novel GIS-based method to characterize patterns of gross changes during a time series of raster maps. Trajectory Analysis produces maps and bar graphs that help to visualize hotspots and hot moments of change. The method computes up to eight trajectories and three components of change: Quantity, Exchange, and Alternation. Quantity is the non-spatial absolute net change between the two time points that bound the temporal extent. Exchange is the additional spatial gross change between those two time points. Alternation is the additional temporal gross change that reverses itself during the sequence of time intervals, such as gross gain followed by gross loss at the same location. Alternation shrinks through coarser temporal resolutions to reflect the durations between gross gains and gross losses. An application illustrates the concepts by analyzing soybean cultivation during annual time intervals from 1989 to 2021 in Western Bahia, Brazil. The largest component of change at temporal resolutions of fewer than four years for our case study is Alternation, which is a pattern that popular methods miss. Readers may apply the <em>timeseriesTrajectories <\\\/em>R package available for free at <a href="https:\\\/\\\/github.com\\\/bilintoh\\\/timeseriesTrajectories">https:\\\/\\\/github.com\\\/bilintoh\\\/timeseriesTrajectories<\\\/a>.<\\\/p>\",\"Include description in output citation\":1,\"Origin\":\"Manual\"},\"facultyid\":\"C11301315\",\"status\":[{\"id\":12414,\"status\":\"Completed\\\/Published\",\"term\":\"Fall\",\"year\":2025,\"termid\":\"2025\\\/01\",\"listingorder\":6,\"completionorder\":6},{\"id\":12414,\"status\":\"Submitted\",\"term\":\"Summer\",\"year\":2025,\"termid\":\"2024\\\/05\",\"listingorder\":2,\"completionorder\":2}],\"userid\":\"C11301315\",\"attachments\":[{\"attachmentid\":13566,\"mimetype\":\"custom\\\/url\",\"filename\":\"https:\\\/\\\/onlinelibrary.wiley.com\\\/doi\\\/10.1111\\\/tgis.70169\",\"filesize\":452448,\"downloadurl\":\"https:\\\/\\\/faculty180.interfolio.com\\\/public\\\/download.php?key=SDRwNCtxSUpsamxBQ213WS9ucHFuNnMwT0hzQU11b2RPQkJ2cWc3amxyUmNRdVVXTkF4MU10Q1FvdGN3MEZPOWxLQytxSXV3YUNZTmNTR0tYR29Id1crNS9XV2tLdDlTa1dnS2QzSVN5SEkyN1BqYnN0bWR0U21NLzNLN3RFMm9pSHdkTVJkOUc3THJPNGxTMW1BNURkbVg3R1lhQUZPYmlQemFDTjNZVnRIdDBYbS9wK3lBZ0E9PQ%3D%3D\"}],\"coauthors_list\":[\"Thomas Mumuni Bilintoh\",\"Robert G. Pontius Jr.\",\"Gustavo Oliveira\",\"Julia Shimbo\"],\"sort_date\":\"2025-12-01\"},{\"activityid\":12416,\"fields\":{\"Type\":\"Articles in Refereed Journals\",\"Title\":\"<p><span style="font-size:12pt;">A GIS method to summarize changes among classes during a time series with an application to land cover in western Bahia, Brazil<span style="color:#000000;"><\\\/span><\\\/span><\\\/p>\",\"Journal Title\":\"Transactions in GIS\",\"Series Title\":\"\",\"Month \\\/ Season\":\"October\",\"Year\":2025,\"Publisher\":\"\",\"Publisher City and State\":\"\",\"Publisher Country\":\"\",\"Volume\":\"29\",\"Issue Number \\\/ Edition\":\"7\",\"Page Number(s) or Number of Pages\":\"\",\"ISSN\":\"\",\"DOI\":\"\",\"CoAuthor\":null,\"URL\":\"https:\\\/\\\/onlinelibrary.wiley.com\\\/doi\\\/10.1111\\\/tgis.70136\",\"Description\":\"<p>Petabytes of raster maps exist that show numerous classes across a series of many time points. Scientists need new methods to summarize the temporal changes so scientists can understand broad patterns and have guidance for how to look deeper into particular places, trajectories, classes, and time intervals. Our manuscript addresses this need by presenting an innovative method that summarizes the temporal changes