User:GeogSage/sandbox

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This is my "To do list" and "Scrap drawer" where I keep fragmented half baked ideas for articles and scraps. When I begin to focus on one more seriously, I move it to one of my other sandboxes.

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Pages to create

Pages to create Pages for the "Space cadets" who worked as graduate students under William Garrison at the University of Washington Department of Geography: Richard Morrill (Geographer) John Nystuen Ronald Boyce Pages for editors of the Geographic Information Science and Technology Body of Knowledge: David DiBiase Ann Johnson (Geographer) Karen Kemp Ann Taylor Luck Elizabeth Wentz Pages for The AAG Applied Geography Specialty Group James R. Anderson Medal of Honor winners Budhendra "Budhu" Badhuri (2018) William Derrenbacher (2015) Jerome E. "Jerry" Dobson (2014) Jeffrey Osleeb (2013) Lee R. Schwartz (2012) Robert B. Honea (2011) Michael Sutcliffe (2007) Marilyn A. Brown (2004) Barry Wellar (2003) Richard D. Wright (2002) William B. Wood (2001) Kingsley E. Haynes (2000) Joel R. Morrison (1999) Frank H. Thomas (1997) John W. Frazier (1996) Pages for former presidents of the American Association of Geographers Esri Press Mobile GIS Mobile GIS Mobile GIS, or Mobile Geographic Information System, refers to using GIS technologies on mobile devices such as smartphones and tablets. It enables users to access, collect, edit, analyze, and display geospatial information in real time, anywhere, and at any time. A mobile GIS typically includes mapping software, GPS functionality, and data collection tools that can be used to gather and update information about the physical world. This information can then be used to make informed decisions, solve problems, and better understand patterns and relationships in the data. Number of mobile cellular subscriptions 2012–2016 Cell phones and other wireless communication forms have become common in society. Many of these devices are connected to the internet and can access internet GIS applications like any other computer. These devices are networked together, using technology such as the mobile web. Unlike traditional computers, however, these devices generate immense amounts of spatial data available to the device user and many governments and private entities. The tools, applications, and hardware used to facilitate GIS through the use of wireless technology is mobile GIS. Used by the holder of the device, mobile GIS enables navigation applications like Google Maps to help the user navigate to a location. When used by private firms, the location data collected can help businesses understand foot traffic in an area to optimize business practices. Governments can use this data to monitor citizens. Access to locational data by third parties has led to privacy concerns. With ~80% of all data deemed to have a spatial component, modern Mobile GIS is a powerful tool. The number of mobile devices in circulation has surpassed the world's population (2013) with a rapid acceleration in iOS, Android and Windows 8 tablet up-take. Tablets are fast becoming popular for Utility field use. Low-cost MIL-STD-810 certified cases transform consumer tablets into fully ruggedized yet lightweight field-use units at 10% of legacy ruggedized laptop costs. Although not all applications of mobile GIS are limited by the device, many are. These limitations are more applicable to smaller devices such as cell phones and PDAs. Such devices have small screens with poor resolution, limited memory and processing power, a poor (or no) keyboard, and short battery life. Additional limitations can be found in web client-based tablet applications: poor web GUI and device integration, online reliance, and very limited offline web client cache. Mobile GIS has a significant overlap with internet GIS; however, not all mobile GIS employs the internet, much less the mobile web. Thus, the categories are distinct. History Artist's impression of GPS Block IIR satellite in Earth orbit Civilian GPS receivers ("GPS navigation device") in a marine application A typical cell tower mounted on electric lines. Two decades of evolution of mobile phones, from a 1992 Motorola 8900X-2 to the 2014 iPhone 6 Plus The history of mobile GIS can be traced back to the early days of GPS and portable computing technology. In the 1990s, GPS receivers began to be integrated into portable computers, which paved the way for the developing of early mobile GIS systems. These systems were used primarily for navigation and mapping in outdoor environments. In the early 2000s, the advent of smartphones and tablets with built-in GPS, cameras, and data connectivity capabilities provided a new platform for mobile GIS. The first mobile GIS applications were developed for these devices, focusing primarily on mapping and navigation. Over the next decade, mobile GIS