VECTOR DATA PROCESSING USING PYTHON TOOLS 11:15. GeoPandas Advanced Topics. What additional capabilities does GeoPandas provide, including data access, plotting and analysis? How does it integrate with other common Python tools? How do GeoPandas data objects integrate with analyses of raster data over vector geospatial features? 11:50. OpenStreetMap data access and processing. MACHINE LEARNING: SUPERVISED LEARNING: TREE-BASED METHODSSEE MORE ON GEOHACKWEEK.GITHUB.IO VECTOR DATA PROCESSING USING PYTHON TOOLS: GEOPANDAS Advanced vector geospatial analytics. Go over all the analytics that have been wrapped into GeoPandas, including Geometric Manipulations, Set-Operations with Overlay, Aggregation with Dissolve and Merging Data. Take a look at PySAL, the Python Spatial Analysis Library. It’s in the conda environment. This is a powerful, multi-faceted

package.

RASTER PROCESSING USING PYTHON TOOLS Raster Formats and Libraries. What sorts of formats are available for representing raster datasets? 10:55. Working with Raster Datasets. How can I extract pixel values from rasters and perform computations? How might I write pixel values out to a new raster file? 11:40. Rainier DEM Example. How can I work with rasters from different sources MACHINE LEARNING: MACHINE LEARNING WITH GIS APPLICATION (A Machine learning is a general term used to apply to many techniques which utilize statistical iteration and feedback so that correlations or logic is learned rather than dictated. Learning itself is the act of gradually improving performance on a task without being explicitly programmed. This process mimics human neurological functions. GOOGLE EARTH ENGINE: GEE ACCESS AND JAVASCRIPT TIPS Please complete this tutorial before arriving at geohackweek. Use the steps below to get registered for a Google Earth Engine account and to join our shared repository. 1. Registering for a Google Earth Engine account. Go the the GEE sign up page and enter > the email you want to use for your GEE account. A gmail is best if you have one. GOOGLE EARTH ENGINE: SUPERVISED CLASSIFICATION OFSEE MORE ON GEOHACKWEEK.GITHUB.IO RASTER PROCESSING USING PYTHON TOOLS: INTRODUCTION TO Raster data is any pixelated (or gridded) data where each pixel is associated with a specific geographical location. The value of a pixel can be continuous (e.g. elevation) or categorical (e.g. land use). If this sounds familiar, it is because this data structure is very common: it’s how we represent any digital image. GOOGLE EARTH ENGINE: TEMPORAL AND SPATIAL REDUCERS Reducers: Overview. In Google Earth Engine (GEE), reducers are used to aggregate data over time, space, and other data structures. They belong to the ee.Reducer class and include summary statistics, histograms, and linear regression, among others. Here’s a visual from Google demonstrating a reducer applied to an ImageCollection: GOOGLE EARTH ENGINE: TIME SERIES Add the time series plots to the panels. Now that we have set up our user interface and built the call-back, we can define a time series chart. The chart uses the lat/long selected by the user and builds a time series for NDVI or EVI at that point. It takes the average NDVI or EVI at that point, extracts it, and then adds it to the time

series.

VECTOR DATA PROCESSING USING PYTHON TOOLS 11:15. GeoPandas Advanced Topics. What additional capabilities does GeoPandas provide, including data access, plotting and analysis? How does it integrate with other common Python tools? How do GeoPandas data objects integrate with analyses of raster data over vector geospatial features? 11:50. OpenStreetMap data access and processing. MACHINE LEARNING: SUPERVISED LEARNING: TREE-BASED METHODSSEE MORE ON GEOHACKWEEK.GITHUB.IO VECTOR DATA PROCESSING USING PYTHON TOOLS: GEOPANDAS Advanced vector geospatial analytics. Go over all the analytics that have been wrapped into GeoPandas, including Geometric Manipulations, Set-Operations with Overlay, Aggregation with Dissolve and Merging Data. Take a look at PySAL, the Python Spatial Analysis Library. It’s in the conda environment. This is a powerful, multi-faceted

package.