in terms of a map of four trajectories and bar charts of five components: quantity, allocation exchange, allocation shift, alternation exchange, and alternation shift. An application illustrates the method using 16 land cover classes at 7 time points from 1990 to 2020 in western Bahia, Brazil. Results reveal a substantial component of alternation shift, which exists when pixels transition from savanna to temporary crops to soybean. Readers may apply the method using the app available for free at <a href="https:\\\/\\\/github.com\\\/antoniovfonseca\\\/summarize-change-components">https:\\\/\\\/github.com\\\/antoniovfonseca\\\/summarize-change-components<\\\/a>.<\\\/p>\",\"Include description in output citation\":1,\"Origin\":\"Manual\"},\"facultyid\":\"C11301315\",\"status\":[{\"id\":12416,\"status\":\"Completed\\\/Published\",\"term\":\"Fall\",\"year\":2025,\"termid\":\"2025\\\/01\",\"listingorder\":6,\"completionorder\":6},{\"id\":12416,\"status\":\"Submitted\",\"term\":\"Spring\",\"year\":2025,\"termid\":\"2024\\\/03\",\"listingorder\":2,\"completionorder\":2}],\"userid\":\"C11301315\",\"attachments\":[{\"attachmentid\":13568,\"mimetype\":\"custom\\\/url\",\"filename\":\"https:\\\/\\\/onlinelibrary.wiley.com\\\/doi\\\/10.1111\\\/tgis.70136\",\"filesize\":452448,\"downloadurl\":\"https:\\\/\\\/faculty180.interfolio.com\\\/public\\\/download.php?key=SDRwNCtxSUpsamxBQ213WS9ucHFuNnMwT0hzQU11b2RPQkJ2cWc3amxyUmNRdVVXTkF4MU10Q1FvdGN3MEZPOWxLQytxSXV3YUNZTmNTR0tYR29Id1crNS9XV2tLdDlTa1dnS2QzSVN5SEkyN1BqYnN0bWR0U21NLzNLN3RFMm9pSHdkTVJkOUc3THJPNGxTMW1BNURkbVg3R1lhQUZPYmlQemFDTjNZVnRGZS9FRk0waGlaVGc9PQ%3D%3D\"}],\"coauthors_list\":[\"Robert G. Pontius Jr.\",\"Antonio Galvao da Fonseca\"],\"sort_date\":\"2025-10-01\"},{\"activityid\":12417,\"fields\":{\"Type\":\"Articles in Refereed Journals\",\"Title\":\"<p>The Bayesian Operating Characteristic Curve for Feature Analysis applied to Urban Land Cover Change<\\\/p>\",\"Journal Title\":\"Ecological Modelling and Software\",\"Series Title\":\"\",\"Month \\\/ Season\":\"October\",\"Year\":2025,\"Publisher\":\"\",\"Publisher City and State\":\"\",\"Publisher Country\":\"\",\"Volume\":\"194\",\"Issue Number \\\/ Edition\":\"106688\",\"Page Number(s) or Number of Pages\":\"\",\"ISSN\":\"\",\"DOI\":\"\",\"CoAuthor\":null,\"URL\":\"https:\\\/\\\/www.sciencedirect.com\\\/science\\\/article\\\/abs\\\/pii\\\/S136481522500372X?via%3Dihub\",\"Description\":\"<p><span>The <em>Total Operating Characteristic<\\\/em> curve (TOC) is a visual tool used to assess the performance of binary classifiers, introduced as an improvement over the <em>Receiver Operating Characteristic<\\\/em> (ROC) curve. The TOC provides a function that maps the number of <em>hits<\\\/em> plus <em>false alarms<\\\/em> (True Positives and False Positives) to <em>hits<\\\/em> (True Positives). Despite its broad adoption in model evaluation, especially for land use change studies, the TOC\\u2019s mathematical properties for explaining the probabilistic performance of classifiers and data distributions remain underexplored. To fill this gap, this article introduces the Bayesian Operating Characteristic (BOC) curve, a novel explanatory framework derived from the application of Bayes\\u2019 theorem and numerical analysis on the TOC curve. Such a framework establishes the computation of cumulative and density distribution functions that describe and identify non-linear relations between a rank variable and its binary outcome, specifying the rank\\u2019s interval with the maximal positive or negative impact in the classification. The proposal is validated by the analysis and identification of urban land change drivers, revealing different non-linear probabilistic relationships between rank variables and the binary outcome.