OSMnx is a Python package to retrieve, model, analyze, and visualize street networks from OpenStreetMap. OpenStreetMap (OSM) is a collaborative mapping project that provides a free and publicly editable map of the world. OpenStreetMap provides a valuable crowd-sourced database of raw geospatial data for constructing models of urban street networks for scientific analysis

1. Graph Centric Analysis of Road Network Patterns
for CBD’s of Metropolitan Cities in India
Presented by
Punit Hirdilal Sharnagat
Reg.No.: 2020TR10
M.Tech.(Transportation)
MOTILAL NEHRU NATIONAL INSTITUTE OF TECHNOLOGY ALLAHABAD
PRAYAGRAJ 211004, INDIA
Department of Civil Engineering

2. Presentation Overview
PROJECT
Analysis
Methodology
Introduction
Literature review
Objectives
Data collection

3. Introduction
• Cities worldwide comprises a variety of street network patterns and configurations that
affect human mobility, equity, health, and livelihoods.
• Street networks organize and structure human spatial dynamics and flows in a city.
• Street network analysis has become an important bridge between graph theory and
urban morphology and planning.
• Different street structures result in varying levels of efficiency, accessibility, and usage
of the transportation infrastructure.
• These structural properties have uncovered unique characteristics of different cities as
well as demonstrated hidden statistical commonalities manifested as scale invariant
patterns across different urban context.

4. Introduction
• With the availability of crowd sourced OpenStreetMap (OSM) Dataset, study of
network patterns is increasingly becoming focus area to understand the road patterns
and deficits related to transportation network development.
• With the advent of OSM dataset, new tools have been developed to derive the new
parameters like degrees of street junctions, lengths of road and network centrality that
can reveal the region wise road patterns.

5. Introduction
• To Study number
Tool Focus Type Language OS License Reference
Urban Network
Analysis toolbox
Network analysis
ArcMap plug-in,
Rhino3D plug-in
Python Windows
CC BY-NC-SA
3.0
Sevtsuk (2018); Sevtsuk
and Mekonnen (2012)
Metropolitan Form
Analysis toolbox
City footprint,
land use
ArcMap plug-in Python Windows CC BY-ND 4.0
Amindarbari and Sevtsuk,
2013
Multiple Centrality
Assessment
Network analysis Standalone Python Cross-platform unknown Gasser and Caillet (2013)
depthmapX Network analysis
Standalone, QGIS
plug-in, CLI, R
API
C++, R Cross-platform CC-GNU GPL
depthmapX Development
Team (2017); Turner, 2021
Place Syntax Tool Network analysis
QGIS plug-in,
MapInfo plug-in
C++,
Python
Cross-platform GPL-3.0
Ståhle Marcus and
Karlström (2005)
AwaP-IC Permeability QGIS plug-in Python Cross-platform GPL-3.0 Majic and Pafka (2019)
Continuity in Street
Networks
Network analysis QGIS plug-in, CLI Python Cross-platform MIT Tripathy et al. (2020)
sDNA Network analysis
QGIS plug-in,
ArcGIS plug-in,
AutoCAD plug-in,
CLI, Python API
C++,
Python
Cross-
platform/
Windows
GPL-3.0
Cooper and Chiaradia,
2020
OSMnx Network analysis Python package Python Cross-platform MIT Boeing (2017)
momepy
General purpose
morphometrics
Python package Python Cross-platform MIT Fleischmann (2019)
foot Building footprints R package R Cross-platform GPL-3.0 Jochem and Tatem, 2021

6. • OSMnx is a Python package to retrieve, model, analyze, and visualize street networks from
OpenStreetMap.
• Users can download and model walkable, drivable, or bikeable urban networks with a single line
of Python code, and then easily analyze and visualize them.
• With OSMnx, user can easily download and work with amenities/points of interest, building-
footprints, elevation data, street bearings/orientations, and network routing.
• OSMnx contributes five primary capabilities for researchers and practitioners.
1. It enables automated and on-demand downloading of political boundary geometries,
building footprints, and elevations.
2. It can automate and customize the downloading of street networks from OpenStreetMap
and construct them into multidigraphs.
3. It can correct and simplify network topology.
4. It can save/load street networks to/from disk in various file formats.
5. OSMnx has built-in functions to analyze street networks, calculate routes, project and
visualize networks, and quickly and consistently calculate various metric and topological
measures.
Introduction

7. OpenStreetMap
• OpenStreetMap (OSM) is a collaborative mapping project that provides a free and publicly
editable map of the world.
• OpenStreetMap provides a valuable crowd-sourced database of raw geospatial data for
constructing models of urban street networks for scientific analysis.
• OpenStreetMap uses a topological data structure, with four core elements (also known as
data primitives):
1. Nodes are points with a geographic position, stored as coordinates (pairs of a latitude
and a longitude).
2. Ways are ordered lists of nodes, representing a polyline, or possibly a polygon if they
form a closed loop.
3. Relations are ordered lists of nodes, Relations are used for representing the relationship
of existing nodes and ways.
4. Tags are key-value pairs.They are used to store metadata about the map objects.

