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SpatialDataLab- Spatiotemporal Innovation Workshop 2024

This Repository is for Spatiotemporal Innovation Workshop Held by Spatial Data Lab , Center for Geographic Analysis, Harvard Univeristy

More Detail :Spatiotemporal Innovation Summer Workshop 2024

Harvard DataVerse : https://dataverse.harvard.edu/dataverse/sdl_dataverse

KNIME Geospatial Extension : https://github.com/spatial-data-lab/knime-geospatial-extension

The workshop mainly use KNIME Analytic Platform and its Python Extension.

Step 1: KNIME Installation

  • Go to the KNIME website (choose suitable version of Operation System)

    https://www.knime.com/downloads/download-knime

    Note:as the updating of KNIME is frequent, self-extrating archive is highly recommended for Windows**

  • Extract to a folder, double click KNIME.exe to start KNIME

  • To get the best performance of KNIME, open knime.ini with Notepad(Windows)

    revise -Xmx2048m as -Xmx8g ( 8 Giga Byte, or half for your computer memory)

  • Choose a directory as workspace(e.g. E:\KNIMEworkspace),While starting KNIME

    user can execute multiple KNIME APP simultaneously (same or different versions), but each KNIME Analysis Platform must use a unique directory as workspace;

    While run only one KNIME APP at a time, different versions of KNIME software can use the same workspace directory.

Step 2: Python Installation(Anaconda)

  • Go to Anaconda website and download

    https://www.anaconda.com/

    Double click the installation file to run Anaconda

  • Star Anaconda Prompt or Anaconda,build a new Conda enviroment of Python 3.9 for KNIME

    conda create -n geoai python=3.9 knime-extension knime-python-base -c conda-forge -c knime

    Screenshot

  • Python packages

    2-2-GeoAI

    pip install XGeoML mgwr geoshapley matplotlib pygam

    pip install git+https://github.com/hyperopt/hyperopt-sklearn

    remember to install git first

    3-1-Network Community

    pip install fiona python-igraph leidenalg pygeoda

    pip install torch torch_geometric

    refer to: https://pytorch-geometric.readthedocs.io/en/latest/install/installation.html

    3-2-Remote Sensing

    pip install rasterio folium mapclassify

    pip install earthengine-api geemap

Step 3: KNIME Extension

  • Install Python and R extension in KNIME

    open KNIME > File > Install KNIME extension , in the dialog

    input "Python" in the box of type filter text bar > check KNIME Python Integration > input "R Statistics" in the box of type filter text bar > check KNIME Interactive R Statistics Integration > next >... > Restart KNIME

  • Commomly used Extensions

    Geospatial Analytics Extension for KNIME

    KNIME H2O Machine Learning Integration

    KNIME XGBoost Integration

    KNIME Al Assistant (Labs)

    KNIME Al Extension

    KNlME Deep Learning - Keras Integration

    KNIME H20 Machine Learning Integration

    KNlME Machine Learning Interpretability Extension

    KNIME XGBoost Integration

    KNIME Column Expressions (Labs)

    KNIME Plotly

    KNIME Data Generation

    KNlME Textprocessing Chinese Language Pack

    *KNIME Email Processing

    *KNIME Nodes for Scikit-Learn (sklearn) Algorithm

    *KNIME Parallel Chunk Loop Nodes

    *KNIME Statistics Nodes (Labs)

    *KNIME Web Interaction (Labs)

    *KNlME Interactive R Statistics Integration

    *Redfield NLP Nodes

    *Redfield BERT Nodes

Step 4: Default Python Environment for Deep learning

KNIME > Preferences > KNIME > Python Deep Learning > click "New environment"

Screenshot Screenshot

Step 5: Scripting Extension Configuration

  • Configuring R and Python enviroment in KNIME

open KNIME > File > Preference, in the dialog

KNIME > Conda > click Browse... to choose the installed Anaconada directory, such as : D:\ProgramData\Anaconda3

KNIME > Python > choose geoai(or other environment) for Python3 (defult)

KNIME >R >click Browse... to choose the installed R directory, such as C:\Program Files\R\R-4.2.1

click Apply and Close.

Optional: R and RStudio Desktop

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