```
import pandas as pd
import shapely
import geopandas as gpd
```

# 1 Geographic data in Python

## 1.1 Introduction

This chapter outlines two fundamental geographic data models — vector and raster — and introduces the main Python packages for working with them. Before demonstrating their implementation in Python, we will introduce the theory behind each data model and the disciplines in which they predominate.

The vector data model (Section 1.2) represents the world using points, lines, and polygons. These have discrete, well-defined borders, meaning that vector datasets usually have a high level of precision (but not necessarily accuracy). The raster data model (Section 1.3), on the other hand, divides the surface up into cells of constant size. Raster datasets are the basis of background images used in web-mapping and have been a vital source of geographic data since the origins of aerial photography and satellite-based remote sensing devices. Rasters aggregate spatially specific features to a given resolution, meaning that they are consistent over space and scalable, with many worldwide raster datasets available.

Which to use? The answer likely depends on your domain of application, and the datasets you have access to:

- Vector datasets and methods dominate the social sciences because human settlements and and processes (e.g., transport infrastructure) tend to have discrete borders
- Raster datasets and methods dominate many environmental sciences because of the reliance on remote sensing data

Python has strong support for both data models. We will focus on **shapely** and **geopandas** for working with geograpic vector data, and **rasterio** for working with rasters.

**shapely** is a “low-level” package for working with individual vector geometry objects. **geopandas** is a “high-level” package for working with geometry columns (`GeoSeries`

objects), which internally contain **shapely** geometries, and vector layers (`GeoDataFrame`

objects). The **geopandas** ecosystem provides a comprehensive approach for working with vector layers in Python, with many packages building on it.

There are several partially overlapping packages for working with raster data, each with its own advantages and disadvantages. In this book, we focus on the most prominent one: **rasterio**, which represents “simple” raster datasets with a combination of a **numpy** array, and a metadata object (`dict`

) providing geographic metadata such as the coordinate system. **xarray** is a notable alternative to **rasterio** not covered in this book which uses native `xarray.Dataset`

and `xarray.DataArray`

classes to effectively represent complex raster datasets such as ‘NetCDF’ files with multiple bands and metadata.

There is much overlap in some fields and raster and vector datasets can be used together: ecologists and demographers, for example, commonly use both vector and raster data. Furthermore, it is possible to convert between the two forms (see Chapter 5). Whether your work involves more use of vector or raster datasets, it is worth understanding the underlying data models before using them, as discussed in subsequent chapters.

## 1.2 Vector data

The geographic vector data model is based on points located within a coordinate reference system (CRS). Points can represent self-standing features (e.g., the location of a bus stop), or they can be linked together to form more complex geometries such as lines and polygons. Most point geometries contain only two dimensions (3-dimensional CRSs may contain an additional \(z\) value, typically representing height above sea level).

In this system, London, for example, can be represented by the coordinates `(-0.1,51.5)`

. This means that its location is -0.1 degrees east and 51.5 degrees north of the origin. The origin, in this case, is at 0 degrees longitude (a prime meridian located at Greenwich) and 0 degrees latitude (the Equator) in a geographic (‘lon/lat’) CRS (Figure 1.1, left panel). The same point could also be approximated in a projected CRS with ‘Easting/Northing’ values of `(530000, 180000)`

in the British National Grid, meaning that London is located 530 \(km\) East and 180 \(km\) North of the origin of the CRS (Figure 1.1, right panel). The location of National Grid’s origin, in the sea beyond South West Peninsular, ensures that most locations in the UK have positive Easting and Northing values.

There is more to CRSs, as described in Section 1.4 and Chapter 6 but, for the purposes of this section, it is sufficient to know that coordinates consist of two numbers representing the distance from an origin, usually in \(x\) then \(y\) dimensions.

**geopandas** (Bossche et al. 2023) provides classes for geographic vector data and a consistent command-line interface for reproducible geographic data analysis in Python. It also provides an interface to three mature libraries for geocomputation which, in combination, represent a strong foundation on which many geographic applications (including QGIS and R’s spatial ecosystem):

- GDAL, for reading, writing, and manipulating a wide range of geographic data formats, covered in Chapter 7
- PROJ, a powerful library for coordinate system transformations, which underlies the content covered in Chapter 6
- GEOS, a planar geometry engine for operations such as calculating buffers and centroids on data with a projected CRS, covered in Chapter 4

Tight integration with these geographic libraries makes reproducible geocomputation possible: an advantage of using a higher level language such as Python to access these libraries is that you do not need to know the intricacies of the low level components, enabling focus on the methods rather than the implementation.

