1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
|
.. _using_the_shapereader:
Using the cartopy shapereader
=============================
Cartopy provides an object oriented shapefile reader based on top of the
`pyshp`_ module to provide easy, programmatic, access to standard vector datasets.
Cartopy's wrapping of pyshp has the benefit of being pure python, and is therefore
easy to install and extremely portable. However, for heavy duty shapefile I/O `Fiona`_ and
`GeoPandas`_ are highly recommended.
.. _pyshp: https://github.com/GeospatialPython/pyshp
.. _Fiona: https://fiona.readthedocs.io/
.. _GeoPandas: http://geopandas.org/
Detailed API for the shapereader functionality can be found in the :ref:`documentation <api.io.shapereader>`
.. currentmodule:: cartopy.io.shapereader
Helper functions for shapefile acquisition
-------------------------------------------
Cartopy provides an interface for access to frequently used data such as the
`GSHHS <https://www.ngdc.noaa.gov/mgg/shorelines/gshhs.html>`_ dataset and from
the `NaturalEarthData <https://www.naturalearthdata.com/>`_ website.
These interfaces allow the user to define the data programmatically, and if the data does not exist
on disk, it will be retrieved from the appropriate source (normally by
downloading the data from the internet). Currently the interfaces available are
:func:`natural_earth` and :func:`gshhs`.
Using the shapereader
---------------------
We can acquire the countries dataset from Natural Earth found at
https://www.naturalearthdata.com/downloads/110m-cultural-vectors/110m-admin-0-countries/
by using the :func:`natural_earth` function:
.. testcode:: countries
import cartopy.io.shapereader as shpreader
shpfilename = shpreader.natural_earth(resolution='110m',
category='cultural',
name='admin_0_countries')
From here, we can make use of the :class:`Reader` to get the first country
in the shapefile:
.. testcode:: countries
reader = shpreader.Reader(shpfilename)
countries = reader.records()
country = next(countries)
We can get the country's attributes dictionary with the
:data:`Record.attributes` attribute:
.. doctest:: countries
:options: +ELLIPSIS
>>> print type(country.attributes)
<type 'dict'>
>>> print sorted(country.attributes.keys())
['abbrev', ..., 'name_long', ... 'pop_est', ...]
We could now find the 5 least populated countries with:
.. testcode:: countries
reader = shpreader.Reader(shpfilename)
# define a function which returns the population given the country
population = lambda country: country.attributes['pop_est']
# sort the countries by population and get the first 5
countries_by_pop = sorted(reader.records(), key=population)[:5]
Which we can print with
.. doctest:: countries
>>> ', '.join([country.attributes['name_long']
... for country in countries_by_pop])
'Western Sahara, French Southern and Antarctic Lands, Falkland Islands, Antarctica, Greenland'
**Exercises**:
* **SHP.1**: Repeat the last example to show the 4 most populated African countries in to the shapefile.
Hint: Look at the possible attributes to find out which continent a country belongs.
Answer:
.. testcode:: countries
:hide:
reader = shpreader.Reader(shpfilename)
# define a function which can return the population of a given country
population = lambda country: country.attributes['pop_est']
# sort the countries by population
countries_by_pop = sorted(reader.records(), key=population)
# define a function which can return whether a country belongs to Africa
is_african = lambda country: country.attributes['continent'] == 'Africa'
# remove non-African countries
african_countries = filter(is_african, countries_by_pop)
print ', '.join([country.attributes['name_long']
for country in african_countries[-4:]])
.. testoutput:: countries
Democratic Republic of the Congo, Egypt, Ethiopia, Nigeria
* **SHP.2**: Using the countries shapefile, find the most populated country grouped
by the first letter of the "name_long".
Hint: :func:`itertools.groupby` can help with the grouping.
Answer:
.. testcode:: countries
:hide:
import itertools
reader = shpreader.Reader(shpfilename)
# define a function which returns the first letter of a country's name
first_letter = lambda country: country.attributes['name_long'][0]
# define a function which returns the population of a country
population = lambda country: country.attributes['pop_est']
# sort the countries so that the groups come out alphabetically
countries = sorted(reader.records(), key=first_letter)
# group the countries by first letter
for letter, countries in itertools.groupby(countries, key=first_letter):
# print the letter and least populated country
print letter, sorted(countries, key=population)[-1].attributes['name_long']
.. testoutput:: countries
A Argentina
B Brazil
C China
D Democratic Republic of the Congo
E Ethiopia
F France
G Germany
H Hungary
I India
J Japan
K Kenya
L Lao PDR
M Mexico
N Nigeria
O Oman
P Pakistan
Q Qatar
R Russian Federation
S South Africa
T Turkey
U United States
V Vietnam
W Western Sahara
Y Yemen
Z Zimbabwe
|
