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How To Get Open Street Map Data with Python
How To Get Open Street Map Data Using Python When your app needs to know more about the world around us
Have you ever been working on a project where you need some real-world geographical location data to, for example, say, how many highways cross through this particular city or how many restaurants are in this particular locality?
OpenStreetMap is a great open-source map of the world that can give us some insight into these and similar questions. There is a lot of data hidden in this data set, full of useful labels and geographic information.
The OpenStreetData Model
Let’s have a look at how OSM is structured.
We have three basic components in the OSM data model, which are nodes, ways, and relations, that all come with an ID. Many of the elements come with tags that describe specific features represented as key-value pairs.
In simple terms, nodes are points on the maps (in latitude and longitude) as in the next image of a well documented India Gate in Delhi.
Another way is an ordered list of nodes, which could correspond to a street or the outline of a house. Here is an example of NH 24 in India.
The final data element is a relation which is also an ordered list containing either nodes, ways, or even other relations.
It is used to model logical or geographic relationships between objects. This can be used for example for large structures like the Parliament of India which contains multiple polygons to describe the building.
Using the Overpass API
Now we’ll take a look at how to load data from OSM. The Overpass API uses a custom query language to define the queries.
It takes some time getting used to, but luckily there is Overpass Turbo by Martin Raifer which comes in handy to interactively evaluate our queries directly in the browser.
Let’s say you want to query nodes for cafes, then your query looks like this:
node["amenity"="cafe"]({{bbox}}); out;
Where each statement in the query source code ends with a semicolon. This query starts by specifying the component we want to query, which is, in this case, a node.
We are applying a filter by tag on our query which looks for all the nodes where the key-value pair is "amenity"="cafe". There are different options to filter by a tag that can be found in the documentation.
There are a variety of tags to choose from, one common key is amenity which covers various community facilities like a cafe, restaurant, or just a bench. To have an overview of most of the other possible tags in OSM take a look at the OSM map features or taginfo.
Another filter is the bounding box filter where {{bbox}} corresponds to the bounding box in which we want to search and work only in Overpass Turbo.
Otherwise, you can specify a bounding box by (south, west, north, east) in latitude and longitude which can look like:
node["amenity"="pub"]
(53.2987342,-6.3870259,53.4105416,-6.1148829);
out;
Which you can try in Overpass Turbo. As we saw before in the OSM data model, there are also ways and relations that might hold the same attribute.
We can get those as well by using a union block statement, which collects all outputs from the sequence of statements inside a pair of parentheses as in:
( node["amenity"="cafe"]({{bbox}});
way["amenity"="cafe"]({{bbox}});
relation["amenity"="cafe"]({{bbox}});
);
out;
The next way to filter our queries is by element id. Here is the example for the query node(1); out;, which gives us the prime meridian of the world with longitude close to zero.
Another way to filter queries is by area which can be specified like area["ISO3166-1"="GB"][admin_level=2];, which gives us the area for Great Britain.
We can use this now as a filter for the query by adding (area) to our statement as in:
area["ISO3166-1"="GB"][admin_level=2];
node["place"="city"](area);
out;
This query returns all cities in Great Britain. It is also possible to use a relation or a way as an area. In this case, area IDs need to be derived from an existing OSM way by adding 2400000000 to its OSM ID, or, in case of relation, by adding 3600000000.
Note that not all ways/relations have an area counterpart (i.e. those that are tagged with area=no, and most multipolygons and that don’t have a defined name=* will not be part of areas).
If we apply the relation of Great Britain to the previous example, we’ll then get:
area(3600062149);
node["place"="city"](area);
out;
Finally, we can specify the output of the queried data, which is configured by the out action. Until now, we specified the output as out;, but there are various additional values that can be appended.
The first set of values can control the verbosity or the detail of information of the output, such as ids, skel, body(default value), tags, meta, and count as described in the documentation.
Additionally, we can add modifiers for the geocoded information. geom adds the full geometry to each object. This is important when returning relations or ways that have no coordinates associated and you want to get the coordinates of their nodes and ways.
For example, the query rel["ISO3166-1"="GB"][admin_level=2]; out geom; would otherwise not return any coordinates. The value bb adds only the bounding box to each way and relation, and center adds only the center of the same bounding box.
The sort order can be configured by asc and qt, sorting by object ID or by quadtile index respectively, where the latter is significantly faster. Lastly, by adding an integer value, you can set the maximum number of elements to return.
After combining what we have learned so far, we can finally query the location of all Biergarten in Germany.
area["ISO3166-1"="DE"][admin_level=2];
( node["amenity"="biergarten"](area);
way["amenity"="biergarten"](area);
rel["amenity"="biergarten"](area);
);
out center;
Accessing with Python
To access the Overpass API with Python use the overpy package as a wrapper. Here you can see how we can translate the previous example with the overpy package:
import overpy
api = overpy.Overpass()
r = api.query("""
area["ISO3166-1"="DE"][admin_level=2];
(node["amenity"="biergarten"](area);
way["amenity"="biergarten"](area);
rel["amenity"="biergarten"](area);
);
out center;
""")
coords = []
coords += [(float(node.lon), float(node.lat))
for node in r.nodes]
coords += [(float(way.center_lon), float(way.center_lat))
for way in r.ways]
coords += [(float(rel.center_lon), float(rel.center_lat))
for rel in r.relations]
One nice thing about overpy is that it detects the content type (i.e. XML, JSON) from the response. For further information take a look at their documentation.
Thank you for reading !