<\\\/span><\\\/p>\",\"Include description in output citation\":1,\"Origin\":\"Manual\"},\"facultyid\":\"C11301315\",\"status\":[{\"id\":12417,\"status\":\"Completed\\\/Published\",\"term\":\"Fall\",\"year\":2025,\"termid\":\"2025\\\/01\",\"listingorder\":6,\"completionorder\":6},{\"id\":12417,\"status\":\"Submitted\",\"term\":\"Spring\",\"year\":2025,\"termid\":\"2024\\\/03\",\"listingorder\":2,\"completionorder\":2}],\"userid\":\"C11301315\",\"attachments\":[{\"attachmentid\":13569,\"mimetype\":\"custom\\\/url\",\"filename\":\"https:\\\/\\\/www.sciencedirect.com\\\/science\\\/article\\\/abs\\\/pii\\\/S136481522500372X?via%3Dihub\",\"filesize\":452448,\"downloadurl\":\"https:\\\/\\\/faculty180.interfolio.com\\\/public\\\/download.php?key=SDRwNCtxSUpsamxBQ213WS9ucHFuNnMwT0hzQU11b2RPQkJ2cWc3amxyUmNRdVVXTkF4MU10Q1FvdGN3MEZPOWxLQytxSXV3YUNZTmNTR0tYR29Id1crNS9XV2tLdDlTNGc1dno0aVlWMW9FZVlzenNEV3dnTzdSbzNVSy8wcDRpSzJSQ1lFREJaZ2VNTFNuM0J2M2VoNHNCMGFZSlhEZ1hNSTQ4ZVVWVGZMUitobGZTZHAxUlFzemhtRjNYR3VHU3Y0Vjd4V041NmNiSlU4U3dFNFU1UXdMTDBxMG9IdG4%3D\"}],\"coauthors_list\":[\"Rodrigo Lopez-Farias\",\"S. Ivvan Valdez\",\"Carlos Lara-Alvarez\",\"Robert G. Pontius Jr.\"],\"sort_date\":\"2025-10-01\"},{\"activityid\":12410,\"fields\":{\"Type\":\"Articles in Refereed Journals\",\"Title\":\"<span style="font-size:12pt;">Analyzing the Losses and Gains of a Land Category: Insights from the Total Operating Characteristic<\\\/span>\",\"Journal Title\":\"Land\",\"Series Title\":\"\",\"Month \\\/ Season\":\"July\",\"Year\":2024,\"Publisher\":\"\",\"Publisher City and State\":\"\",\"Publisher Country\":\"\",\"Volume\":\"13\",\"Issue Number \\\/ Edition\":\"8\",\"Page Number(s) or Number of Pages\":\"11\",\"ISSN\":\"\",\"DOI\":\"10.3390\\\/land13081177\",\"CoAuthor\":null,\"URL\":\"https:\\\/\\\/www.mdpi.com\\\/2073-445X\\\/13\\\/8\\\/1177\",\"Description\":\"This manuscript provides guidance concerning how to use the Total Operating Characteristic (TOC) when (1) analyzing change through time, (2) ranking a categorical independent variable, and (3) constraining the extent for a gaining category. The illustrative variable is the marsh landcover category in the Plum Island Ecosystems of northeastern Massachusetts, USA. The data are an elevation map and maps showing the land categories of water, marsh, and upland in 1938, 1971, and 2013. There were losses and gains near the edge of the marsh between 1938 and 1972 and between 1972 and 2013. The TOC curves show that marsh gained most intensively at intermediate elevations during the first time interval and then had a weaker association with elevation during the second time interval. Marsh gains more intensively from water than from upland during both time intervals. The TOC curves also demonstrate that the marsh gains occurred where marsh was previously lost, a phenomenon called Alternation. Furthermore, eliminating far distances and extreme elevations from the spatial extent decreased the area under the curve (AUC) for distance and increased the AUC for elevation. We invite scientists to use the TOC because the TOC is easier to interpret and shows more information than the Relative Operative Characteristic.