Horizon DTC Integrator Day:The Geospatial Industry

Jeremy Morley,Steven Feldman

2nd December 2009

Purpose

• Scope of the geospatial industry• Exciting opportunities at the bleeding edge• Most projects have some geo• What’s the geography, be it explicit or implicit,

in your projects?

Programme

2 – 3pm Isn’t there geo in everything?

3 – 3.15pm Break3.15 – 3.50pm Spatial is special3.50 – 4pm Break4 – 4:40pm Geofutures

(& why spatial isn’t special)

4.40 – 5pm Wrap-up

Your words

Geo in 2010(1 hour)

• The industry now• Geospatial without the degree– My maps– GeoCommons– Google Earth

• Breakout: “where’s the geo in our projects?”

Geocommons – screen-scraping university locations to a map

http://www.geocommons.com/

mgr = new MarkerManager(map);var lat = parseFloat(57.1650804282195);var lng = parseFloat(-2.09906504822913);var point = new GLatLng(lat,lng);array_points[1] = [];array_points[1]['point'] = point;var html = '<div style="width: 260px; padding-right: 10px"><h3>The University of Aberdeen

(A20)</h3>'+'University Office<br />' +'King\'s College<br />' +'Aberdeen<br />' +'AB24 3FX<br />' +'t: +44 (0) 1224 273504<br />' +'e: <a href="mailto:sras@abdn.ac.uk">sras@abdn.ac.uk</a><br />' +'w: <a href="http://www.abdn.ac.uk/sras" target="_blank">www.abdn.ac.uk/sras</a><br

/><br />' + '<a href="/students/choosingcourses/choosinguni/instguide/a/a20">further details</a>';array_points[1]['description'] = html;array_points[1]['region'] = '11';array_points[1]['name'] = 'The University of Aberdeen';array_points[1]['code'] = 'A20’;

Javascript source for map, showing one university’s record. We can use the fixed structure to extract the information we want.

BEGIN {print "name, latitude, longitude” }

match($0,"lat =") {i = match($0 , "[)]" )lat = substr($0,22,i-22); }

match($0,"lng =") {i = match($0 , "[)]" )lng = substr($0,22,i-22); }

{i = match($0,"/H3>") + match($0,"</h3>")if( i > 0 ) { j = match($0,"<H3>") + match($0,"<h3>") name = substr($0,j+4,i-(j+4)) gsub(",","-",name) printf "%s, %s, %s\n ","\"" name "\"", lat, lng }}

Unix ‘awk’ script to extract and format the text

name, latitude, longitude"The University of Aberdeen (A20)", 57.1650804282195, -2.09906504822913 "University of Abertay Dundee (A30)", 56.4634, -2.9726 "Aberystwyth University (A40)", 52.4147760680295, -4.08403520146778 "ALRA (The Academy of Live and Recorded Arts) (A42)", 51.4551, -0.1730 "Accrington & Rossendale College (A44)", 53.7549, -2.3714 "The College of Agriculture- Food and Rural Enterprise (A45)", 54.6986, -6.2152 "American InterContinental University - London (A50)", 51.5188, -0.1516 "Anglia Ruskin University (A60)", 51.7412476988799, 0.474334439742583 "Anglo European College of Chiropractic (A65)", 50.7262, -1.8243 "Askham Bryan College (A70)", 53.9110, -1.1053 "Aston University- Birmingham (A80)", 52.4860, -1.8895

Comma-separated variables file, ready for

import into Geocommons

Breakout session –“Where’s the geo in our projects?”

• Four groups

• 15mins to work as a group to find geographic dependencies, and analysis, in your projects

• 20mins: each group has 5mins to give examples of their geographic content

Your words• Marketing

Communications• recontextualized • social networks and

virtual worlds • web 2.0 technologies for

public engagement and activism

• ways people divide up and structure information

• interactive handheld guide

• innovative solutions in the transport sector

• spatial cognition • learning with

technology• environmental sensors

and location-aware technology

Your words

Reporting

Break!

Spatial is special

“How to lie with maps”

1 – change the map units

Political context

Gerrymandering: adjusting political units to favour particular party

See: http://www.redistrictinggame.org

The Modifiable Areal Unit Problem

'the areal units (zonal objects) used in many geographical studies are arbitrary, modifiable, and subject to the whims and fancies of whoever is doing, or did, the aggregating."(Openshaw, 1984 p.3)

Openshaw and Taylor's (1979):The results of statistical analysis of data for spatial zones can be varied at will by changing the zonal boundaries.

Types of MAUP• Scale effect– The variation in numerical results that occurs due to the

number of zones used in analysis.– E.g. tendency towards smoother statistics with larger

aggregation areas• Zoning effect (Gerrymandering)– The variation induced by the choice of units in which to

collect data, or to aggregate to.• Ecological Fallacy– The error in thinking that the results of an aggregated

area can be uniquely distributed to constituent parts

“How to lie with maps”

2 – extensive versus intensive

Examples

• Some examples here - taken from the work of Dr. Jason Dykes and Prof. David Unwin.