RASTER PROCESSING USING PYTHON TOOLS Raster Formats and Libraries. What sorts of formats are available for representing raster datasets? 10:55. Working with Raster Datasets. How can I extract pixel values from rasters and perform computations? How might I write pixel values out to a new raster file? 11:40. Rainier DEM Example. How can I work with rasters from different sources MACHINE LEARNING: MACHINE LEARNING WITH GIS APPLICATION (A Machine learning is a general term used to apply to many techniques which utilize statistical iteration and feedback so that correlations or logic is learned rather than dictated. Learning itself is the act of gradually improving performance on a task without being explicitly programmed. This process mimics human neurological functions. GOOGLE EARTH ENGINE: GEE ACCESS AND JAVASCRIPT TIPS Please complete this tutorial before arriving at geohackweek. Use the steps below to get registered for a Google Earth Engine account and to join our shared repository. 1. Registering for a Google Earth Engine account. Go the the GEE sign up page and enter > the email you want to use for your GEE account. A gmail is best if you have one. GOOGLE EARTH ENGINE: SUPERVISED CLASSIFICATION OFSEE MORE ON GEOHACKWEEK.GITHUB.IO RASTER PROCESSING USING PYTHON TOOLS: INTRODUCTION TO Raster data is any pixelated (or gridded) data where each pixel is associated with a specific geographical location. The value of a pixel can be continuous (e.g. elevation) or categorical (e.g. land use). If this sounds familiar, it is because this data structure is very common: it’s how we represent any digital image. GOOGLE EARTH ENGINE: TEMPORAL AND SPATIAL REDUCERS Reducers: Overview. In Google Earth Engine (GEE), reducers are used to aggregate data over time, space, and other data structures. They belong to the ee.Reducer class and include summary statistics, histograms, and linear regression, among others. Here’s a visual from Google demonstrating a reducer applied to an ImageCollection: GOOGLE EARTH ENGINE: TIME SERIES Add the time series plots to the panels. Now that we have set up our user interface and built the call-back, we can define a time series chart. The chart uses the lat/long selected by the user and builds a time series for NDVI or EVI at that point. It takes the average NDVI or EVI at that point, extracts it, and then adds it to the time

series.

VECTOR DATA PROCESSING USING PYTHON TOOLS 11:15. GeoPandas Advanced Topics. What additional capabilities does GeoPandas provide, including data access, plotting and analysis? How does it integrate with other common Python tools? How do GeoPandas data objects integrate with analyses of raster data over vector geospatial features? 11:50. OpenStreetMap data access and processing. VECTOR DATA PROCESSING USING PYTHON TOOLS: GEOPANDAS Advanced vector geospatial analytics. Go over all the analytics that have been wrapped into GeoPandas, including Geometric Manipulations, Set-Operations with Overlay, Aggregation with Dissolve and Merging Data. Take a look at PySAL, the Python Spatial Analysis Library. It’s in the conda environment. This is a powerful, multi-faceted

package.

VECTOR DATA PROCESSING USING PYTHON TOOLS: REFERENCE Vector Data Processing using Python Tools. : Reference. A common terminology and set of concepts has been defined by the OGC Simple Feature Access, and is widely used across open source geospatial libraries. Core geometric objects (point, line/linestring and polygon) and their multi-part collections (multi-point, multi-line,