\",\"Include description in output citation\":1,\"Origin\":\"Manual\"},\"facultyid\":\"C11301315\",\"status\":[{\"id\":12410,\"status\":\"Completed\\\/Published\",\"term\":\"Summer\",\"year\":2024,\"termid\":\"2023\\\/05\",\"listingorder\":6,\"completionorder\":6}],\"userid\":\"C11301315\",\"attachments\":[{\"attachmentid\":10925,\"mimetype\":\"application\\\/pdf\",\"filename\":\"Bilintoh et al. - 2024 - Analyzing the Losses and Gains of a Land Category.pdf\",\"filesize\":7168508,\"downloadurl\":\"https:\\\/\\\/faculty180.interfolio.com\\\/public\\\/download.php?key=SDRwNCtxSUpsamxBQ213WS9ucHFuNnMwT0hzQU11b2RPQkJ2cWc3amxyUmNRdVVXTkF4MU10Q1FvdGN3MEZPOUNHVURvZmMzUmMvdFpSU3Uva2s2K3YrNHFxa1oyZ2poU2hvaGJlZnBxbkw5U1R3UVNCVHN6VnpNV0lpZkFmb25ZMklicWw0QnFIZEFONWl3MFBhZThXVzNkZDMzQitvUkZuUndidlBDY0pBeEYwRkQ5c0RjallTb1dURmZQMEpi\"}],\"coauthors_list\":[\"Thomas Mumuni Bilintoh\",\"Robert G. Pontius Jr.\",\"Zhen Liu\"],\"sort_date\":\"2024-7-01\"},{\"activityid\":12423,\"fields\":{\"Type\":\"Presentations\",\"Title of Presentation\":\"<span style="font-size:12pt;color:#323131;">How and why do some authors claim their data and models are good?<\\\/span>\",\"Conference \\\/ Meeting Name\":\"3rd MOPT Conference: Advanced Spatial Modeling and Analysis\",\"Location of Conference \\\/ Meeting\":\"Lisbon, Portugal\",\"Month \\\/ Season\":\"May\",\"Year\":2024,\"Sponsoring Organization\":\"Modelling, Urban, and Regional Planning (MOPT) research group\",\"CoAuthor\":null,\"URL\":\"\",\"Description\":\"\",\"Include description in output citation\":0,\"Origin\":\"Manual\"},\"facultyid\":\"C11301315\",\"status\":[{\"id\":12423,\"status\":\"Completed\\\/Published\",\"term\":\"Summer\",\"year\":2024,\"termid\":\"2023\\\/05\",\"listingorder\":6,\"completionorder\":6}],\"userid\":\"C11301315\",\"attachments\":[{\"attachmentid\":10933,\"mimetype\":\"application\\\/pdf\",\"filename\":\"Pontius2024Lisbon2.pdf\",\"filesize\":4846747,\"downloadurl\":\"https:\\\/\\\/faculty180.interfolio.com\\\/public\\\/download.php?key=SDRwNCtxSUpsamxBQ213WS9ucHFuNnMwT0hzQU11b2RPQkJ2cWc3amxyUmNRdVVXTkF4MU10Q1FvdGN3MEZPOUNHVURvZmMzUmMva3JIN2tFNlJZTU1WMEdiekhVR0hRaytrWWhBcXhROTVxN2FEbnV4Yjd3Zz09\"}],\"coauthors_list\":[\"Robert G. Pontius Jr.\"],\"sort_date\":\"2024-5-01\"},{\"activityid\":12425,\"fields\":{\"Type\":\"Presentations\",\"Title of Presentation\":\"<span style="font-size:12pt;">Metrics That Make a Difference Workshop<\\\/span>\",\"Conference \\\/ Meeting Name\":\"5th Open Science Meeting\",\"Location of Conference \\\/ Meeting\":\"Oaxaca, Mexico\",\"Month \\\/ Season\":\"November\",\"Year\":2024,\"Sponsoring Organization\":\"Global Land Programme\",\"CoAuthor\":null,\"URL\":\"\",\"Description\":\"\",\"Include description in output citation\":0,\"Origin\":\"Manual\"},\"facultyid\":\"C11301315\",\"status\":[{\"id\":12425,\"status\":\"Completed\\\/Published\",\"term\":\"Fall\",\"year\":2024,\"termid\":\"2024\\\/01\",\"listingorder\":6,\"completionorder\":6}],\"userid\":\"C11301315\",\"attachments\":[],\"coauthors_list\":[\"Robert G. Pontius Jr.\"],\"sort_date\":\"2024-11-01\"},{\"activityid\":12409,\"fields\":{\"Type\":\"Articles in Refereed Journals\",\"Title\":\"<span style="font-size:12pt;">Methods to compare sites concerning a category\\u2019s change during various time intervals<\\\/span>\",\"Journal Title\":\"GIScience and Remote Sensing\",\"Series Title\":\"\",\"Month \\\/ Season\":\"October\",\"Year\":2024,\"Publisher\":\"\",\"Publisher City and State\":\"\",\"Publisher Country\":\"\",\"Volume\":\"61\",\"Issue Number \\\/ Edition\":\"1\",\"Page Number(s) or Number of Pages\":\"14\",\"ISSN\":\"\",\"DOI\":\"10.1080\\\/15481603.2024.2409484\",\"CoAuthor\":null,\"URL\":\"https:\\\/\\\/www.tandfonline.com\\\/doi\\\/full\\\/10.1080\\\/15481603.2024.2409484\",\"Description\":\"This paper presents new methods to analyze a category\\u2019s change