Geodata Processing and Webservices with Python and Azure

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Python can be used not only for geoprocessing but also for web services. In this short talk for GeoPython 2019 we at EBP show you how to compute isochrones and make them available through an API.

Data & Analytics

Geodata Processing and
Webservices with
Python and Azure
André Zehnder
Stephan Heuel

Who we are
André Zehnder
andre.zehnder@ebp.ch
25.06.2019 © EBP | 2
Stephan Heuel
stephan.heuel@ebp.ch

EBP Switzerland, www.ebp.ch
Services
Software
(plan, build, run)
Data science
(spatial, temporal)
IT consulting
Business domains
Mobility
Infrastructure
Environment

25.06.2019 © EBP | 4| Titel der Präsentation
It is all about space and time…

25.06.2019 © EBP | 5| Titel der Präsentation
Isochrone
 Line of equal time
 Simultaneous ocurrence
 Marks area of accessibility within
a time limit
Concept of isochrones

Let’s get a quick overview
25.06.2019 © EBP | 6
AVswissTLM
OSM
Ruleset for
walkability
Web-
API
Isochrone
Command
-line
Cost surface
(Raster)

How to get a cost surface
25.06.2019 © EBP | 7

How to get a cost surface
 Focus on walkability for pedestrians
 Integration of various data sources
 Key element: Set of rules
 Mixture of Python & FME
 Rasterio
 Numpy
 fmeobjects
25.06.2019 © EBP | 8
…
Type Rank Cost [s/100m]
Pavement 1 90
Footpath 1 90
Street 4 120
Bridge 2 200
Main_road 4 320
Railway_track 5 9000
River 7 -1
construction_site 5 -1
building_private 6 -1
building_public 6 300

25.06.2019
© EBP | 9
| Titel der Präsentation
How to compute
isochrones

How to compute isochrones
 Implemented with
 scikit-fmm (skfmm):
 scipy
 Numpy
 Based on Fast Marching method
 Accumulative cost distance
 Alternatives
 ArcGIS: Cost Distance
 QGIS: Accumulated cost
25.06.2019 © EBP | 10
Source: desktop.arcgis.com
skfmm.travel_time(phi, speed)
POI Cost surface Cost distance
(for isochrones)

Let’s compute some more isochrones…
11EBP | Walkalytics – Pedestrian Routing Done Right

A perfect task to parellelize
 Thousands of POIs to compute
 Suited for MapReduce approach
 Map: Compute individual isochrones
 Reduce: Combine to one raster
 Options for parallelization
 Multithreading: multiprocessing
 Asynchronous computing: asyncio
 Cluster computing: Hadoop, Spark
 Multiple shells: GNU parallel
25.06.2019 © EBP | 12
Shell 1
Shell 2
Shell 3
cat input/servicePoint.csv
| parallel --pipe -N4
python costdistance.py
cat input/servicePoint.csv
| python costdistance.py

How to run in the cloud: Azure/AWS/Digital Ocean
25.06.2019 © EBP | 13
Source: dotnettricks.com/learn/azure
 Azure Storage: BLOB-
storage for input data and
cost models
 Azure VM: Linux machine
to process cost surface
 Azure Web-App: On-demand processing of isochrones
Scalable & load-balancing
WSGI-Framework (Flask)
 Azure API Management: Gateway between API-call and
Web-App
Authentification
Logging, statistics

Web deployment & API
25.06.2019 © EBP | 14
dev.walkalytics.comSee code on github.com/ernstbaslerpartner/walkalytics-clients

Thank you!
andre.zehnder@ebp.ch
25.06.2019 © EBP | 15
stephan.heuel@ebp.ch

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This project aims to predict whether a loan application will be approved or denied based on various factors such as applicant's income, credit score, loan amount, etc. Using a dataset containing historical loan application data, we employed machine learning algorithms to build a predictive model. The model was trained on features such as applicant's income, credit history, loan amount, loan term, and others. After training the model, we evaluated its performance using metrics like accuracy, precision, recall, and F1 score. The insights from this project can help financial institutions streamline their loan approval process and make informed decisions. Visit for more information: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/

Predicting Loan Approval: A Data Science Project

Boston Institute of Analytics

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Geodata Processing and Webservices with Python and Azure

1. Geodata Processing and Webservices with Python and Azure André Zehnder Stephan Heuel

3. EBP Switzerland, www.ebp.ch Services Software (plan, build, run) Data science (spatial, temporal) IT consulting Business domains Mobility Infrastructure Environment