evolved rapidly, becoming more powerful, user-friendly, and accessible. The widespread adoption of mobile devices and cloud computing technologies made it possible to collect, store, and analyze large amounts of geospatial data in real time. This led to the development of advanced mobile GIS applications that can be used in various industries and disciplines, including asset management, field inspections, data collection, environmental monitoring, emergency response, and many others. Today, mobile GIS is a critical tool for organizations and individuals who need to access, collect, and analyze geospatial information in real time. The increasing capabilities of mobile devices and the development of new GIS technologies continue to drive the growth and evolution of mobile GIS. Applications Applications of mobile GIS include asset management, field inspections, data collection, environmental monitoring, emergency response, and many others. With the increasing availability and capabilities of mobile devices, mobile GIS is becoming an increasingly important tool for organizations and individuals in various industries and disciplines. Criticism Cellular networks work by only reusing radio frequencies (in this example frequencies f1-f4) in non adjacent cells to avoid interference Accuracy and reliability of data: One of the main criticisms of mobile GIS is the accuracy and reliability of the data collected using mobile devices. GPS signals can be disrupted by obstacles such as tall buildings, trees, and weather conditions, leading to inaccurate location data collected by mobile GIS. Data security: Mobile GIS systems often rely on cloud-based storage for data collection and management, which raises concerns about data security and privacy. Cost: Mobile GIS systems can be expensive to implement and maintain, especially for smaller organizations and individuals. This is because they require specialized hardware, software, and technical expertise. User experience: The user experience of mobile GIS can be limited by the size and form factor of mobile devices and the complexity of the underlying GIS software. Integration with existing systems: Integrating mobile GIS with existing GIS systems can be challenging, especially if the systems use different data formats and technologies. Privacy: See also At-location mapping Automotive navigation system Comparison of GIS software Counter-mapping Geomatics Historical GIS List of GIS data sources List of GIS software Participatory GIS QGIS SAGA GIS Technical geography Virtual globe References David DiBiase David DiBiase Alma mater University of Wisconsin Scientific career Institutions Pennsylvania State University Thesis Do people notice?  (1988) David DiBiase is a cartographer and geographer known for contributing to geographic information science (GIScience) education and research. He has made significant advancements in cartography and has played a crucial role in promoting global GIS literacy and establishing professional ethics. Notably, DiBiase was the lead editor on the Geographic Information Science and Technology Body of Knowledge and the Department of Labor’s Geospatial Technology Competency Model. He has been a professor at Pennsylvania State University since 1989, and has worked for ESRI. Education and field Master of Science, Cartography, University of Wisconsin—Madison Bachelor of Science., Cartography, University of Wisconsin—Madison Career DiBiase's career spans several decades and includes academic, research, and leadership roles. He has held positions at various academic institutions and organizations dedicated to GIS education and research. Penn State University DiBiase served as the Director of Education for the GeoTech Center at Penn State University. He also worked as a Senior Lecturer in the Department of Geography and as the John A. Dutton e-Education Institute Director. Esri DiBiase has worked with Esri, a leading provider of GIS software, as the Director of Education for several years. In this role, he has been instrumental in developing educational resources and initiatives to promote GIS literacy worldwide. Publications DiBiase has contributed extensively to geographic information science research. His research interests include cartography, spatial analysis, and GIS education. He has published numerous papers in peer-reviewed journals and has authored or co-authored several books on GIS and cartography. Awards and honors Over the course of his career, DiBase has received the following awards: American Association of Geographers Fellow, 2018 Urban and Regional Information Systems Association Horwood Distinguished Service Award, 2012 University Consortium for Geographic Information Science Educator of the Year, 2005 American Association of Geographers Media Achievement Award, 1999 Philanthropy In 2020, DiBiase established the Founders Scholarship Fund through the John A. Dutton e-Education Institute