through a time series of maps, even when the time intervals have inconsistent durations. The methods include an option to facilitate comparison among sites by expressing results as an annual percentage of each site\\u2019s unified size. A site\\u2019s unified size is the union of where the category exists at any of the site\\u2019s time points. The methods also specify gross losses, gross gains, eight trajectories, and three compo\\u00ad nents: Quantity, Exchange, and Alternation. The illustrative application compares maps of the marsh category for three Long-Term Ecological Research sites: Plum Island Ecosystems (PIE), Georgia Coastal Ecosystems (GCE), and the Virginia Coast Reserve (VCR). The application analyzes marsh\\u2019s changes during two time intervals that have unequal durations within each site\\u2019s distinct temporal extent. Results show that PIE has the fastest change during each site\\u2019s temporal extent. Gross change is more than double the quantity change for all sites. Exchange accounts for most of the change in GCE, while Alternation accounts for most of the change in PIE and VCR. The methods provide more information than popular methods that quantify annual net change. Our timeseriesTrajectories R package performs the analysis and is available for free at https:\\\/\\\/github. com\\\/bilintoh\\\/timeseriesTrajectories.\",\"Include description in output citation\":1,\"Origin\":\"Manual\"},\"facultyid\":\"C11301315\",\"status\":[{\"id\":12409,\"status\":\"Completed\\\/Published\",\"term\":\"Fall\",\"year\":2024,\"termid\":\"2024\\\/01\",\"listingorder\":6,\"completionorder\":6}],\"userid\":\"C11301315\",\"attachments\":[{\"attachmentid\":10924,\"mimetype\":\"application\\\/pdf\",\"filename\":\"Bilintoh et al. - 2024 - Methods to compare sites concerning a category\\u2019s c.pdf\",\"filesize\":8716037,\"downloadurl\":\"https:\\\/\\\/faculty180.interfolio.com\\\/public\\\/download.php?key=SDRwNCtxSUpsamxBQ213WS9ucHFuNnMwT0hzQU11b2RPQkJ2cWc3amxyUmNRdVVXTkF4MU10Q1FvdGN3MEZPOUNHVURvZmMzUmM5VXZVdTBUdk5EOGYrNHFxa1oyZ2poU2hvaGJlZnBxbkw5U1R3UVNCVHN6VmxNMUdwN2x3eWkrTGwxNk95ZG9saWg0QkZoT05DUGdIbkpNK0hlbGF0b1haS2xOVlFLZTQzNHBSUVkrcVR2MVI5M0Uxcmc4UWF0L0c2RytLcE1veHVpQkxpUzJMUFZWQT09\"}],\"coauthors_list\":[\"Thomas Mumuni Bilintoh\",\"Robert G. Pontius Jr.\",\"Aiyin Zhang\"],\"sort_date\":\"2024-10-01\"},{\"activityid\":10042,\"fields\":{\"Type\":\"Articles in Refereed Journals\",\"Title\":\"<span style="font-size:12pt;color:#000000;">Performance of CA_Markov and DINAMICA EGO models<\\\/span><span style="font-size:12pt;"> <span style="color:#000000;">to evaluate urban risk in Antofagasta and Mejillones, Chile<\\\/span><\\\/span>\",\"Journal Title\":\"Natural Hazards\",\"Series Title\":\"\",\"Month \\\/ Season\":\"\",\"Year\":2024,\"Publisher\":\"\",\"Publisher City and State\":\"\",\"Publisher Country\":\"\",\"Volume\":\"120\",\"Issue Number \\\/ Edition\":\"\",\"Page Number(s) or Number of Pages\":\"\",\"ISSN\":\"\",\"DOI\":\"https:\\\/\\\/doi.org\\\/10.1007\\\/s11069-024-06512-5\",\"CoAuthor\":null,\"URL\":\"https:\\\/\\\/doi.org\\\/10.1007\\\/s11069-024-06512-5\",\"Description\":\"\",\"Include description in output citation\":0,\"Origin\":\"Manual\"},\"facultyid\":\"C11301315\",\"status\":[{\"id\":10042,\"status\":\"Completed\\\/Published\",\"term\":\"Spring\",\"year\":2024,\"termid\":\"2023\\\/03\",\"listingorder\":6,\"completionorder\":6}],\"userid\":\"C11301315\",\"attachments\":[{\"attachmentid\":8451,\"mimetype\":\"application\\\/pdf\",\"filename\":\"Henr\\u00edquez et al. - 2024 - Performance of CA_Markov and DINAMICA EGO models t.pdf\",\"filesize\":3780411,\"downloadurl\":\"https:\\\/\\\/faculty180.interfolio.com\\\/public\\\/download.php?key=SDRwNCtxSUpsamxBQ213WS9ucHFuNnMwT0hzQU11b2RPQkJ2cWc3amxyUmNRdVVXTkF4MU10Q1FvdGN3MEZPOXE0SEhVVkJ3R3IyaHRpQmszSHRlOWl5Q3JnT0U3TUZUSnlURHVBazI3NU9HQmN6dWdqYmpMTHNsVUp3cXRFbFlXS0VkczFSakZOei9EVndEdk1wYit2RlljejNMam9uRmVURHlreWJ5WjRlb3RGK21BZXRZQktra1h6Ym5saWRJS2lHL2dNSy9VN1E9\"}],\"coauthors_list\":[\"Cristian Henr\\u00edquez\",\"Robert G. Pontius Jr.