Urban Geographic Information System (Python) @ NTNU
Course Content
As the development of air transportation, people are suffered from infectious disease much severe than ever. But how can we help the global public health system from a geographic approach? In the undergraduate courses, you have already understood various spatial analysis methods; however, you seldom apply these methods into your projects or dissertations. Here, we will use three examples to demonstrate the spatiotemporal disease transmission and the applications of spatial analysis in the clinical medicine.
Course Intro.
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Contents: (1) Course intro (2) Grading policy (3) Why do you need to take this course? (4) What will you learn from this course?
Python Environment Settings
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Contents: (1) Anaconda install (2) Hello world (3) Terminal/ Windows powershell (4) Git
Python Basic I
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Content: (1) Variables (2) Data types (3) Numbers (4) Strings (5) Booleans (6) Operators (7) Lists (8) Tuples (9) Sets (10) Dictionaries
Python Basic II
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Content: (1) Conditions (2) For loops (3) While loops (4) Functions (5) Recursive Functions
Python – Statistics I
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Content: (1) PCA in Mathematics (2) PCA in Python
Python Visualization
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Content: (1) Matplotlib (2) Seaborn (3) Plotly (4) Contextily
Python Statistics II & Regression
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Content: (1) Statistical analysis (2) Normality test (3) Inferential statistics (4) Correlation analysis (5) Scale problem (6) Regression
Spatial Analysis: OSM
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Content: (1) Get city list (2) Get map (3) Map view
Spatial Analysis: GeoPandas
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Content: (1) Read spatial data (2) Visualization with explore (3) Sampling points (4) Spatial Join (5) Clip (6) Dissolve (7) Area calculation (8) Get centroid (9) Distance calculation
Spatial Analysis: Spatial Autocorrelation
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Content: (1) Weighting matrix (2) Global Moran's I (3) Local indicators of spatial association – LISA (ESDA & pysal) (4) Weighting matrix (GeoDa) (5) Local indicators of spatial association – LISA (GeoDa) (6) Local Getis-ord statistics (7) Bivariate LISA
Spatial Analysis: GWR
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Content: (1) Geographically weighted regression (2) Parallel version multi-scale/ normal geographically weighted regression (3) GWR prediction (4) Plot intercept map (5) Plot coefficient map
Spatial Analysis: Spatial Regression
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Content: (1) Spatial generalized linear model (2) Ordinary least squares model (3) Spatial lagged exogenous regression (4) Spatial lagged endogenous regression (5) Spatial error model
Python ML Classification
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Content: (1) Concept of classification (2) Evaluation metrics (3) Model optimization (4) Overfitting vs underfitting (5) Bagging (6) Boosting (7) K-nearest neighbors (8) Decision tree (9) Random forest (10) AdaBoost (11) Gradient Boost (12) XGBoost (13) Naive Bayes (14) Linear SVM (15) Remarks