to support online students enrolled online at the Pennsylvania State University College of Earth and Mineral Sciences Scholarship awards range between 500 and 2,500. See also Brandon Plewe – American historical geographer Cynthia Brewer – American geographer Michael DeMers – American geographer and fiction writer Michael Frank Goodchild – British-American geographer Michael Peterson – American geographer and cartographer Technical geography – Study of using and creating tools to manage spatial information Waldo Tobler – American geographerPages displaying short descriptions of redirect targets Patricia Gober Patricia Gober Citizenship United States of America Alma mater The Ohio State University, University of Wisconsin–Whitewater Occupation Geographer Patricia Gober is a geographer, professor of geography at Arizona State University, and was the president of the American Association of Geographers (AAG) between 1997 and 1998. Her research interests include "urban planning and design, sustainability, and the connections among food, water, and energy." Education and field Gober earned their B.S. in geography in 1970 from the University of Wisconsin-Whitewater. She earned her M.A. in 1972 and Ph.D. in 1975, both in geography from The Ohio State University. Honors and awards Presidential Achievement Award Recipients, American Association of Geographers, 2011 Prince Sultan bin Abdulaziz International Prize for Water, 2008 Faculty Research Achievement Award, Arizona State University, 2009 Honorary doctorate of science, Carthage college, 1998 Arizona State University Pat Gober Water Prize The "Pat Gober Water Prize" was created to recognize Gober's contributions to Arizona State University in 2019. It awarded annually by the ASU School of Geographical Sciences and Urban Planning to students who win a research proposal competition related to water-related research. The award is $1,500, and can be used for research and travel expenses. See also David H. Kaplan – American geographer, academic, author Gamma Theta Upsilon – International geography honor society Rebecca Lave – American critical physical geographer Mei-Po Kwan – GeographerPages displaying short descriptions with no spaces Mona Domosh – American geographer and academic Waldo Tobler – American geographerPages displaying short descriptions of redirect targets Yi-Fu Tuan – Chinese-American geographer (1930–2022) References Early detection rapid response Geographically Weighted Regression The Geographically Weighted Regression (GWR) Family of Statistics is a collection of spatial statistical techniques that extend traditional regression methods by allowing for spatial variability in the relationships between dependent and independent variables. Where linear Ordinary least squares (OLS) regression assumes that the variables have a global relationship, GWR looks at local relationships between variables. This family of statistics is instrumental in spatial analysis, as it accounts for spatial heterogeneity and the influence of geographic location on statistical relationships. GWR is built on. In OLS Regression, the formula is: $y_{i}=\beta _{1}\ x_{i1}+\beta _{2}\ x_{i2}+\cdots +\beta _{p}\ x_{ip}+\varepsilon _{i},$ where $\mathbf {x} _{i}$, is a column vector of the $i$-th observation of all the explanatory variables; ${\boldsymbol {\beta }}$ is a $p\times 1$ vector of unknown parameters; and the scalar $\varepsilon _{i}$ represents unobserved random variables (errors) of the $i$-th observation. $\varepsilon _{i}$ accounts for the influences upon the responses $y_{i}$ from sources other than the explanatory variables $\mathbf {x} _{i}$. Getis-Ord Gi Getis-Ord Gi (also known as the Getis-Ord General G statistic) is a statistical method used to identify spatial clusters of high or low values in a spatial dataset. The method was developed by Arthur Getis and J. K. Ord in 1992. The Gi statistic measures the degree of spatial autocorrelation of a variable in a set of neighboring locations. Spatial autocorrelation refers to the extent to which similar values tend to cluster together in space. The Gi statistic is calculated for each location in the dataset and can be used to identify clusters of high or low values and outliers. The calculation of the Gi statistic involves three steps: Calculate the local sum for each location. This involves adding up the variable values for the location and its neighboring locations. Calculate the global sum and mean for the entire dataset. This involves adding up the variable values for all locations in the dataset and dividing by the total number of locations. Calculate the standard deviation for the entire dataset. The Gi statistic for each location is then calculated as follows: Gi = (Xi - Xbar) / S * Σj(wij * Xj - Xbar) where Xi is the value of the variable at location i, Xbar is the mean of the variable for the entire dataset, S is the standard deviation for the entire