Applications of Spatial Data Mining & Visualization - Case

Studies

2

Introduction• Meteorological Data and Demographics Data hold

important information that can help in several application contexts

• Several data mining applications possible on these data sets

• In the department we have research projects working on these data– RoadSafe – Summarizing large spatio-temporal weather

prediction data– Atlas.txt – Summarizing UK 2001 Census data

• Both these projects present summaries to users in natural language, English and other modes

• Real World applications contain data mining as one of the modules or tasks in the project– Not as the end product in itself

3

Road Ice Forecasts -RoadSafe

• Road Ice Forecasts:– Are required by local councils for winter road maintenance

operations– Are driven by computer simulation models that predict weather

conditions for 1000’s of points on a road network – Output of model is a huge spatio-temporal data set (up to 33mb

for some councils)– Form part of a road forecasting service delivered to Road

Engineers via an online Road Weather Information System (RWIS)

• RWIS allows model data to be communicated in various modalities, e.g. text, tables, graphs and maps

4

• Model output is a large spatio-temporal data set (in order of Megabytes)• Road network split into routes, 9 meteorological parameters (e.g. Road Surface Temperature) measured at each point on a route• Sampled at 20 minute intervals over a 24hr period

5

6

24 Hour Forecast for Kirklees

All Routes

Min RST

Time <= 0c

Ice Hoar Frost

Snow Fog MaxGust

s Rain TS

Worst/Best

-1.1 /1.4

21:00 /NA

Yes /No No/No No/No Yes/Yes 15/13 No /No No

Wind (mph) Light south to south-easterlies for the duration of the forecast period. Winds may become more moderate late morning on higher ground, but remaining southerly.

Weather

A mainly cloudy night, with foggy patches across much of the forecast area. Higher ground above the low cloud level could see temperatures drop below freezing during the late evening, with most western parts of the forecast area dropping below freezing by the morning. Urban areas are expected to remain marginal throughout the night.

Route All routes summary worst/best

1 0.4/1.8 NA/NA No/No No/No No/No Yes/Yes 13/11 No /No No

2 0.7/2.0 NA/NA No/No No/No No/No Yes/Yes 13/10 No /No No

3 0.5/1.8 NA/NA No/No No/No No/No Yes/Yes 13/9 No /No No

4 0.4/1.8 NA/NA No/No No/No No/No Yes/Yes 13/12 No /No No

5 0.7/1.9 NA/NA No/No No/No No/No Yes/Yes 13/9 No /No No

6 0.7/2.1 NA/NA No/No No/No No/No Yes/Yes 13/11 No /No No

7 0.9/1.8 NA/NA No/No No/No No/No Yes/Yes 13/9 No /No No

8 0.8/2.1 NA/NA No/No No/No No/No Yes/Yes 13/9 No /No No

9 1.4/2.1 NA/NA No/No No/No No/No Yes/Yes 13/9 No /No No

10 0.8/1.9 NA/NA No/No No/No No/No Yes/Yes 13/9 No /No No

11 0.3/1.8 NA/NA No/No No/No No/No Yes/Yes 13/11 No /No No

12 -0.8 /1.5

22:40 /NA

Yes /No No/No No/No Yes/Yes 15/11 No /No No

7

Problem• Input: Spatio-temporal weather prediction data

(shown on slide 4)• Output: Summary of input data (shown on slide 6)• Task:?

– There is no well defined data mining task (classification or clustering or a new task)

– Clusters of similar weather spatially and temporally can be one kind of summary

– Classification of routes can be another kind of summary– Both used in the final system

• Challenges – Complex spatio-temporal data set– Spatio-temporal analysis methods are still maturing– Even visualization of the entire data is hard

8

9

Overview of Data Analysis• Two main challenges:

– Analysing the input data along the temporal dimension– Analysing the input data along the spatial dimension

• Ideally analysis should be performed on both dimensions simultaneously

• Solution inspired by Video Processing– The input data set is seen as a video containing 3*24*9=648 frames

(maps)• 3 key elements: 0. Pre-processing – geo-characterization – merging

required data with other relevant themes1. Low level processing

- Global Trends – Temporal segmentation- Local Events – Spatial Segmentation (Classification and Clustering)

2. Event detection and indexing3. Keyframe extraction.

• Extracted keyframes form the summary

10

Preprocessing

• Frames of reference used for spatial clustering

• Geographic Characterisation assigns properties to each data point based on frames of reference for the region

11

Spatial Reference Frames• Spatial descriptions should be meteorologically correct

(not necessarily most geographically accurate)• Forecasters consider how geography influences weather

conditions in their descriptions (meteorological inferences)

"exposed locations may have gales at times”• Dominant geographical features within regions also

affect the reference strategy

Kirklees (land locked) Hampshire1. Altitude 1. Coastal Proximity2. Direction 2. Altitude3. Population 3. Direction