multi-polygon) are

MACHINE LEARNING: MACHINE LEARNING WITH GIS APPLICATION (A Machine learning is a general term used to apply to many techniques which utilize statistical iteration and feedback so that correlations or logic is learned rather than dictated. Learning itself is the act of gradually improving performance on a task without being explicitly programmed. This process mimics human neurological functions. GOOGLE EARTH ENGINE: HOME Introduction to Google Earth Engine. What is Google Earth Engine? What are the strengths and limitations of this platform? 09:05. Code Editor. What are the key features of the online code editor? Where can I go for help while learning GEE? How do I search for and import GOOGLE EARTH ENGINE: REFERENCE Google Earth Engine is a powerful tool for analyzing remote sensing imagery. GEE’s data catalog hosts a formidable variety of earth observation data. GEE is changing how we understand and study the Earth. Code Editor. The Code Editor is a one stop shop for accessing GEE data catalog and conducting geospatial analysis. GEOSPATIAL DATA VISUALIZATION: BASICS: QUICK + SIMPLE MAPS Cartopy 101. For our purposes here, cartopy is a python package which provides a set of tools for creating projection-aware geospatial plots using python’s standard plotting package, matplotlib. Cartopy also has a robust set of tools for defining projections and reprojecting data, which are used under-the-hood in our tutorial, but won’t be GOOGLE EARTH ENGINE: ACCESSING SATELLITE IMAGERY Overview: Satellite Imagery at Regional Scales. Most satellite products are broken up into tiles for distribution. Global Landsat data is broken up in ~180 km 2 scenes, with unique path/row identifiers. 455 scenes cover the United States. Each scene is currently imaged every 16 days by Landsat 8, and every 16 days by Landsat 7 (approximately 45 times each year). GOOGLE EARTH ENGINE: TIME SERIES Add the time series plots to the panels. Now that we have set up our user interface and built the call-back, we can define a time series chart. The chart uses the lat/long selected by the user and builds a time series for NDVI or EVI at that point. It takes the average NDVI or EVI at that point, extracts it, and then adds it to the time

series.

INTRODUCTORY MATERIAL: INTRODUCTION TO CONDA To know the current environment that you’re in you can either look at your terminal: (test_env) D-69-91-135-15:env_files lsetiawan$. The (test_env) in the beginning of the line indicates that I’m curently using the test_env conda environment. Another way that you can check for your current active environment is a command: $ conda env list. VECTOR DATA PROCESSING USING PYTHON TOOLS 11:15. GeoPandas Advanced Topics. What additional capabilities does GeoPandas provide, including data access, plotting and analysis? How does it integrate with other common Python tools? How do GeoPandas data objects integrate with analyses of raster data over vector geospatial features? 11:50. OpenStreetMap data access and processing. MACHINE LEARNING: SUPERVISED LEARNING: TREE-BASED METHODSSEE MORE ON GEOHACKWEEK.GITHUB.IO VECTOR DATA PROCESSING USING PYTHON TOOLS: GEOPANDAS Advanced vector geospatial analytics. Go over all the analytics that have been wrapped into GeoPandas, including Geometric Manipulations, Set-Operations with Overlay, Aggregation with Dissolve and Merging Data. Take a look at PySAL, the Python Spatial Analysis Library. It’s in the conda environment. This is a powerful, multi-faceted

package.

RASTER PROCESSING USING PYTHON TOOLS Raster Formats and Libraries. What sorts of formats are available for representing raster datasets? 10:55. Working with Raster Datasets. How can I extract pixel values from rasters and perform computations? How might I write pixel values out to a new raster file? 11:40. Rainier DEM Example. How can I work with rasters from different sources MACHINE LEARNING: MACHINE LEARNING WITH GIS APPLICATION (A Machine learning is a general term used to apply to many techniques which utilize statistical iteration and feedback so that correlations or logic is learned rather than dictated. Learning itself is the act of gradually improving performance on a task without being explicitly programmed. This process mimics human neurological functions. GOOGLE EARTH ENGINE: GEE ACCESS AND JAVASCRIPT TIPS Please complete this tutorial before arriving at geohackweek. Use the steps below to get registered for a Google Earth Engine account and to join our shared repository. 1. Registering for a Google Earth Engine account. Go the the GEE sign up page and enter > the email you want to use for your GEE account. A gmail is best if you have one. GOOGLE EARTH ENGINE: SUPERVISED CLASSIFICATION OFSEE MORE ON GEOHACKWEEK.GITHUB.IO RASTER PROCESSING USING PYTHON TOOLS: INTRODUCTION TO Raster data is any pixelated (or gridded) data where each pixel is associated with a specific geographical location. The value of a pixel can be continuous (e.g. elevation) or categorical (e.g. land use). If this sounds familiar, it is because this data structure is very common: it’s how we represent any digital image. GOOGLE EARTH ENGINE: TEMPORAL AND SPATIAL REDUCERS Reducers: Overview. In Google Earth Engine (GEE), reducers are used to aggregate data over time, space, and other data structures. They belong to the ee.Reducer class and include summary statistics, histograms, and linear regression, among others. Here’s a visual from Google demonstrating a reducer applied to an ImageCollection: GOOGLE EARTH ENGINE: TIME SERIES Add the time series plots to the panels. Now that we have set up our user interface and built the call-back, we can define a time series chart. The chart uses the lat/long selected by the user and builds a time series for NDVI or EVI at that point. It takes the average NDVI or EVI at that point, extracts it, and then adds it to the time

