Hahmann and Burghart estimate at least 60% of all information is geospatially referenced. Fortunately, Django includes a variety of spatial and mapping tools to help build spatially-aware services. This talk will give an overview of geospatial concepts for the Australian and Django communities. From geo-tagged photos to the route between a user and their destination, at least 60% of all information generated is geospatially referenced (Hahmann and Burghart 2013). Therefore, being able to store, query and display spatial information in a Django can be a core function of a contemporary web application. Fortunately, GeoDjango is a Django core module that adds spatial capabilities to any Django website or service and this talk will provide an introduction to the world of Django and web mapping. This talk will cover: an introduction to fundamental geospatial concepts, highlighting key issues for Australian developers, a tour of common, third-party geospatial services and technologies used in websites such as maps and satellite imagery, geocoding, routing and spatial databases, an overview of storing and querying spatial data in a Django app, front-end libraries you can use to display spatial data, and related topics to continue your spatial journey including big data and streaming data. It is envisioned that this talk will provide a solid foundation for experienced Django developers new to geospatial analysis and mapping to develop their own Django web services using the GeoDjango documentation and concepts covered in this talk: avoiding common pitfalls in building and localising geospatial data. It is also envisioned that this talk would allow geospatial analysts to convert their algorithms to Django-equivalent concepts. This talk will also give an overview of future technologies that may be of interest to advanced developers to investigate.

1. A TRAVELLER'S
GUIDE TO MAPPING
TECHNOLOGIES IN
DJANGO
Anthony I. Joseph
DjangoConAU 2018
24 August 2018
anthony.djangoconau@fastmail.com
linkedin.com/in/anthony-joseph/
This work is licensed under a Creative Commons
Attribution-NonCommercial-NoDerivs 3.0
Australia License.

2. Image source: http://cdn.sydneybarani.com.au/assets/1_AboriginalGroupsSydney_-Cropped.jpg

3. TALK OVERVIEW
1. Mapping Fundamentals
2. Storing Geospatial Data
3. Querying Geospatial Data
4. Displaying Geospatial Data
5. Future Directions
6. Q&A

4. 1. MAPPING FUNDAMENTALS

5. THE BASICS
Sydney Opera House
Latitude: -33.8566
Longitude: 151.21507
But what is a co-ordinate?

6. A GEOGRAPHIC COORDINATE SYSTEM
3D Spherical Surface
Latitude/Longitude
Angles from a
centre point
WGS84
World Geodetic
System
SRID: 4362

7. GEOGRAPHIC COORDINATE SYSTEMS
Advantages
No distortions
Represent any point
Disadvantages
Complicated distance/area
calculations
Display 3D point in 2D media?
Image source: https://desktop.arcgis.com/en/arcmap/10.3/guide-
books/map-projections/about-geographic-coordinate-systems.htm

8. PROJECTED COORDINATE SYSTEMS
Examples:
Web Mercator
GDA94
Geocentric Datum of
Australia 1994
Advantages:
Great for display
Great for analysis*
Disadvantages:
Distorted
area/distance/shape
Image source: https://www.xkcd.com/977/

9. COMMON DATA FORMATS
Vector Raster
Image Sources:
- https://twitter.com/PSMA/status/1031799625831854080/photo/1
- http://www.ga.gov.au/scientific-topics/national-location-information
/digital-elevation-data

10. COMMON DATA TYPES: POINTS

11. COMMON DATA TYPES: (POLY)LINES
Data source:
https://www.smh.com.au/national/nsw/sydney-s-latte-line
exposes-a-city-divided-20180327-p4z6et.html

12. COMMON DATA TYPES: POLYGONS
Data source:
https://www.psma.com.au/products/administrative-
boundaries

13. COMMON DATA TYPES: MULTI-POLYGONS
Data source:
https://www.psma.com.au/products/administrative-
boundaries

14. ANALYTICAL CONCEPTS
Geocoding:
“130 George Street, Sydney”  -33.8599, 151.2090
Reverse Geocoding:
-33.8599, 151.2090 
“130 George Street, Sydney”
“Museum of Contemporary Art”
…

15. ANALYTICAL CONCEPTS
Geolocation
IP  Latitude/Longitude
Autocomplete
Use geolocation to suggest address

16. ANALYTICAL CONCEPTS
Routing:
“Opera House” 
“Museum of Contemporary
Art” by walking
Transportation modes:
Walking
Public Transport
Cycling
Driving/taxi/ridesharing

17. SPATIAL ANALYSIS
Image source:
- https://www.mapbox.com/use-cases/data-
visualization/
- https://www.smh.com.au/national/nsw/sydney-s-
latte-line-exposes-a-city-divided-20180327-
p4z6et.html

18. SPATIAL ANALYSIS - ISOCHRONES
Image source:
https://app.traveltimeplatform.com

19. 2. STORING GEOSPATIAL DATA

20. GIS DATA SOURCES
Web services:
Web Map Service (WMS)
Web Coverage Services
(WCS)
Open Geospatial Consortium
File formats:
Well-known text (WKT)
Keyhole Markup (KML)
GeoJSON
ESRI ArcGIS Shapefiles
MapInfo TAB files

21. DATABASE ENGINES
GIS extensions to database systems
OpenGIS® Implementation Standard for Geographic
information
PostgreSQL + GIS = PostGIS
SQLite + GIS = Spatialite
…MySQL/MSSQL/OracleDB
…MongoDB

22. DJANGO COMPATIBILITY
Source:
https://docs.djangoproject.com/en/2
.0/ref/contrib/gis/db-api/

23. DATABASE FIELDS
DATA TYPE FIELD TYPE
Points PointField/MultiPointField
Lines LineStringField/
MultiLineStringField
Polygons PolygonField/MultiPolygonField
Source:
https://docs.djangoproject.com/en/2.0/ref/contrib/gis/model-api

24. MAP PROJECTIONS / SPATIAL REFERENCES
Default:
WGS84
SRID: 4362
geography=True
Improves query
performance on
distance queries
from django.contrib.gis.db import models
class Suburb(models.Model):
postcode = models.CharField(max_length=4)
boundary = models.PolygonField(
geography=True,
# defaults
srid=4326,
spatial_index=True,
)

25. SPATIAL REFERENCE SYSTEMS
Data often in WGS84
May get data in other
reference systems
Convert between systems:
Reproject – ogr2ogr
Check the metadata!