4. Population

12

Spatial Segmentation

• Each of the 648 frames (maps) are analysed to compute spatial segmentations (clusters)

• Because weather parameters are continuous, they are first discretized

• E.g for road surface temperature (map shown on the next slide)– OK => {>4}– Marginal => {<=4 & >1}– Critical => {<=1 & >0}– Subzero => {<=0}

• Density based clustering used for performing spatial segmentation

13

Discretization of weather parameters

14

Cluster DensitiesFrame of Reference Proportion of subzero points 07:20 0740 08:00 08:20 08:40Altitude 0m: 0.0 0.0 0.0 0.0 0.0

100m: 0.0 0.0 0.0 0.0 0.0200m: 0.0 0.0 0.0 0.0 0.0300m: 0.0 0.0 0.0 0.0 0.0400m: 0.041 0.041 0.12 0.125 0.166500m: 0.5 1.0 1.0 1.0 1.0

Direction Central: 0.0 0.0 0.0 0.0 0.0Northeast: 0.0 0.0 0.0 0.0 0.0Northwest: 0.0 0.0 0.0 0.0 0.0Southeast: 0.0 0.0 0.0 0.0 0.0Southwest: 0.014 0.021 0.035 0.0354 0.042

Urban/Rural Rural: 0.002 0.003 0.005 0.006 0.007Urban: 0.0 0.0 0.0 0.0 0.0

15

All Data Points 13/01/2007

-1

0

1

2

3

4

5

6

712:0

0

13:0

0

14:0

0

15:0

0

16:0

0

17:0

0

18:0

0

19:0

0

20:0

0

21:0

0

22:0

0

23:0

0

00:0

0

01:0

0

02:0

0

03:0

0

04:0

0

05:0

0

06:0

0

07:0

0

08:0

0

09:0

0

10:0

0

11:0

0

Time

Min

. R

oad

Su

rface T

em

pera

ture Trend (RST Decreasing)

Event (RST <= 0)

Ok

CriticalMarginal

16

Atlas.txt• Is an ongoing research project

– Produces textual summaries of geo-referenced statistics

– for visually impaired users• The focus of the project is more on

visualization of spatial data by visually impaired (VI) users– Spatial data is essentially geometric and it is

not clear how visually impaired users model geometric information

– In the absence of vision , is it possible to model geometric information based on tactile and audio inputs?

• If possible, what is the nature of these mental models of geometries

17

Input

%Unemployment in Aberdeen

<2.2<3.5<4.8<6.1

18

Output• No gold standard models of spatial information

suitable to VI users available• So several alternative summaries of spatial

information that need to be tested on real users• One possible example textual summary:

“Some wards in the east and central parts (3,5,6,9) of the city have high percentage of unemployed people aged 16-74 above 03.51%”

• Are the textual summaries adequate on their own?• Do they need to be supplemented by tactile or

sonic maps?– Tactile maps

http://homepages.phonecoop.coop/vamos/work/intact/– Sonic Maps http://www.cs.umd.edu/hcil/audiomap/

19

Problem

• Input: 2001 UK census data• Output: Summary of input data• Task: Spatial segmentation + Spatial

visualization for VI users– Unlike RoadSafe the data mining task is well

defined– What is less defined though is the task of

visualization of summary by VI users– Shape (geometry) and topology of segments

need to be accessible to visually impaired users

20

Space and Visual Impairment

• Atlas.txt is an ongoing research project– more open questions than useful answers

• VI users need to perform two tasks for modeling spatial data– Scanning space for information

• Several scanning strategies possible • E.g. Left-right VS top down

– Coding spatial information using a suitable reference frame

• Once again several coding strategies available• E.g. body (ego) centric VS external

• VI users are trapped in a vicious circle while finding efficient scanning and coding strategies

21

Strategic Disadvantage for VI users

• Scanning strategy determines the quality of spatial information acquisition– But better scanning strategy possible only with

knowledge of spatial information• Sighted users take a quick look at an image which helps

them to scan the image lot more efficiently• VI users do not have the luxury of a quick glance!

• Coding strategy determines the quality of mental representation– Mental models coded on body centric reference frame

less useful for complicated spatial analysis– External reference frames help to code better quality

mental models– VI users need improved scanning strategies for acquiring

suitable external reference frames– Because VI users are disadvantaged to find a quality

scanning strategy, they are also disadvantaged to find a quality coding strategy

22

Solution Options

• VI users clearly need external help in finding suitable external reference frames

• Atlas.txt solution– Identify several reference frames and present summary

coded in each of these– VI users may be familiar with some spatial layouts

• E.g. telephone key pad and clock face– Use several of these to code summary information“Some wards in the east and central parts (3,5,6,9) of the

city have high percentage of unemployed people aged 16-74 above 03.51%”

– E.G. ‘east and central parts’ can also be expressed by (3,5,6,9) each number referring to a location on the telephone keypad layout