\",\"Paulina Contreras\"],\"sort_date\":\"2024-1-01\"},{\"activityid\":12422,\"fields\":{\"Type\":\"Other Media (social media, blogs, podcasts, etc)\",\"Title\":\"<span style="font-size:12pt;">Best Practices for Classification Accuracy Metrics<\\\/span>\",\"Episode Title\":\"\",\"Date Published\":\"\",\"Year\":2024,\"Platform\":\"National Aeronautics and Space Administration\",\"CoAuthor\":null,\"URL\":\"https:\\\/\\\/lcluc.umd.edu\\\/sites\\\/default\\\/files\\\/Pontius-Webinar.mp4\",\"Description\":\"\",\"Include description in output citation\":0,\"Origin\":\"Manual\"},\"facultyid\":\"C11301315\",\"status\":[{\"id\":12422,\"status\":\"Completed\\\/Published\",\"term\":\"Summer\",\"year\":2024,\"termid\":\"2023\\\/05\",\"listingorder\":6,\"completionorder\":6}],\"userid\":\"C11301315\",\"attachments\":[{\"attachmentid\":10932,\"mimetype\":\"custom\\\/url\",\"filename\":\"https:\\\/\\\/lcluc.umd.edu\\\/sites\\\/default\\\/files\\\/Pontius-Webinar.mp4\",\"filesize\":452448,\"downloadurl\":\"https:\\\/\\\/faculty180.interfolio.com\\\/public\\\/download.php?key=SDRwNCtxSUpsamxBQ213WS9ucHFuNnMwT0hzQU11b2RPQkJ2cWc3amxyUmNRdVVXTkF4MU10Q1FvdGN3MEZPOWxLQytxSXV3YUNZTmNTR0tYR29Id1crNS9XV2tLdDlTUTBGU01DMmtlT1RtSmMrMDhwMGUyZWIrMmNsYk8yd3lvWCtUeVRwQ0xrbFh5RnFTbk5hOTB2VG9YaTNoYjdXVnhqMFZleDdaYTViVVdONW4wa2dMck04a3lNN2pZSDNY\"}],\"coauthors_list\":[\"Robert G. Pontius Jr.\"],\"sort_date\":\"2024-1-01\"},{\"activityid\":12418,\"fields\":{\"Type\":\"Chapters in Books\",\"Chapter Title\":\"<span style="font-size:12pt;">Comparison Among Time Series Maps of Deforestation in the Amazon: How Independent Monitoring Systems Relate to Official Data<\\\/span>\",\"Book Title\":\"Biodiversidade, meio ambiente e desenvolvimento sustent\\u00e1vel\",\"Series Title\":\"\",\"Year\":2024,\"Date Published\":\"2024-08-15\",\"Publisher\":\"Editora Conhecimento Livre\",\"Publisher City and State\":\"Piracanjuba-GO\",\"Country of Publisher\":\"Brazil\",\"Edition\":\"\",\"Page Numbers\":\"94-102\",\"ISSN\":\"2024-08-15\",\"DOI\":\"10.37423\\\/240609115\",\"CoAuthor\":null,\"URL\":\"https:\\\/\\\/api.conhecimentolivre.org\\\/ecl-api\\\/storage\\\/app\\\/public\\\/L.964-2024.pdf\",\"Description\":\"This study compares raster maps of Amazon deforestation during annual time intervals from 2008 to 2020 that derive from three sources: Global Forest Change (GFC), MapBiomas, and PRODES. We perform pairwise map overlays between PRODES and each of GFC and MapBiomas. Each overlay generates a square contingency table, which reveals Misses, Hits, and False Alarms. GFC shows more deforestation than PRODES while MapBiomas shows less deforestation between 2008 and 2020. Both MapBiomas and GFC have more disagreement than agreement with PRODES. False Alarms constitute 64% of GFC\\u2019s deforestation and 27% of MapBiomas\\u2019s deforestation with respect to PRODES. GFC missed 8% of PRODES deforestation while MapBiomas missed 33% between 2008 and 2020. We report Misses, Hits, and False Alarms for each of the 13 years.