26. SPATIAL INDEXES
Regular DB Indexes (B-trees)
index=True
Spatial Indexes (R-tree variants)
spatial_index=True
Image sources:
https://ieftimov.com/postgresql-indexes-btree
http://revenant.ca/www/postgis/workshop/indexing.html

27. 3. QUERYING GEOSPATIAL DATA

28. QUERYING DATA
qs = Model.objects.filter(
<field>__<lookup_type>=<param>
)
Complete list: https://docs.djangoproject.com/en
/2.0/ref/contrib/gis/geoquerysets/

29. QUERYSETS: POINT IN POLYGON
Example model Example query
class Suburb(models.Model):
postcode = models.CharField()
boundary = models.PolygonField()
pnt = GEOSGeometry('POINT(151.178 -33.884)’,
srid=4326)
qs = Suburb.objects.filter(
boundary__contains=pnt)

30. QUERYSETS: WITHIN DISTANCE (2KM)
Example model Example query
class Store(models.Model):
name = models.CharField()
location = models.PointField()
from django.contrib.gis.measure import
D
pnt = GEOSGeometry('POINT(151.196 -33.870)’,
srid=4326)
qs = Store.objects.filter(
location__dwithin=(pnt, D(m=2000)))
)

31. QUERYSETS: SORT BY DISTANCE
Example model Example query
class Store(models.Model):
name = models.CharField()
location = models.PointField()
from django.contrib.gis.db.models.functions
import Distance
usr = GEOSGeometry('POINT(151.178 -33.884)’,
srid=4326)
qs = Store.objects.annotate(
distance=Distance(‘location',listing.point)
).order_by('distance')
1
2
3

32. GEOSPATIAL DATABASE FUNCTIONS
https://docs.djangoproject.com/en/2.0/ref/contrib/gis/functions/

33. 4. DISPLAYING GEOSPATIAL DATA

34. REQUIREMENTS
>Spatial data (already have this!)
>A web mapping library
>Base maps

35. WEB MAPPING LIBRARIES
>Javascript-based
>Examples:
>Openlayers
>Leaflet/Mapbox
>Google Maps
>Mobile SDKs for native apps

36. BASE MAPS
>Tile-based imagery
>Graphics: Google Maps / Mapbox / OpenStreetMap /
ArcGIS …
>Satellite imagery: DigitalGlobe / Nearmap / US Gov (NASA
etc)
>URL formats:
>https://{s}.tile.openstreetmap.org/{z}/{x}/{y}.png

37. BASE MAP EXAMPLES
Apple Maps Google Maps
(Satellite)
Google
Maps
HERE Maps Mapbox
Maps

38. SIMPLE EXAMPLE
<!DOCTYPE html>
<html>
<head>
<link rel="stylesheet” href="https://unpkg.com/leaflet
@1.3.3/dist/leaflet.css" />
<script src="https://unpkg.com/leaflet@1.3.3 /dist/leaflet.js"></script>
</head>
<body>
<div id="map" style="width: 600px; height:
400px;"></div>
<script>
var map = L.map('map').setView([-33.8733, 151.1991],
19);
L.tileLayer(
'https://{s}.tile.openstreetmap.org/{z}/{x}/{y}.png’
,{attribution: 'OpenStreetMap contributors’}
).addTo(map);
L.marker([-33.8733, 151.1991]).addTo(map)
.bindPopup('You are here.')
.openPopup();
</script>
</body>
</html>

39. SIMPLE EXAMPLE
Web maps automatically
reproject:
>from WGS84
(geographical coordinate
system),
>to Web Mercator
(projected coordinate
system)

40. FORMS API
DATA TYPE FIELD TYPE
Points PointField/MultiPointField
Lines LineStringField/
MultiLineStringField
Polygons PolygonField/MultiPolygonField
Source:
https://docs.djangoproject.com/en/2.1/ref/contrib/gis/forms-api/

41. 5. FUTURE DIRECTIONS

42. RELATED TECHNOLOGIES
Offline analysis:
QGIS/ArcGIS
Tableau/PowerBI
GeoMesa: big data for spatial
WebGL Maps
Mobile:
3D graphics (Unity)
Augmented/Virtual Reality
Image source: https://www.mapbox.com/augmented-reality/

43. DATA SOURCES
Open data
data.gov.au
Government data
PSMA: psma.com.au
Buildings API
Commercial data
MaxMind/ip2location
Check licences!

44. NEW AUSTRALIAN DATUM
Currently use GDA94 (EPSG
4939)
Geosciences Australian
developing GDA2020 (EPSG
7844)
Affects Australian government
data
May need to reproject data
Image source: http://www.ga.gov.au/scientific-
topics/positioning-navigation/datum-
modernisation

45. KEY TAKEAWAY
POINTS
The official docs are great:
don’t forget to use them!
Make use of existing
libraries/3rd party services:
“don’t roll your own crypto”!
Be careful of:
• coordinate systems
• licences
• coverage
Geospatial analysis is a lot
like statistics: easy to do
something that looks
correct, but has underlying
issues. Question assumptions and
check metadata