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<!--
-->
<!DOCTYPE html>
<html>
<head>
<title>Introduction to geospatial data analysis with GeoPandas and the PyData stack</title>
<meta charset="utf-8">
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<textarea id="source">
class: center, middle
# Introduction to geospatial data analysis with GeoPandas and the PyData stack
Joris Van den Bossche, GeoPython conference, June 24, 2019
https://github.com/jorisvandenbossche/geopandas-tutorial
---
# About me
Joris Van den Bossche
- Background: PhD bio-science engineer, air quality research
- Open source enthusiast: pandas core dev, geopandas maintainer, scikit-learn contributor
- Currently freelance open source software developer and teacher + working for Ursa Labs on Apache Arrow
https://github.com/jorisvandenbossche Twitter: [@jorisvdbossche](https://twitter.com/jorisvdbossche)
<div style="margin-bottom:-20px"></div>
<!-- .affiliations[
] -->
---
# Raster vs vector data
--
count: false
.right[
### -> in this tutorial: focus on vector data
]
--
count: false
.right[
### -> simple features (points, linestrings, polygons) with attributes
]
???
Two major families of geospatial data
raster: grid based (topology lacking, difficult to link to tabular data)
vector: coordinate based objects, topological
here: vector
vector -> common abstraction model in many software
Open Geospatial consortium standard
Attributes : each vector feature can have a record in attribute table
and that is where geopandas comes into play
but before talking about geopandas, first a bit more general about open source geospatial software
# geospatial software
This presentation: in python
but everything I will present -> builds upon widely used open source libraries
---
class: middle, center
# Open source geospatial software
.center[
]
???
Open Source Geospatial Foundation
OSGeo was created to support the collaborative development of open source geospatial software, and promote its widespread use.
---
# GDAL / OGR
### Geospatial Data Abstraction Library.
<img style="position: absolute; top: 12px; right: 20px; height:35%" src="img/GDALLogoColor.svg">
* The swiss army knife for geospatial.
* Read and write Raster (GDAL) and Vector (OGR) datasets
* More than 200 (mainly) geospatial formats and protocols.
.center[
]
.credits[
Slide from "GDAL 2.2 What's new?" by Even Rouault (CC BY-SA)
]
???
GDAL is a translator library for raster and vector geospatial data formats. As a library, it presents a single raster abstract data model and single vector abstract data model to the calling application for all supported formats. It also comes with a variety of useful command line utilities for data translation and processing.
<!--
# GDAL / OGR
### Widely used (FOSS & proprietary)
.center[
]
.credits[
Slide from "GDAL 2.2 What's new?" by Even Rouault (CC BY-SA)
] -->
---
# GEOS
<img style="position: absolute; top: 20px; right: 20px; width:40%" src="img/geos.gif">
## Geometry Engine Open Source
* C/C++ port of a subset of Java Topology Suite (JTS)
* Most widely used geospatial C++ geometry library
* Implements geometry objects (simple features), spatial predicate functions and spatial operations
Used under the hood by many applications (QGIS, PostGIS, MapServer, GRASS, GeoDjango, ...)
[geos.osgeo.org](http://geos.osgeo.org)
<!-- ---
# PROJ.4
C library for performing conversions between cartographic projections.
[http://proj4.org/](http://proj4.org/) -->
---
# Python geospatial packages
--
count:false
Interfaces to widely used libraries:
- Python bindings to GDAL/OGR (`from osgeo import gdal, ogr`)
- [`pyproj`](https://jswhit.github.io/pyproj/): python interface to PROJ.4.
- Pythonic binding to GDAL/OGR:
- [`rasterio`](https://mapbox.github.io/rasterio/) for GDAL
- [`fiona`](http://toblerity.org/fiona/README.html) for OGR
- [`shapely`](https://shapely.readthedocs.io/en/latest/): python package based on GEOS.
---
# Shapely
Python package for the manipulation and analysis of geometric objects
<div style="margin-bottom:-10px"></div>
Pythonic interface to GEOS
--
count:false
.mmedium[
```python
>>> from shapely.geometry import Point, LineString, Polygon
>>> point = Point(1, 1)
>>> line = LineString([(0, 0), (1, 2), (2, 2)])
>>> poly = line.buffer(1)
```
]
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.mmedium[
```python
>>> poly.contains(point)
True
```
]
--
count: false
Nice interface to GEOS, but: single objects, no attributes
???
# Shapely
typical predicates and operations
(images from shapely docs)
---
One of the packages driving the growing popularity of Python for data science, machine learning and academic research
* High-performance, easy-to-use data structures and tools
* Suited for tabular data (e.g. columnar data, spread-sheets, database tables)
```python
import pandas as pd
df = pd.read_csv("myfile.csv")
subset = df[df['value'] > 0]
subset.groupby('key').mean()
```
---
# GeoPandas
Make working with geospatial data in python easier
* Started by Kelsey Jordahl in 2013
* Extends the pandas data analysis library to work with geographic objects and spatial operations
* Combines the power of whole ecosystem of (geo) tools (pandas, geos, shapely, gdal, fiona, pyproj, rtree, ...)
Documentation: http://geopandas.readthedocs.io/
???
make working with geospatial data like working with any other kind of data in python
(data stack, numpy, pandas and other tools around those)
analysis for which you otherwise would need desktop GIS applications (QGIS, ArcGIS) or geospatial databases (PostGIS)
makes pandas objects geometry aware
---
# Summary
* Read and write variety of formats (fiona, GDAL/OGR)
* Familiar manipulation of the attributes (pandas dataframe)
* Element-wise spatial predicates (intersects, within, ...) and operations (intersection, union, difference, ..) (shapely)
* Re-project your data (pyproj)
* Quickly visualize the geometries (matplotlib, descartes)
* More advanced spatial operations: spatial joins and overlays (rtree)
--
count:false
**-> Interactive exploration and analysis of geospatial data**
---
# Ecosystem
[geoplot](http://www.residentmar.io/geoplot/index.html) (high-level geospatial visualization), [cartopy](http://scitools.org.uk/cartopy/) (projection aware cartographic library)
[folium](https://github.com/python-visualization/folium) (Leaflet.js maps)
[OSMnx](http://geoffboeing.com/2016/11/osmnx-python-street-networks/) (python for street networks)
[PySAL](http://pysal.readthedocs.io/en/latest/index.html) (Python Spatial Analysis Library)
[rasterio](https://mapbox.github.io/rasterio/) (working with geospatial raster data)
...
---
class: middle
http://geopandas.readthedocs.io
# Thanks for listening!
## Thanks to all contributors!
## Those slides:
- https://github.com/jorisvandenbossche/talks/
- [jorisvandenbossche.github.io/talks/2018_FOSDEM_geopandas](
http://jorisvandenbossche.github.io/talks/2018_FOSDEM_geopandas)
http://geopandas.readthedocs.io
</textarea>
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