\",\"Include description in output citation\":1,\"Origin\":\"Manual\"},\"facultyid\":\"C11301315\",\"status\":[{\"id\":12418,\"status\":\"Completed\\\/Published\",\"term\":\"Fall\",\"year\":2024,\"termid\":\"2024\\\/01\",\"listingorder\":6,\"completionorder\":6}],\"userid\":\"C11301315\",\"attachments\":[{\"attachmentid\":10928,\"mimetype\":\"custom\\\/url\",\"filename\":\"https:\\\/\\\/api.conhecimentolivre.org\\\/ecl-api\\\/storage\\\/app\\\/public\\\/L.964-2024.pdf\",\"filesize\":452448,\"downloadurl\":\"https:\\\/\\\/faculty180.interfolio.com\\\/public\\\/download.php?key=SDRwNCtxSUpsamxBQ213WS9ucHFuNnMwT0hzQU11b2RPQkJ2cWc3amxyUmNRdVVXTkF4MU10Q1FvdGN3MEZPOWxLQytxSXV3YUNZTmNTR0tYR29Id1crNS9XV2tLdDlTOUJ4blVLM2xVcDBIek9JNDcyaWMxV0VqUzBCRWp5ak5iZzV2WlE1NkZBYWwwUnpuc3l2NmhpVDFScHRnN2VTZit1WDlKRkUvOHJ1cDEwa3MvY2k2Z1JwZlNiQnpVWlZIV0g0SnJOZSt4UE91aFlCSm1Zak12dz09\"}],\"coauthors_list\":[\"Antonio Victor Galvao da Fonseca\",\"Robert G. Pontius Jr.\"],\"sort_date\":\"2024-08-15\"},{\"activityid\":12421,\"fields\":{\"Type\":\"Other Media (social media, blogs, podcasts, etc)\",\"Title\":\"<span style="font-size:12pt;">Kappa the Undead and <span>the Inconsistent Adoption of Insightful Error Metrics<\\\/span><\\\/span>\",\"Episode Title\":\"\",\"Date Published\":\"2024-08-15\",\"Year\":2024,\"Platform\":\"International Society for Photogrammetry and Remote Sensing\",\"CoAuthor\":null,\"URL\":\"https:\\\/\\\/www.youtube.com\\\/watch?v=mAPwR1bkElY\",\"Description\":\"<span><span style="color:#131313;">This webinar is organized by the ISPRS WG II\\\/2 on 13th August, 2024<\\\/span><span style="color:#131313;"> with the technical support of the ISPRS Student Consortium (ISPRS SC). <br>Speaker: Professor Robert Gilmore Pontius Jr Abstract: This webinar is a follow-up to the 2011 paper by Pontius and Millones entitled Death to Kappa: Birth of Quantity Disagreement and Allocation Disagreement for Accuracy Assessment. The paper\\u2019s title indicates its two recommendations: 1 do not use kappa and 2 use components of disagreement to assess errors. We analyzed 200 articles to see how authors cited the Death to Kappa paper, which has more than 2000 citations. Results show that a quarter of the articles followed both recommendations, another quarter of the articles followed only the first recommendation, another quarter of the articles followed only the second recommendation, and the last quarter of the articles followed neither recommendation. Thus, the attempt to kill kappa and to replace kappa with more enlightening metrics has been partially effective, but kappa continues to haunt our profession. This webinar discusses and illustrates persistent bad habits concerning how authors use metrics in Remote Sensing and Land Change Modeling that were not covered in the 2011 paper. These habits include the use of kappa to assess thematic map accuracy at time points when the research question concerns change during time intervals and to assess the validation of land change predictions. We speculate on some reasons why some scientists use flawed methods and cite literature erroneously. We urge all scientists to pay attention to at least the titles of articles that they cite<\\\/span><\\\/span>\",\"Include description in output citation\":1,\"Origin\":\"Manual\"},\"facultyid\":\"C11301315\",\"status\":[{\"id\":12421,\"status\":\"Completed\\\/Published\",\"term\":\"Summer\",\"year\":2024,\"termid\":\"2023\\\/05\",\"listingorder\":6,\"completionorder\":6}],\"userid\":\"C11301315\",\"attachments\":[{\"attachmentid\":10931,\"mimetype\":\"custom\\\/url\",\"filename\":\"https:\\\/\\\/www.youtube.com\\\/watch?v=mAPwR1bkElY\",\"filesize\":452448,\"downloadurl\":\"https:\\\/\\\/faculty180.interfolio.com\\\/public\\\/download.php?key=SDRwNCtxSUpsamxBQ213WS9ucHFuNnMwT0hzQU11b2RPQkJ2cWc3amxyUmNRdVVXTkF4MU10Q1FvdGN3MEZPOWxLQytxSXV3YUNZTmNTR0tYR29Id1crNS9XV2tLdDlTNGc1dno0aVlWMW9Ra1VYaWpDWGs1UXRrQ0wzdm91bkxnU3VDckV2NzBEejBMQW93Nk91S2pFYnVodkZXSi82c29nUzRrdGl6MVZRPQ%3D%3D\"}],\"coauthors_list\":[\"Robert G. 