### 1.2.1 Vector data classes

The main classes for working with geographic vector data in Python are hierarchical, meaning the highest level ‘vector layer’ class is composed of simpler ‘geometry column’ and individual ‘geometry’ components. This section introduces them in order, starting with the highest level class. For many applications, the high level vector layer class, which are essentially a data frame with geometry columns, are all that’s needed. However, it’s important to understand the structure of vector geographic objects and their component pieces for more advanced applications. The three main vector geographic data classes in Python are:

`GeoDataFrame`

, a class representing vector layers, with a geometry column (class`GeoSeries`

) as one of the columns`GeoSeries`

, a class that is used to represent the geometry column in`GeoDataFrame`

objects`shapely`

geometry objects which represent individual geometries, such as a point or a polygon

The first two classes (`GeoDataFrame`

and `GeoSeries`

) are defined in **geopandas**. The third class is defined in the **shapely** package, which deals with individual geometries, and is a main dependency of the **geopandas** package.

### 1.2.2 Vector layers

The most commonly used geographic vector data structure is the vector layer. There are several approaches for working with vector layers in Python, ranging from low-level packages (e.g., **osgeo**, **fiona**) to the relatively high-level **geopandas** package that is the focus of this section. Before writing and running code for creating and working with geographic vector objects, we need to import **geopandas** (by convention as `gpd`

for more concise code) and **shapely**.

We also limit the maximum number of printed rows to six, to save space, using the `'display.max_rows'`

option of **pandas**.

`'display.max_rows', 6) pd.set_option(`

Projects often start by importing an existing vector layer saved as a GeoPackage (`.gpkg`

) file, an ESRI Shapefile (`.shp`

), or other geographic file format. The function `read_file()`

imports a GeoPackage file named `world.gpkg`

located in the `data`

directory of Python’s working directory into a `GeoDataFrame`

named `gdf`

.

`= gpd.read_file('data/world.gpkg') gdf `

The result is an object of type (class) `GeoDataFrame`

with 177 rows (features) and 11 columns, as shown in the output of the following code:

`type(gdf)`

`geopandas.geodataframe.GeoDataFrame`

` gdf.shape`

`(177, 11)`

The `GeoDataFrame`

class is an extension of the `DataFrame`

class from the popular **pandas** package (McKinney 2010). This means we can treat non-spatial attributes from a vector layer as a table, and process them using the ordinary, i.e., non-spatial, established function methods. For example, standard data frame subsetting methods can be used. The code below creates a subset of the `gdf`

dataset containing only the country name and the geometry.

```
= gdf[['name_long', 'geometry']]
gdf gdf
```

name_long | geometry | |
---|---|---|

0 | Fiji | MULTIPOLY... |

1 | Tanzania | MULTIPOLY... |

2 | Western S... | MULTIPOLY... |

... | ... | ... |

174 | Kosovo | MULTIPOLY... |

175 | Trinidad ... | MULTIPOLY... |

176 | South Sudan | MULTIPOLY... |

177 rows × 2 columns

The following expression creates a subdataset based on a condition, such as equality of the value in the `'name_long'`

column to the string `'Egypt'`

.

`'name_long'] == 'Egypt'] gdf[gdf[`

name_long | geometry | |
---|---|---|

163 | Egypt | MULTIPOLY... |

Finally, to get a sense of the spatial component of the vector layer, it can be plotted using the `.plot`

method (Figure 1.2).

`; gdf.plot()`

Interactive maps of `GeoDataFrame`

objects can be created with the `.explore`

method, as illustrated in Figure 1.3 which was created with the following command:

` gdf.explore()`

A subset of the data can be also plotted in a similar fashion.

`'name_long'] == 'Egypt'].explore() gdf[gdf[`

### 1.2.3 Geometry columns

The geometry column of class `GeoSeries`

is an essential column in a `GeoDataFrame`

. It contains the geometric part of the vector layer, and is the basis for all spatial operations. This column can be accessed by name, which typically (e.g., when reading from a file) is `'geometry'`

, as in `gdf['geometry']`

. However, the recommendation is to use the fixed `.geometry`

property, which refers to the geometry column regardless whether its name is `'geometry'`

or not. In the case of the `gdf`

object, the geometry column contains `'MultiPolygon'`

s associated with each country.