dataset, wij is a spatial weight that measures the distance between location i and j, and Xj is the value of the variable at location j. A positive Gi value indicates that the location has a high value relative to its neighbors, while a negative Gi value indicates that the location has a low value relative to its neighbors. The magnitude of the Gi value indicates the strength of the spatial clustering. The Gi statistic can be visualized using a map, with locations colored based on their Gi values. This can help identify the dataset's spatial clusters of high or low values. The Gi statistic is commonly used in geography, epidemiology, and environmental science to analyze spatial patterns in data. Getis-Ord Gi* Getis-Ord Gi* (pronounced "Getis-Ord G-star") is an extension of the Getis-Ord Gi statistic, which is used to identify statistically significant hotspots and coldspots in a spatial dataset. The method was developed by Arthur Getis and J. K. Ord in 1996 to improve the original Gi statistic. The Gi* statistic is calculated using a similar formula to the Gi statistic but with an additional term that considers the spatial autocorrelation of the data at different distances. The formula for the Gi* statistic is: Gi* = (Xi - Xbar) / S * Σj(wij * Xj - Xbar) / √(Σj(wij))^2 / N where N is the total number of locations in the dataset. The numerator of the Gi* formula is the same as the Gi formula. At the same time, the denominator represents a measure of the expected value of the sum of the weights for each location. The denominator considers the spatial autocorrelation of the data at different distances and is used to standardize the numerator. The Gi* statistic produces a z-score, which can be used to determine the statistical significance of a hotspot or coldspot. A positive z-score indicates a statistically significant hotspot (i.e., a location with a high value surrounded by locations with high values), while a negative z-score indicates a statistically significant coldspot (i.e., a location with a low value surrounded by locations with low values). The significance of the z-score can be determined using a p-value or a critical value. A p-value represents the probability of obtaining a z-score as extreme as the observed value, assuming that the null hypothesis (i.e., no spatial clustering) is true. A critical value represents the threshold above which the z-score is considered statistically significant. The Gi* statistic can be used to identify hotspots and coldspots in various spatial datasets, such as crime data, disease incidence data, and environmental data. The method is particularly useful for identifying spatial patterns that may be missed by other methods and for generating hypotheses about the underlying causes of spatial clustering. The laws of geography Waldo Tobler in front of the Newberry Library. Chicago, November 2007 The laws of geography are a set of scientific laws defining spatial data characteristics. The concept of laws in geography is a product of the quantitative revolution and is a central focus of quantitative geography. Their emergence is highly influential and one of the major contributions of quantitative geography to the broader branch of technical geography. The discipline of geography is unlikely to settle the matter anytime soon. Several laws have been proposed, and Tobler's first law of geography is the most widely accepted. The first law of geography, and its relation to spatial autocorrelation, is highly influential in the development of technical geography. Some have argued that geographic laws do not need to be numbered. The existence of a first invites a second, and many are proposed as that. It has also been proposed that Tobler's first law of geography should be moved to the second and replaced with another. A few of the proposed laws of geography are below: List of Laws in Geography Law Name Law Author Year Tobler's first law of geography "Everything is related to everything else, but near things are more related than distant"Tobler, Waldo (2004). "On the First Law of Geography: A Reply". Annals of the Association of American Geographers. 94 (2): 304–310. doi:10.1111/j.1467-8306.2004.09402009.x. S2CID 33201684. Retrieved 10 March 2022.