series.

VECTOR DATA PROCESSING USING PYTHON TOOLS 11:15. GeoPandas Advanced Topics. What additional capabilities does GeoPandas provide, including data access, plotting and analysis? How does it integrate with other common Python tools? How do GeoPandas data objects integrate with analyses of raster data over vector geospatial features? 11:50. OpenStreetMap data access and processing. MACHINE LEARNING: SUPERVISED LEARNING: TREE-BASED METHODSSEE MORE ON GEOHACKWEEK.GITHUB.IO VECTOR DATA PROCESSING USING PYTHON TOOLS: GEOPANDAS Advanced vector geospatial analytics. Go over all the analytics that have been wrapped into GeoPandas, including Geometric Manipulations, Set-Operations with Overlay, Aggregation with Dissolve and Merging Data. Take a look at PySAL, the Python Spatial Analysis Library. It’s in the conda environment. This is a powerful, multi-faceted

package.

RASTER PROCESSING USING PYTHON TOOLS Raster Formats and Libraries. What sorts of formats are available for representing raster datasets? 10:55. Working with Raster Datasets. How can I extract pixel values from rasters and perform computations? How might I write pixel values out to a new raster file? 11:40. Rainier DEM Example. How can I work with rasters from different sources MACHINE LEARNING: MACHINE LEARNING WITH GIS APPLICATION (A Machine learning is a general term used to apply to many techniques which utilize statistical iteration and feedback so that correlations or logic is learned rather than dictated. Learning itself is the act of gradually improving performance on a task without being explicitly programmed. This process mimics human neurological functions. GOOGLE EARTH ENGINE: GEE ACCESS AND JAVASCRIPT TIPS Please complete this tutorial before arriving at geohackweek. Use the steps below to get registered for a Google Earth Engine account and to join our shared repository. 1. Registering for a Google Earth Engine account. Go the the GEE sign up page and enter > the email you want to use for your GEE account. A gmail is best if you have one. GOOGLE EARTH ENGINE: SUPERVISED CLASSIFICATION OFSEE MORE ON GEOHACKWEEK.GITHUB.IO RASTER PROCESSING USING PYTHON TOOLS: INTRODUCTION TO Raster data is any pixelated (or gridded) data where each pixel is associated with a specific geographical location. The value of a pixel can be continuous (e.g. elevation) or categorical (e.g. land use). If this sounds familiar, it is because this data structure is very common: it’s how we represent any digital image. GOOGLE EARTH ENGINE: TEMPORAL AND SPATIAL REDUCERS Reducers: Overview. In Google Earth Engine (GEE), reducers are used to aggregate data over time, space, and other data structures. They belong to the ee.Reducer class and include summary statistics, histograms, and linear regression, among others. Here’s a visual from Google demonstrating a reducer applied to an ImageCollection: GOOGLE EARTH ENGINE: TIME SERIES Add the time series plots to the panels. Now that we have set up our user interface and built the call-back, we can define a time series chart. The chart uses the lat/long selected by the user and builds a time series for NDVI or EVI at that point. It takes the average NDVI or EVI at that point, extracts it, and then adds it to the time

series.

