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 The SAGE Handbook series ==List of Handbooks Title Editor(s) Year Editions Ref 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 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. 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