5. 25.06.2019 © EBP | 5| Titel der Präsentation Isochrone  Line of equal time  Simultaneous ocurrence  Marks area of accessibility within a time limit Concept of isochrones

6. Let’s get a quick overview 25.06.2019 © EBP | 6 AVswissTLM OSM Ruleset for walkability Web- API Isochrone Command -line Cost surface (Raster)

8. How to get a cost surface  Focus on walkability for pedestrians  Integration of various data sources  Key element: Set of rules  Mixture of Python & FME  Rasterio  Numpy  fmeobjects 25.06.2019 © EBP | 8 … Type Rank Cost [s/100m] Pavement 1 90 Footpath 1 90 Street 4 120 Bridge 2 200 Main_road 4 320 Railway_track 5 9000 River 7 -1 construction_site 5 -1 building_private 6 -1 building_public 6 300

10. How to compute isochrones  Implemented with  scikit-fmm (skfmm):  scipy  Numpy  Based on Fast Marching method  Accumulative cost distance  Alternatives  ArcGIS: Cost Distance  QGIS: Accumulated cost 25.06.2019 © EBP | 10 Source: desktop.arcgis.com skfmm.travel_time(phi, speed) POI Cost surface Cost distance (for isochrones)

11. Let’s compute some more isochrones… 11EBP | Walkalytics – Pedestrian Routing Done Right

12. A perfect task to parellelize  Thousands of POIs to compute  Suited for MapReduce approach  Map: Compute individual isochrones  Reduce: Combine to one raster  Options for parallelization  Multithreading: multiprocessing  Asynchronous computing: asyncio  Cluster computing: Hadoop, Spark  Multiple shells: GNU parallel 25.06.2019 © EBP | 12 Shell 1 Shell 2 Shell 3 cat input/servicePoint.csv | parallel --pipe -N4 python costdistance.py cat input/servicePoint.csv | python costdistance.py

13. How to run in the cloud: Azure/AWS/Digital Ocean 25.06.2019 © EBP | 13 Source: dotnettricks.com/learn/azure  Azure Storage: BLOB- storage for input data and cost models  Azure VM: Linux machine to process cost surface  Azure Web-App: On-demand processing of isochrones Scalable & load-balancing WSGI-Framework (Flask)  Azure API Management: Gateway between API-call and Web-App Authentification Logging, statistics

14. Web deployment & API 25.06.2019 © EBP | 14 dev.walkalytics.comSee code on github.com/ernstbaslerpartner/walkalytics-clients

Editor's Notes

Unit: [s/100m] SH: Hier fehlt eine Legende. Die Einheit [s/100m] würde ich für die Darstellung nicht verwenden, die haben wir nur aus numerischen Gründen so gewählt. Besser ist [km/h]. – muss man halt umrechnen: 80 [s/100m] = 4.5 [km/h] In der Präsentation Bike-Analysis-2018-11-27.pptx in diesem Ordner findest Du (für Velos) auf Folie 8 eine beispielhafte Darstellung eines Kostenrasters bzw. Kostenoberfläche.
Model each square-meter according to the walkability OSM: Pedestrian Crossings Only OSM + 5m pixel raster: 1h OSM & AV + 1m pixel raster: 1.5 days
Lines of equal time python costdistance/lib/run_walkalytics.py -cp cosodel_website_azure --animate -e 4327 geoPython,7.43708,46.94855 --fill-buildings --max-min 21 >/dev/null --break-values 0,4,8,12,16,20,21 --stats statpop_bbtot
Accumulated cost distance (diagonal: 1.41), cheapest movement variant (vertical, horizontal, diagonal) per node is selected CostDistance-Tool in ArcGIS, computed with «Fast marching method» in scikit-fmm Phi: line feature as a scalar function Speed: Array of speeds at each point Dx: spacing factor
A single shell process per POI https://adamdrake.com/command-line-tools-can-be-235x-faster-than-your-hadoop-cluster.html
Flask, Falcon, Bottle are interchangeable WSGI: Web Server Gateway Interface -> Link between Python code and API call API Management: Forward API-request to Web-App + Authorization (access key) + Logging AWS S3 -> Alternative to Azure torage AWS EC2 -> Alternative to Azure VM AWS Lambda -> Alternative to Azure Functions Azure API Management
Flask -> Alternative to Falcon AWS S3 -> Alternative to Azure torage AWS EC2 -> Alternative to Azure VM AWS Lambda -> Alternative to Azure Functions Azure API Management

Geodata Processing and Webservices with Python and Azure

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Editor's Notes