#Python #Data Science #Machine Learning #Programming
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1619510796
Lambda, Map, Filter functions in python
Welcome to my Blog, In this article, we will learn python lambda function, Map function, and filter function.
Lambda function in python: Lambda is a one line anonymous function and lambda takes any number of arguments but can only have one expression and python lambda syntax is
Syntax: x = lambda arguments : expression
Now i will show you some python lambda function examples:
#python #anonymous function python #filter function in python #lambda #lambda python 3 #map python #python filter #python filter lambda #python lambda #python lambda examples #python map
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top 30 Python Tips and Tricks for Beginners
Welcome to my Blog , In this article, you are going to learn the top 10 python tips and tricks.
1) swap two numbers.
2) Reversing a string in Python.
3) Create a single string from all the elements in list.
4) Chaining Of Comparison Operators.
5) Print The File Path Of Imported Modules.
6) Return Multiple Values From Functions.
7) Find The Most Frequent Value In A List.
8) Check The Memory Usage Of An Object.
…
#python #python hacks tricks #python learning tips #python programming tricks #python tips #python tips and tricks #python tips and tricks advanced #python tips and tricks for beginners #python tips tricks and techniques #python tutorial #tips and tricks in python #tips to learn python #top 30 python tips and tricks for beginners
1620466520
Your Data Architecture: Simple Best Practices for Your Data Strategy
If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.
If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.
In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.
#big data #data science #big data analytics #data analysis #data architecture #data transformation #data platform #data strategy #cloud data platform #data acquisition
1593156510
Basic Data Types in Python | Python Web Development For Beginners
At the end of 2019, Python is one of the fastest-growing programming languages. More than 10% of developers have opted for Python development.
In the programming world, Data types play an important role. Each Variable is stored in different data types and responsible for various functions. Python had two different objects, and They are mutable and immutable objects.
Table of Contents hide
III Built-in data types in Python
Mutable objects
The Size and declared value and its sequence of the object can able to be modified called mutable objects.
Mutable Data Types are list, dict, set, byte array
Immutable objects
The Size and declared value and its sequence of the object can able to be modified.
Immutable data types are int, float, complex, String, tuples, bytes, and frozen sets.
id() and type() is used to know the Identity and data type of the object
a**=25+**85j
type**(a)**
output**:<class’complex’>**
b**={1:10,2:“Pinky”****}**
id**(b)**
output**:**238989244168
Built-in data types in Python
a**=str(“Hello python world”)****#str**
b**=int(18)****#int**
c**=float(20482.5)****#float**
d**=complex(5+85j)****#complex**
e**=list((“python”,“fast”,“growing”,“in”,2018))****#list**
f**=tuple((“python”,“easy”,“learning”))****#tuple**
g**=range(10)****#range**
h**=dict(name=“Vidu”,age=36)****#dict**
i**=set((“python”,“fast”,“growing”,“in”,2018))****#set**
j**=frozenset((“python”,“fast”,“growing”,“in”,2018))****#frozenset**
k**=bool(18)****#bool**
l**=bytes(8)****#bytes**
m**=bytearray(8)****#bytearray**
n**=memoryview(bytes(18))****#memoryview**
Numbers (int,Float,Complex)
Numbers are stored in numeric Types. when a number is assigned to a variable, Python creates Number objects.
#signed interger
age**=**18
print**(age)**
Output**:**18
Python supports 3 types of numeric data.
int (signed integers like 20, 2, 225, etc.)
float (float is used to store floating-point numbers like 9.8, 3.1444, 89.52, etc.)
complex (complex numbers like 8.94j, 4.0 + 7.3j, etc.)
A complex number contains an ordered pair, i.e., a + ib where a and b denote the real and imaginary parts respectively).
String
The string can be represented as the sequence of characters in the quotation marks. In python, to define strings we can use single, double, or triple quotes.
# String Handling
‘Hello Python’
#single (') Quoted String
“Hello Python”
# Double (") Quoted String
“”“Hello Python”“”
‘’‘Hello Python’‘’
# triple (‘’') (“”") Quoted String
In python, string handling is a straightforward task, and python provides various built-in functions and operators for representing strings.
The operator “+” is used to concatenate strings and “*” is used to repeat the string.
“Hello”+“python”
output**:****‘Hello python’**
"python "*****2
'Output : Python python ’
#python web development #data types in python #list of all python data types #python data types #python datatypes #python types #python variable type
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Getting Started With Data Lakes
Frameworks for Efficient Enterprise Analytics
The opportunities big data offers also come with very real challenges that many organizations are facing today. Often, it’s finding the most cost-effective, scalable way to store and process boundless volumes of data in multiple formats that come from a growing number of sources. Then organizations need the analytical capabilities and flexibility to turn this data into insights that can meet their specific business objectives.
This Refcard dives into how a data lake helps tackle these challenges at both ends — from its enhanced architecture that’s designed for efficient data ingestion, storage, and management to its advanced analytics functionality and performance flexibility. You’ll also explore key benefits and common use cases.
Introduction
As technology continues to evolve with new data sources, such as IoT sensors and social media churning out large volumes of data, there has never been a better time to discuss the possibilities and challenges of managing such data for varying analytical insights. In this Refcard, we dig deep into how data lakes solve the problem of storing and processing enormous amounts of data. While doing so, we also explore the benefits of data lakes, their use cases, and how they differ from data warehouses (DWHs).
This is a preview of the Getting Started With Data Lakes Refcard. To read the entire Refcard, please download the PDF from the link above.
#big data #data analytics #data analysis #business analytics #data warehouse #data storage #data lake #data lake architecture #data lake governance #data lake management