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Spatial Data Analysis: Theory And Practice

More geospatial data is being collected than ever before. When combined with artificial intelligence to automatically scan and interpret this vast amount of visual data on the cloud, unprecedented capabilities are becoming available. These rapidly growing data mountains can then feed increasingly sophisticated predictive models to generate more and more insights and results.

Spatial Data Analysis: Theory and Practice

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Understanding all the possible use cases is in its infancy. Geospatial data and analysis enabled by new technologies will also allow us to (re)examine a vast range of hypotheses, potentially with implications for financial theory as well as practice.

We founded and work through the Spatial Finance Initiative (SFI), which aims to mainstream geospatial capabilities enabled by space technology and data science into financial decision-making globally. SFI undertakes and coordinates research and channels it into real-world finance-related applications. SFI has been established by The Alan Turing Institute, Satellite Applications Catapult, and the University of Oxford.

The book has 10 chapters, divided into two sections on geodesy and on techniques for visualization of spatial data; each chapter has separate sections on theory and practice.[1] For practical aspects of geographic information systems it uses ArcGIS as its example system.[2]

Moving from geodesy to visualization,[1] chapters 4 and 5 concern the use of color and scale on maps. Chapter 6 concerns the types of data to be visualized, and the types of visualizations that can be made for them. Chapter 7 concerns spatial hierarchies and central place theory, while chapter 8 covers the analysis of spatial distributions in terms of their covariance. Finally, chapter 10 covers network and non-Euclidean data.[1][3]

Mousavi also writes that, although the book covers a broad selection of topics, it "suffers from lack of necessary depth" and that it is confusingly structured.[1] Sang-Il Lee points to a lack of depth as the book's principal weakness.[3] Stein notes that its reliance on a specific version of ArcGIS makes it difficult to reproduce its examples, especially for international users with different versions or for users of versions updated after its publication.[2] Another weakness highlighted by Griffith is "its limited connection to the existing literature, with its citations far too often being only those works by its authors".[5] Harris sees a missed opportunity in the omission of spatial statistics, movement data, and spatio-temporal data, the design of spatial data structures, and advanced techniques for visualizing geospatial data.[4]

In recent years, the spatial econometrics literature has exhibited a growing interest in the specification and estimation of econometric relationships based on spatial panels. Spatial panels typically refer to data containing time series observations of a number of spatial units (zip codes, municipalities, regions, states, jurisdictions, countries, etc.). This interest can be explained by the fact that panel data offer researchers extended modeling possibilities as compared to the single equation cross-sectional setting, which was the primary focus of the spatial econometrics literature for a long time. Panel data are generally more informative, and they contain more variation and less collinearity among the variables. The use of panel data results in a greater availability of degrees of freedom, and hence increases efficiency in the estimation. Panel data also allow for the specification of more complicated behavioral hypotheses, including effects that cannot be addressed using pure cross-sectional data (see Hsiao 2005 for more details).

PLSCS 6200 - Spatial Modeling and Analysis (crosslisted) NTRES 6200 Spring. 3 credits. Student option grading.Prerequisite: PLSCS 4110 , PLSCS 4200 , or equivalent or permission of instructor. D. G. Rossiter.Theory and practice of applying geo-spatial data for resource inventory and analysis, biophysical process modeling, and land surveys. Emphasizes use and evaluation of spatial analytical methods applied to agronomic and environmental systems and processes. Laboratory section is used to process, analyze, and visualize geo-spatial data of interest to the student, ending in a comprehensive student project.

EPID 702 Analysis with Missing Data in Epidemiology - ONLINE(1 credit hour) Lu WangThis course discusses both statistical theory and methodology aimed at addressing missing data problems in epidemiology studies. We will introduce different patterns of missing data, various missing data mechanisms, different statistical methods to deal with missing data, the advantages and disadvantages of each method, likelihood-based inference, data augmentation, multiple imputation, various missing patterns in longitudinal studies, drop-out, selection model, and pattern-mixture model. Overall, this course covers both applied and theoretical aspects related to statistical analysis with missing data. Prerequisites: introduction to statistical inferences, such as likelihood estimations; regression models; correlated and longitudinal data analysis. Syllabus for EPID 702

EPID 703 Applied Infectious Disease Modeling - ONLINE(1 credit hour) Andrew BrouwerInfectious disease modeling is increasingly being used to inform policy, practice, and research. This course will provide an introduction to the epidemiological and mathematical concepts underlying infectious disease modeling as well as the application these concepts through hands-on model implementation. This course will be taught in an alternating lecture and lab style; we will be coding in R software. We will discuss the use of models in making predictions, selecting interventions, and assessing counterfactuals. Student will develop skills identifying the important underlying processes and assumptions in the infectious disease systems they want to model. We will explore the basic reproduction number, its importance to infectious disease dynamics, and how it is calculated. We will compare and contrast compartmental, stochastic, and agent-based model frameworks, as well as deterministic and stochastic model implementations. We will consider how models can be connected to data, introducing parameter identifiability, parameter estimation, and uncertainty quantification. Prerequisites: Epid 793 is a good introduction to Epid 703 and Epid 730. Experience with modeling or good quantitative background, including statistics and differential equations; familiarity with R software. Syllabus for EPID 703