</ref> Waldo Tobler 1970 Tobler's second law of geography "the phenomenon external to a geographic area of interest affects what goes on inside." Waldo Tobler Arbia's law of geography U"Everything is related to everything else, but things observed at a coarse spatial resolution are more related than things observed at a finer resolution." Arbia 1996 : : : Uncertainty principle: "that the geographic world is infinitely complex and that any representation must therefore contain elements of uncertainty, that many definitions used in acquiring geographic data contain elements of vagueness, and that it is impossible to measure location on the Earth's surface exactly." Terrain Ruggedness Index The Terrain Ruggedness Index (TRI) is a quantitative measure used in geography and geomorphology to assess the roughness or ruggedness of a terrain surface. It is a tool commonly employed in fields such as hydrology, ecology, and geology to characterize landscapes and understand their influence on various processes and phenomena. ==Calculation The Terrain Ruggedness Index is typically computed using elevation data, such as digital elevation models (DEMs) derived from satellite imagery or ground-based surveys. The index is calculated based on the variability of elevation within a defined area, with higher values indicating greater ruggedness or roughness. ==Interpretation The Terrain Ruggedness Index provides a quantitative measure of the variability in terrain elevation within a specified area. Higher values of TRI indicate rougher or more rugged terrain, whereas lower values suggest smoother or flatter landscapes. This index is particularly useful in landscape analysis, ecological studies, and terrain modeling, where understanding terrain complexity is essential. Riley et al. 1999 A terrain ruggedness index that quantifies topographic heterogeneity https://livingatlas-dcdev.opendata.arcgis.com/content/28360713391948af9303c0aeabb45afd/about John Nystuen John Nystuen Born (1931-01-07)7 January 1931 Northfield, Minnesota Died 2 July 2022(2022-07-02) (aged 91) Citizenship United States of America Alma mater University of California, Berkeley, University of Washington Occupation Geographer John Nystuen (January 1, 1931 – July 7, 2022) was an American Geographer https://deepblue.lib.umich.edu/bitstream/handle/2027.42/175283/SolsticeVolumeXXXIIINumber2.pdf?sequence=1&isAllowed=y https://www.sierraclub.org/sites/default/files/2022-12/The%20Lookout%20Fall%202022%20Final.pdf http://faculty-history.dc.umich.edu/faculty/john-nystuen/memoir https://www.tandfonline.com/doi/pdf/10.1559/152304000783547867 Dissolution of Geography department at Harvard Smith, Neil (1987). ""Academic War Over the Field of Geography": The Elimination of Geography at Harvard, 1947-1951". Annals of the Association of American Geographers. 77 (2). doi:10.1111/j.1467-8306.1987.tb00151.x. Retrieved 27 May 2024. Pradyumna Prasad Karan Pradyumna Prasad Karan Born (1930-09-31)31 September 1930 Gaya (India) Died Occupation Geographer Academic background Alma mater Patna University, Banaras Hindu University, Indiana University Bloomington Academic work Discipline Geography Sub-discipline geographic information science Pradyumna Prasad Karan, also known as Paul, was an influential South Asian Geographer in the United States, focusing on environmental management and sustainable development in the non-western world. Education and field Career Publications Awards See also References External links Springer Handbook Series List of Handbooks Series number Title Author(s) Year ISBN DOI Ref 1 Spatial Data Analysis: Models, Methods and Techniques Manfred M. Fischer, Jinfeng Wang 2011 ISBN 978-3-642-21719-7 doi:10.1007/978-3-642-21720-3 2 Quantile Regression for Spatial Data Daniel P. McMillen 2013 ISBN 978-3-642-31814-6 doi:10.1007/978-3-642-31815-3 3 Spatial Econometrics J. Paul Elhorst 2014 ISBN 978-3-642-40339-2 doi:10.1007/978-3-642-40340-8 4 Technology and Industrial Parks in Emerging Countries Andrés Rodríguez-Pose, Daniel Hardy 2014 ISBN 978-3-319-07991-2 doi:10.1007/978-3-319-07992-9 5 Regional Perspectives on Policy Evaluation Marco Percoco 2014 ISBN 978-3-319-07991-2 doi:10.1007/978-3-319-07992-9 6 The Creation of Local Innovation Systems in Emerging Countries Marco Ferretti, Adele Parmentola 2015 ISBN 978-3-319-10439-3 doi:10.1007/978-3-319-10440-9 7 Regional Development in Rural Areas André Torre, Frédéric Wallet 2016 ISBN 978-3-319-02371-7 doi:10.1007/978-3-319-02372-4 8 Making Megacities in Asia Du Huynh 2020 ISBN 978-981-15-0659-8 doi:10.1007/978-981-15-0660-4 9 Rethinking Input-Output Analysis Jan Oosterhaven 2019 ISBN 978-3-030-33447-5 doi:10.1007/978-3-030-33447-5 10 Regional Resilience to Climate and Environmental Shocks: A Spatial Econometric Perspective Rita De Siano, Valerio Leone Sciabolazza, Alessandro Sapio 2020 ISBN 978-3-030-54587-1 doi:10.1007/978-3-030-54588-8 11 The Economics of Talent: Human Capital, Precarity and the Creative Economy Roberta Comunian, Lauren England, Alessandra Faggian, Charlotta Mellander 2021 ISBN 978-3-319-95122-5 doi:10.1007/978-3-319-95124-9 12 Innovation and Regional Technological Convergence: Theory and Evidence Tomasz Kijek, Arkadiusz Kijek, Anna Matras-Bolibok 2023 ISBN 978-3-031-24530-5 doi:10.1007/978-3-031-24531-2 13 Regional Business Cycles in Latin America Patricio Aroca, Pablo Mejía-Reyes 2024 ISBN 978-3-319-98865-8 taskforce template {{Wikipedia:{{{projectname1}}}/Navigation}} Welcome to the {{{taskforce}}} task force of [[Wikipedia:{{{projectname1}}}|{{{projectname1}}}]]. 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