8. S.N. Authors
Name
Title Dataset Model or
Method
Description
1. Geoff Boeing,
July 2017.
OSMnx: New Methods
for Acquiring,
Constructing,
Analyzing, and
VisualizingComplex
Street Networks
OpenStreetMap
Data
OSMnx • This study introduced OSMnx, a new
tool to make the collection of data and
creation and analysis of street
networks.
• This study addresses the limitations of
data availability, consistency, and
technology which made researchers’
work gratuitously difficult.
2. Zhao, Fangxia
et al.,
March 2016.
Analysis of Road
Network Pattern
Considering Population
Distribution and Central
Business District
Transit Network
data for Beijing
Relative
Neighborhoo
d Graph
• This paper proposes a road network
growing model with the consideration
of population distribution and central
business district (CBD) attraction.
• In this paper , the relative
neighborhood graph (RNG) is
introduced as the connection
mechanism to capture the
characteristics of road network
topology.
Literature review

9. S.N. Author
Name
Title Dataset Model / Method Description
3. Geoff Boeing,
Aug 2017 .
The Morphology
and Circuity of
Walkable and
Drivable Street
Networks
walkable and
drivable
networks for 40
US cities through
OSMnx
OSMnx • This study examines the relative circuity
of walkable and drivable urban
circulation networks by simulating
routes using OpenStreetMap data and
the OSMnx software.
• Study found that in most cities, driving
networks tend to produce more
circuitous routes than walking
networks.
4. Strano et al.,
Nov 2012.
Urban Street
Networks, a
Comparative
Analysis ofTen
European Cities
Street network
data from City
Councils
planning offices
and Ordnance
Survey maps
GIS Environment • In this paper, authors compared the
structural properties of the street
networks of ten different European
cities.
• They investigated the geometric
properties of network and highlighted
differences and similarities between
cities.

10. S.N
.
Author Name Title Dataset Model / Method Description
5. Giacomin,
David &
Levinson ,
Nov 2015
Road network
circuity in
metropolitan areas.
Metropolitan
statistical area
(MSAs) census
data
Circuity • This study measures the circuity for 51
villages in USA for 1990, 2000 and 2010.
• This study examines how circuity varies
with the length of origin–destination
pairs (OD pairs), estimating a measure
of distance decay for circuity.
6. Geoff Boeing,
January 2020.
Street Network
Models and
Indicators for Every
Urban Area in the
World.
OSM data OpenStreetMap • This study models and analyzes the
street networks of every urban area in
the world, using boundaries derived
from the Global Human Settlement
Layer.
• this study models over 160 million
OpenStreetMap street network nodes
and over 320 million edges across 8,914
urban areas in 178 countries, and
attaches elevation and grade data.

11. S.N. Author Name Title Model / Method Description
7. Ahmadzai et al.,
Nov 2018
Assessment and
modelling of urban road
networks using Integrated
Graph of Natural Road
Network (a GIS-based
approach)
Centrality,
Integrated Graph
of Natural Road
Network (IGNRN)
• In this study road networks are
modelled and assessed using Integrated
Graph of Natural Road Network
(IGNRN) method.
• Authors assess network using three
classes of centrality, namely, closeness,
betweenness and straightness.
8. Coimbra et al.,
April 2021
An analysis of the graph
processing landscape
- • In this study author provided an
overview of different aspects of the
graph processing landscape.
9. Beineke et al.
2000
The average connectivity
of a graph
- • In this paper, authors investigated the
average connectivity, as a new measure
of global connectedness.

12. Objectives
• To assess the road network structure, its pattern and shape , connectivity and
accessibility by applying graph theory.
• To identify the individual nodal characteristics of connectivity, accessibility and
nodal efficiency.
• To conduct comparative analysis of road network characteristics of CBD area of
metropolitan cities in India and selected cities of other countries.