• Part of Project Argus• Using data from the 1991 Population Census

for Leicestershire, UK• 187 wards of varying size and character• http://www.agocg.ac.uk/sosci/casestudies/dykes/dykes.pdf

Example – total population

Example – population density

Types of values• We can divide polygon attributes into two types:• Spatially intensive– True possibly for any part of the area (if the area is

homogeneous), e.g. densities, rates, proportions– A field value, averaged over the area

• Spatially extensive– True only for the entire area, e.g. total population– Integration (summation) of the field over the area– Usually misleading – convert to intensive/normalised

“How to lie with maps”

3 – change the colouring

Equal intervals

Quantile

Choice of classification is critical

Example (from ESRI) – where are the kids?

Spatial is special

• Be careful with spatial reasoning• Be careful with map presentation

• Spatial also special in data structure• Spatial representations:– Points / lines /polygons (e.g. road map)– Topological relationships (e.g. adjacency)– Continuous fields (e.g. temperature)

Break!

Geofutures

Geo in 2015

Generating ideas

• Some thinking time (20 mins)– What’s the geo problem I’d like solved?, or– How does this stuff change my research focus?– Or, what’s the geo data I need and don’t have?

• Reporting:– How does this fit my research direction?– Could this be part of my feasibility project?– Or, what outside interest might incorporate geo ?

• ACTION: what do I need to do next?

GeoVation Awards Program

£21,000 to promote and support innovation for social, economic and

environmental benefit through the use of geography

https://challenge.geovation.org.uk/

Purpose

• Scope of the geospatial industry• Exciting opportunities at the bleeding edge• Most projects have some geo• What’s the geography, be it explicit or implicit,

in your projects?

Wrap-up

• What have you learnt?• Feedback: has this helped?

Further Examples – zoning effects"How to Lie with Maps" by Mark Monmonier

(see Chapter 9 (first edition))

1000 100 50 100 50 100 50

200 100 200 100 200 100 200

100 200 100 4000 100 200 100

200 400 200 400 200 400 3000

Number of televisions

2000 200 100 200 100 200 100

200 100 200 100 200 100 200

100 200 100 4000 100 200 100

100 200 100 200 100 200 1500Number of households

Televisions per household

0.5 0.5 0.5 0.5 0.5 0.5 0.5

1.0 1.0 1.0 1.0 1.0 1.0 1.0

1.0 1.0 1.0 1.0 1.0 1.0 1.0

2.0 2.0 2.0 2.0 2.0 2.0 2.0

AggregationNumber of TVs

Number of h'holds

TVs per h'hold

1450

5900

4800

2900

5900

2400

0.5

1.0

2.0

Aggregation (2)

2300 5700 4150

0.74 1.04 1.60

3100 5500 2600

Number of TVs

Number of h'holds

TVs per h'hold

Aggregation (3)Number of TVs

Number of h'holds

TVs per h'hold

11003550

4100

340022003200

4100

17000.51.11

1.0

2.0

Further examples – ecological fallacy

2300 5700 4150

0.74 1.04 1.60

3100 5500 2600

Number of TVs

Number of h'holds

TVs per h'hold

What’s the pattern in the original cells?

A consistent disaggregation

Number of TVs

Number of h'holds

190 285 200 350 350 210 890

455 450 1085 960 895 520 1260

355 315 525 480 595 360 700

130 120 80 100 80 110 100

100 150 100 200 100 50 100

350 300 700 600 500 200 300

500 450 700 600 700 400 500

650 600 400 500 400 550 500

2300 5700 4150

3100 5500 2600

Ecological FallacyNumber of TVs

Number of h'holds

190 285 200 350 350 210 890

455 450 1085 960 895 520 1260

355 315 525 480 595 360 700

130 120 80 100 80 110 100

100 150 100 200 100 50 100

350 300 700 600 500 200 300

500 450 700 600 700 400 500

650 600 400 500 400 550 500

Ecological FallacyNumber of TVs

Number of h'holds

TVs per h'hold

190 285 200 350 350 210 890

455 450 1085 960 895 520 1260

355 315 525 480 595 360 700

130 120 80 100 80 110 100

1.90 1.90 2.00 1.75 3.50 4.20 8.90

1.30 1.50 1.55 1.60 1.79 2.60 4.20

0.71 0.70 0.75 0.80 0.85 0.90 1.40

0.20 0.20 0.20 0.20 0.20 0.20 0.20

100 150 100 200 100 50 100

350 300 700 600 500 200 300

500 450 700 600 700 400 500

650 600 400 500 400 550 500

Ecological Fallacy

1.90 1.90 2.00 1.75 3.50 4.20 8.90

1.30 1.50 1.55 1.60 1.79 2.60 4.20

0.71 0.70 0.75 0.80 0.85 0.90 1.40

0.20 0.20 0.20 0.20 0.20 0.20 0.20

0.5 0.5 0.5 0.5 0.5 0.5 0.5

1.0 1.0 1.0 1.0 1.0 1.0 1.0

1.0 1.0 1.0 1.0 1.0 1.0 1.0

2.0 2.0 2.0 2.0 2.0 2.0 2.0

0.74 1.04 1.60Same aggregated trend from different fine-scale detail.