VECTOR DATA PROCESSING USING PYTHON TOOLS 11:15. GeoPandas Advanced Topics. What additional capabilities does GeoPandas provide, including data access, plotting and analysis? How does it integrate with other common Python tools? How do GeoPandas data objects integrate with analyses of raster data over vector geospatial features? 11:50. OpenStreetMap data access and processing. VECTOR DATA PROCESSING USING PYTHON TOOLS: GEOPANDAS Advanced vector geospatial analytics. Go over all the analytics that have been wrapped into GeoPandas, including Geometric Manipulations, Set-Operations with Overlay, Aggregation with Dissolve and Merging Data. Take a look at PySAL, the Python Spatial Analysis Library. It’s in the conda environment. This is a powerful, multi-faceted

package.

VECTOR DATA PROCESSING USING PYTHON TOOLS: REFERENCE Vector Data Processing using Python Tools. : Reference. A common terminology and set of concepts has been defined by the OGC Simple Feature Access, and is widely used across open source geospatial libraries. Core geometric objects (point, line/linestring and polygon) and their multi-part collections (multi-point, multi-line,

multi-polygon) are

MACHINE LEARNING: MACHINE LEARNING WITH GIS APPLICATION (A Machine learning is a general term used to apply to many techniques which utilize statistical iteration and feedback so that correlations or logic is learned rather than dictated. Learning itself is the act of gradually improving performance on a task without being explicitly programmed. This process mimics human neurological functions. GOOGLE EARTH ENGINE: HOME Introduction to Google Earth Engine. What is Google Earth Engine? What are the strengths and limitations of this platform? 09:05. Code Editor. What are the key features of the online code editor? Where can I go for help while learning GEE? How do I search for and import GOOGLE EARTH ENGINE: REFERENCE Google Earth Engine is a powerful tool for analyzing remote sensing imagery. GEE’s data catalog hosts a formidable variety of earth observation data. GEE is changing how we understand and study the Earth. Code Editor. The Code Editor is a one stop shop for accessing GEE data catalog and conducting geospatial analysis. GEOSPATIAL DATA VISUALIZATION: BASICS: QUICK + SIMPLE MAPS Cartopy 101. For our purposes here, cartopy is a python package which provides a set of tools for creating projection-aware geospatial plots using python’s standard plotting package, matplotlib. Cartopy also has a robust set of tools for defining projections and reprojecting data, which are used under-the-hood in our tutorial, but won’t be GOOGLE EARTH ENGINE: ACCESSING SATELLITE IMAGERY Overview: Satellite Imagery at Regional Scales. Most satellite products are broken up into tiles for distribution. Global Landsat data is broken up in ~180 km 2 scenes, with unique path/row identifiers. 455 scenes cover the United States. Each scene is currently imaged every 16 days by Landsat 8, and every 16 days by Landsat 7 (approximately 45 times each year). GOOGLE EARTH ENGINE: TIME SERIES Add the time series plots to the panels. Now that we have set up our user interface and built the call-back, we can define a time series chart. The chart uses the lat/long selected by the user and builds a time series for NDVI or EVI at that point. It takes the average NDVI or EVI at that point, extracts it, and then adds it to the time

series.

INTRODUCTORY MATERIAL: INTRODUCTION TO CONDA To know the current environment that you’re in you can either look at your terminal: (test_env) D-69-91-135-15:env_files lsetiawan$. The (test_env) in the beginning of the line indicates that I’m curently using the test_env conda environment. Another way that you can check for your current active environment is a command: $ conda env list.

GEOHACKWEEK 2019

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GEOHACKWEEK 2019

Workshop On Geospatial Data Science University of Washington eScience Institute

Sept 9 - 13, 2019

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ABOUT GEOHACKWEEK

Geohackweek is a 5-day hackweek to be held at the University of Washington eScience Institute. Participants will learn about open source technologies used to analyze geospatial datasets. Mornings will consist of interactive lectures, and afternoon sessions will involve facilitated exploration of datasets and hands-on software development. See 2018 Geohackweek. | Learn

about "hackweek".

INFORMATION FOR APPLICANTS To best benefit from the program, participants are expected to have some experience with Python programming and with analysis of geospatial data (e.g. remote sensing analysis, vector mapping, environmental modeling, etc.). Successful applicants will pay a $100 registration fee and be expected to cover lodging and travel expenses. Financial support may be available based on need.

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