` gdf.geometry`

```
0 MULTIPOLY...
1 MULTIPOLY...
2 MULTIPOLY...
...
174 MULTIPOLY...
175 MULTIPOLY...
176 MULTIPOLY...
Name: geometry, Length: 177, dtype: geometry
```

The geometry column also contains the spatial reference information, if any (also accessible with the shortcut `gdf.crs`

).

` gdf.geometry.crs`

```
<Geographic 2D CRS: EPSG:4326>
Name: WGS 84
Axis Info [ellipsoidal]:
- Lat[north]: Geodetic latitude (degree)
- Lon[east]: Geodetic longitude (degree)
Area of Use:
- name: World.
- bounds: (-180.0, -90.0, 180.0, 90.0)
Datum: World Geodetic System 1984 ensemble
- Ellipsoid: WGS 84
- Prime Meridian: Greenwich
```

Many geometry operations, such as calculating the centroid, buffer, or bounding box of each feature involve just the geometry. Applying this type of operation on a `GeoDataFrame`

is therefore basically a shortcut to applying it on the `GeoSeries`

object in the geometry column. For example, the two following commands return exactly the same result, a `GeoSeries`

with country bounding box polygons (using the `.envelope`

method).

` gdf.envelope`

```
0 POLYGON (...
1 POLYGON (...
2 POLYGON (...
...
174 POLYGON (...
175 POLYGON (...
176 POLYGON (...
Length: 177, dtype: geometry
```

` gdf.geometry.envelope`

```
0 POLYGON (...
1 POLYGON (...
2 POLYGON (...
...
174 POLYGON (...
175 POLYGON (...
176 POLYGON (...
Length: 177, dtype: geometry
```

Note that `.envelope`

, and other similar operators in **geopandas** such as `.centroid`

(Section 4.2.2), `.buffer`

(Section 4.2.3) or `.convex_hull`

, return only the geometry (i.e., a `GeoSeries`

), not a `GeoDataFrame`

with the original attribute data. In case we want the latter, we can create a copy of the `GeoDataFrame`

and then “overwrite” its geometry (or, we can overwrite the geometries directly in case we do not need the original ones, as in `gdf.geometry=gdf.envelope`

).

```
= gdf.copy()
gdf2 = gdf.envelope
gdf2.geometry gdf2
```

name_long | geometry | |
---|---|---|

0 | Fiji | POLYGON (... |

1 | Tanzania | POLYGON (... |

2 | Western S... | POLYGON (... |

... | ... | ... |

174 | Kosovo | POLYGON (... |

175 | Trinidad ... | POLYGON (... |

176 | South Sudan | POLYGON (... |

177 rows × 2 columns

Another useful property of the geometry column is the geometry type, as shown in the following code. Note that the types of geometries contained in a geometry column (and, thus, a vector layer) are not necessarily the same for every row. Accordingly, the `.type`

property returns a `Series`

(of type `string`

), rather than a single value (the same can be done with the shortcut `gdf.geom_type`

).

`type gdf.geometry.`

```
0 MultiPolygon
1 MultiPolygon
2 MultiPolygon
...
174 MultiPolygon
175 MultiPolygon
176 MultiPolygon
Length: 177, dtype: object
```

To summarize the occurrence of different geometry types in a geometry column, we can use the **pandas** method called `value_counts`

.

`type.value_counts() gdf.geometry.`

```
MultiPolygon 177
Name: count, dtype: int64
```

It is possible to have multiple geometry types in a single `GeoSeries`

. However, in this case, we see that the `gdf`

layer contains only `'MultiPolygon'`

geometries.

A `GeoDataFrame`

can also have multiple `GeoSeries`

.

```
'bbox'] = gdf.envelope
gdf['polygon'] = gdf.geometry
gdf[ gdf
```

Only one geometry column at a time is “active”, in the sense that it is being accessed in operations involving the geometries (such as `.centroid`

, `.crs`

, etc.). To switch the active geometry column from one `GeoSeries`

column to another, we use `set_geometry`

. Figure 1.5 and Figure 1.6 shows interactive maps of the `gdf`

layer with the `'bbox'`

and `'polygon'`

geometry columns activated, respectively.

```
= gdf.set_geometry('bbox')
gdf gdf.explore()
```

```
= gdf.set_geometry('polygon')
gdf gdf.explore()
```