EPID 777 Geographic Information Systems for Epidemiology - ONLINE(1 credit hour) Shannon J. Brines Geographic Information Systems (GIS) are used for displaying and analyzing spatial data. Data from a variety of sources may be compared utilizing overlay analysis and spatial statistics. Modern tools permit novice GIS users to perform spatial analysis without extensive training. This course will introduce students to ArcGIS, the world's leading GIS analysis package. Examples of epidemiological applications will give students the opportunity to see and use this powerful tool. Some of the topics to be covered are data import/export, layering, data table management, classification, labeling, spatial and attribute queries, and buffer analysis. No prerequisite. Syllabus for EPID 777

EPID 778 Spatial Statistics for Epidemiological Data - IN PERSON/HYBRID(1 credit hour) Veronica BerrocalWith the increasing availability of geographic information systems, spatial data have become more frequent in many disciplines, including public health and epidemiology. This course aims to provide an introduction to spatial statistical methods for epidemiological data, covering modeling approaches for the two different types of spatial data: point-referenced data, where the geographical coordinates of the observations have been recorded; and areal-averaged data, where summary statistics (e.g., number of disease cases by county, zip code, etc.) are reported for each areal unit. Topics covered include exploratory analysis for spatial data, covariance functions, kriging, spatial regression; disease mapping, spatial smoothing; point processes, assessment of clustering, and cluster detection. Each lecture will feature a lab component, during which spatial analyses of datasets, made available to the participants, will be performed using the publically available R statistical software (downloadable to your laptops at Although previous experience with R is preferred, it is not required. Prerequisite: Course in basic statistics and (e.g., EPID 701) and an introductory course in epidemiology (e.g. EPID 709). Syllabus for EPID 778

EPID 784 Survival Analysis Applied to Epidemiologic and Medical Data - IN PERSON/HYBRID(1 credit hour) Kevin He The primary objectives of this course are to provide participants with thebackground required to understand commonly used survival analysis methodsand to apply such methods using standard statistical software. The coursematerial relies heavily on examples and intuitive explanations of concepts. Themathematical level is completely accessible with knowledge of high schoolalgebra, one semester of calculus, and a one-year course in basic statisticalmethods. Examples have been chosen from various epidemiologic and medicalapplications. The course topics include: an introduction to survival analysis;censoring and truncation; non-parametric (e.g. Kaplan-Meier estimator),parametric (e.g., exponential model), and semi-parametric (e.g., Coxproportional hazards model) estimators; two- and k-sample tests (e.g., log rank);time-varying covariates; competing risks analysis; and recurrent event analysis.Students will carry out some applied problems to illustrate the main ideas ofsurvival analysis and to solidify the concepts. In the labs, students will performmore complex analyses with statistical software. Syllabus for EPID 784EPID 787 An Introduction to Multilevel Analysis in Public Health - IN PERSON/HYBRID(1 credit hour) Jay KaufmanMultilevel analysis is an essential analytic tool in epidemiology and public health that allows the simultaneous investigation of the effects of exposures defined at multiple levels on individual-level outcomes. This short course will review the rationale for multilevel analysis in public health research, build the statistical theory and practice of these models from the fundamentals of the regression-based approaches and demonstrate a variety of different forms that the models can take, including fixed and random effects, marginal (population average) models and extensions for categorical and survival outcomes. Fitting and interpreting models will be demonstrated using Stata statistical software, and parallel code will also be provided in SAS. Special emphasis will be placed on the strengths and limitations of multilevel analyses in investigating social and group-level determinants of health, and the causal interpretations of estimated parameters. Prerequisite: Introductory course in epidemiology and an introductory course in statistics (i.e. some familiarity with regression modeling). Syllabus for EPID 787EPID 793 Complex Systems Modeling for Public Health Research - ONLINEFull Day Course(2 credit hours) Marisa Eisenberg, Michael HayashiThis course will provide an introduction to two major complex systems science modeling techniques with wide applicability to public health. We will cover an introductory overview of complex systems modeling in general, and systems dynamics and agent-based modeling in particular. We will discuss model applications, best practices, and more advanced practical topics such as team-building, computation, funding, and publication. We will provide extensive hands-on lab experience during each section of the course. At the completion of the course the student will be able to explain current and potential future roles of complex systems science in public health, describe the respective advantages/disadvantages of each method covered, and will be expected to produce a draft proposal for applying one of the two system science methods to a particular problem. Students will become informed consumers of complex systems research, will be prepared to actively participate in interdisciplinary teams using the modeling techniques, and will be well positioned to incorporate systems science methods into their own research. Prerequisite: Relevant background in public health. Syllabus for EPID 793 041b061a72


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