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We report Misses, Hits, and False Alarms for each of the 13 years.<\\\/span><\\\/p>\",\"Include description in output citation\":0,\"Origin\":\"Manual\"},\"facultyid\":\"C11301315\",\"status\":[{\"id\":7772,\"status\":\"Completed\\\/Published\",\"term\":\"Spring\",\"year\":2023,\"termid\":\"2022\\\/03\",\"listingorder\":6,\"completionorder\":6}],\"userid\":\"C11301315\",\"attachments\":[{\"attachmentid\":6370,\"mimetype\":\"custom\\\/url\",\"filename\":\"https:\\\/\\\/proceedings.science\\\/p\\\/164339?lang=pt-br\",\"filesize\":452448,\"downloadurl\":\"https:\\\/\\\/faculty180.interfolio.com\\\/public\\\/download.php?key=SDRwNCtxSUpsamxBQ213WS9ucHFuNnMwT0hzQU11b2RPQkJ2cWc3amxyUmNRdVVXTkF4MU10Q1FvdGN3MEZPOWxLQytxSXV3YUNZTmNTR0tYR29Id1crNS9XV2tLdDlTWFZkdUxRbHZpM0RseHVZWHhJLzVTRE5XRm1BRm11ZGNsVWNUMVZIK0lJVXhXbWFVM2JKMklTSGdqZjZweVl4azRhY3J5YXJyQk5FPQ%3D%3D\"}],\"coauthors_list\":[\"Antonio Victor Galv\\u00e3o da Fonseca\",\"Robert G. 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Results show that the Cellular Automata\\u2019s allocation fails to follow the quantity of change that the Markov module computes. The module\\u2019s behaviour is likely to cause users to misinterpret the validation metrics and to miscommunicate with audiences. Thus, the answers to the four questions were not satisfactory for <span>th<\\\/span><span>is manuscript\\u2019s<\\\/span> case study.. <span><\\\/span><span>This manuscript\\u2019s <\\\/span><span>framework helps <\\\/span><span>users <\\\/span><span>to judge a model\\u2019s appropriateness for a specific application by combining sensitivity analysis with verification in a manner that helps to interpret validation.<\\\/span> <span>Users should<\\\/span> answer <span>the <\\\/span>four questions as users decide whether to use any software\\u2019s module<span>s<\\\/span>.<\\\/p>\",\"Include description in output citation\":0,\"Origin\":\"Manual\"},\"facultyid\":\"C11301315\",\"status\":[{\"id\":7773,\"status\":\"Completed\\\/Published\",\"term\":\"Summer\",\"year\":2023,\"termid\":\"2022\\\/05\",\"listingorder\":6,\"completionorder\":6},{\"id\":7773,\"status\":\"Revise & Resubmit\",\"term\":\"Spring\",\"year\":2023,\"termid\":\"2022\\\/03\",\"listingorder\":3,\"completionorder\":3}],\"userid\":\"C11301315\",\"attachments\":[{\"attachmentid\":8452,\"mimetype\":\"application\\\/pdf\",\"filename\":\"Viana et al. - 2023 - Four Fundamental Questions to Evaluate Land Change.pdf\",\"filesize\":3608700,\"downloadurl\":\"https:\\\/\\\/faculty180.interfolio.com\\\/public\\\/download.php?key=SDRwNCtxSUpsamxBQ213WS9ucHFuNnMwT0hzQU11b2RPQkJ2cWc3amxyUmNRdVVXTkF4MU10Q1FvdGN3MEZPOXE0SEhVVkJ3R3IzN3dQNW5adnQ3UmhSaUJxdmlna3BNOVlYWHpTODNDZk5wNkoyRFFnM3NuNWNnckNwbzJZSFRYbzcwRWNWVTV4V0U3NmpyWTN0YVo5NG82YmRzOUlzNTJBQWNUWnVsRXp5WUFSUzBzQmdRSks2RmdFbVppTXkv\"}],\"coauthors_list\":[\"Claudia Viana\",\"Robert G. 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Dr. Pontius specializes in Geographic Information Science (GIS) with particular expertise in statistics, simulation modeling, and land change science. He derives quantitative methods that are in the GIS software TerrSet, which Clark Center for Geospatial Analytics produces. 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