13. Literature
search and
understanding
of rationale
Identification of
key unknowns
and research
questions
Identification of
objectives of
thesis
Leaning basics of python
and morphological
characteristics of transit
network
Data collection,
capture and
preparation
Analyzing street
network data for
cities using OSMnx
Comparative study
of city transit
network
Analyzing
results
Conclusions
and future
scope
Methodology

14. SOFTWARES
DATASET
 Python
(Anaconda)
 JupyterLab (IDE)
 OSMnx
 QGIS
 Python Libraries
 Road Network data
is obtained from
OpenStreetMap
using OSMnx • pandas
• geopandas
• momepy
• pyproj
• libpysal
• Shapely
• Spaghetti

16. • OSMnx geocodes the query “Delhi, India" to retrieve the
place boundaries of that city from the NominatimAPI,
retrieves the drivable street network data within those
boundaries from the Overpass API, constructs a graph
model, then simplifies its topology such that nodes
represent intersections and dead-ends and edges represent
the street segments linking them.
• OSMnx models all networks as NetworkX MultiDiGraph
objects,Which are convertible to :
i. undirected MultiGraphs
ii. DiGraphs : without (possible) parallel edges
iii. GeoPandas node/edge GeoDataFrames

17. a. Not Simplified Network
Nodes = 270908, Edges = 593385
b. Simplified Network
Nodes = 61091, Edges = 208757

18. Network attributes
Values
Delhi Chennai Hongkong
Total No. of Nodes (n) 130357 29113 2979
Total No. of Edges (m) 347738 78646 5542
total edge length 28542820.251 5861122.264 717319.193
average edge length 82.1 74.525 129.433
average streets per node 2.819 2.84 2.89
Intersection count 107751 24684 2598
total street length 15658852.55 3133730.073 513674.88
Street segment count 183526 41156 4331
Average Street length 85.322 76.142 118.604
Average circuity 1.0468 1.0360 1.1315
Self loop proportion 0.0009262 0.000583 0.003232
Clean intersection count 59956 16832 1546
Node density (sq. km) 73.840 155.364 30.436
Intersection density (sq. km) 61.03 131.728 26.543
Edge density (sq. km) 16168.035 31278.512 7327.614

19. S.N. Type of road Total no. S.N. Type of road Total no. S.N. Type of road Total no.
1 Residential 287606 14 Road 70 27 Primary link, secondary 2
2 Tertiary 19635 15 Living street, unclassified 50 28 Motorway, trunk 2
3 Living street 14303 16 Motorway link 49 29 Motorway link, motorway 2
4 Unclassified 12418 17 Residential , tertiary 40 30 Trunk link, secondary 2
5 Secondary 8569 18 Tertiary, residential 38 31 Road, unclassified 2
6 Primary 1843 19 Motorway 33 32 Road, residential 2
7 Secondary link 677 20 Tertiary, secondary 19 33 Residential, road 2
8 Trunk 639 21 Tertiary, unclassified 15 34 Primary link, tertiary 1
9 Tertiary link 564 22 Secondary link, secondary 6 35 Primary link, primary 1
10 Primary link 537 23 Primary, secondary 4 36 Trunk link, tertiary 1
11 Residential , living street 228 24 Secondary link, residential 4 37 Secondary, unclassified 1
12 Trunk link 214 25 Trunk link, trunk 3 38 Primary, trunk 1
13 Residential , unclassified 151 26 Tertiary link, tertiary 3 39 Primary, trunk link 1

21. b) Shortest path w.r.toTravelTime
a) Shortest path w.r.to Length

22. • Isochrone maps, also known as travel
time maps, are maps that show all
reachable locations within a specified
time limit by a specified mode of
transport.
• The isochrone below joins up all points
within a 30, 60 and 180 minutes of drive
with Travel speed of 5 KMPH from the
origin point (77.1837538, 28.5913494).

24. Cities
Circuity
Walk Drive
Delhi, India 1.053 1.046
Mumbai, India 1.083 1.066
Chennai, India 1.047 1.036
Kolkata, India 1.069 1.064
Hongkong, China 1.142 1.131
Los Angeles, US 1.074 1.047
Frankfurt, Germany 1.048 1.061
7. Circuity of different cities for walkable and drivable network :
City
Walk Drive
Nodes Edges Nodes Edges
Delhi 156162 441526 130357 247738
Frankfurt 55060 157576 9402 19977

26. Fig. Betweenness centrality for all nodes

30. • Area of a tessellation cell
• Covered area ratio
• Area of a building footprint
• Length of a perimeter wall
• Building adjacency
• Mean neighbor distance between buildings
• Linearity of a street segment
• Width of a street profile
• Width deviation of a street profile
• Openness of a street profile
• Meshedness of a street network
• Connected components in a spatial network

31. • Network analysis for set of Central Business District (CBDs) and important
transit locations using OSMnx for cities.
• Network-constrained spatial autocorrelation for aggregate transit locations.

33. https://doi.org/10.1080/10095020.2016.1151213
https://doi.org/10.1103/PhysRevE.73.066107

34. Activity
3rd Semester 4th Semester
Sep
2021
Oct
2021
Nov
2021
Dec
2021
Jan
2022
Feb
2022
March
2022
April
2022
May
2022
Literature Review
Identification of project
objectives and Development of
methodology
Basics of python and
characteristics of transit
network
Acquisition transit
Analysis of transit network data
for cities
Comparative study and analysis
of results
Paper writing
Submission of Report

35. THANK
YOU