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This is my "To do list" and "Scrap drawer" where I keep fragmented half baked ideas for articles and scraps.

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Pages to create

A Computer Movie Simulating Urban Growth in the Detroit Region is a seminal academic paper authored by American geographer Waldo R. Tobler and published in the journal Economic Geography in 1970. Widely regarded as a foundational work in the fields of computational geography, urban modeling, and spatial analysis, the paper introduced one of the first implementations of cellular automata-style modeling in a geographic context. It is most famous for containing Tobler’s First Law of Geography: “Everything is related to everything else, but near things are more related than distant things.”

Background

Waldo Tobler in front of the Newberry Library. Chicago, November 2007

The late 1960s and early 1970s marked a transformative period for geography, as the discipline began to adopt quantitative methods and computational tools. Tobler, working at the University of Michigan and associated with the influential Michigan Inter-University Community of Mathematical Geographers (MICMG), was at the forefront of this methodological revolution. His 1970 paper reflects a growing interest in simulating complex spatial processes using computers.

Tobler’s First Law of Geography

Main article: Tobler's first law of geography

The publication is best known for articulating what came to be known as Tobler’s First Law of Geography. Though embedded within a methodological discussion, the law succinctly captures a key principle of spatial interaction and spatial autocorrelation. The law has since become a major componenet of geographic theory, and is often cited in discussions of spatial dependence and regional science.

Everything is related to everything else, but near things are more related than distant things.

— Waldo Tobler (1970)

Significance and Legacy

Tobler's paper is widely considered a pioneering contribution to the quantitative revolution in geography. It demonstrated how computational models could be used to simulate complex geographic phenomena, serving as a precursor to modern spatial modeling, urban analytics, and geosimulation. The work anticipated the integration of computer science and geography, a synthesis now central to disciplines such as Geographic Information Science (GIScience).

The model itself was influential not only for its innovative use of computational tools but also for the conceptual framing it offered regarding spatial processes and neighborhood effects. It laid groundwork for the development of spatial interaction models, diffusion models, and land-use change simulations in geography and urban planning.

Reception and Influence

The paper has been extensively cited and remains a required reading in many geography and GIScience curricula. It has been recognized by geographers and historians of science as a foundational text in computational geography. Tobler’s approach inspired later generations of geographers, including work on spatial econometrics, urban morphogenesis, and simulation-based planning models.

See also

• Geomatics

External Links

Full text]

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
• Monmonier Algorithum

• Esri Press

Technical geography academic programs

Academic programs

Many academic institutions use, or have historically used, the term "technical geography" to either sub-divide their department or describe courses and content offered within their department. These include, but are not limited to:

• Babeș-Bolyai University
• California State University, Long Beach
• Century College
• College of DuPage
• Everett Community College
• Grossmont College
• Jacksonville University
• Muhammadiyah University of Surakarta
• Northwest Missouri State University
• Oregon State University
• South Dakota State University
• Southern Utah University
• Tennessee State University
• University of Mary Washington
• University of Maryland
• University of Peshawar
• University of Saskatchewan
• University of South Alabama
• Portland Community College
• Weber State University

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 (born 1960)
• Michael DeMers – American geographer and fiction writer
• Michael Frank Goodchild – British-American geographer
• Michael Peterson – American geographer and cartographer
• Technical geography – Study of 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
• 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)

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.

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

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