Mapping data Representing data with maps Geographic analysis tasks - - PowerPoint PPT Presentation

Mapping data

Representing data with maps

Geographic analysis tasks

• Mapping where things are
• Mapping the most and the least
• Mapping density
• Finding what’s inside
• Finding what’s nearby
• Mapping changes in time

Why mapping your data

– Maps are more effective than tables or lists to communicate some type of informations – Maps take advantage of our ability to distinguish colors, shapes, and patterns to interpret spatial relationships.

Kitenge 165 Mulongo 197 Manono 19 Kasenga 26 Mufuga 118 Kabalo 493 Manika 162 Kansenya 242 Ankoro 1381 Kabondo 630 Kikula 679 Bukama 1378 Kambove 38 Malemba 1031 Rwashi 115 Katuba 207 Kikondja 3607 Kenya K 673 Kapolowe 887 Lubumbashi 100 Kampemba 403 Dilala 285 Kamina 4

Number of cholera cases during weeks 47-2001 and 9-2002 in Katanga, RDC

Number of cholera cases during weeks 47-2001 and 9-2002 in Katanga, RDC

Maps can be

• Not geocoded (georeferenced)

– Representation of the space without any link with the reality (changes every representation)

• Geocoded (georeferenced)

– Representation of the reality in a geographical model where objects are link with geographical coordinates (Lat. Long. or address)

Distribution of asthma cases in Barcelona Not georeferenced

Georeferenced map

Geocoding

• The process of detemining the

coordinates of a specific location based

• n its street address or its existence

within a known region.

• Coordinates can be assigned as a pair
• f XY coordinates or as Lat. and Long.

Georeferenced map ? Yes, because linked with their address

Kinds of maps

• Reference maps

– Simply convey information about location

• f geographic features (rivers, streets,

houses, HC, Hospitals, etc.)

• Thematic maps

– detect, describe, and analyse spatial patterns; describe associations and correlations with other variables

Reference map

Thematic maps use patterns, colours, or differently sized symbols to display variations in data.

Thematic maps use patterns, colours, or differently sized symbols to display variations in data.

How to present data in a map

• Single Symbol
• Graduated Colour
• Graduated Symbol
• Unique Value
• Dot
• Chart

How to select the appropriate legend

• Magnitude, integers (number
• f cases per area)
• Rates, range of values
• Every feature of same origin

(streets, Hospitals,HC, rivers, etc)

• Categorical data (countries,

regions, districts, types of roads

• Values of many data

attributes at once

• Graduate symbol,

Dot density

• Graduate color areas
• Syngle symbol same color
• Unique value
• Chart legends

Mapping data

• The type and format of data and study

area to be mapped affect the method of mapping

The area to be mapped

The area to be mapped

The area to be mapped

The area to be mapped The importance of Geographical unit

• f measurement

The area to be mapped

The area to be mapped depends from:

• The choice of boundaries.
• The geographical unit
• The scale chosen to represent
• The subject or data of the study:
• Residence
• Onset
• Exposure
• Notification
• The disease to be mapped

Area representation problem

You need to aggregate data because specific problems; e.g. Confidentiality

Area representation problem

Some representation, count, can lose resolution

Area representation problem

Different aggregation Can bring to different results

Place of residence

An outbreak of Trichinosis in Paris Cases represented by place of residence

Mapping data requires careful interpretation

• Underlying population structure

– Population (arbitrary denominator) – Population density(higher density higher expected cases) – Age, sex structure (standardization)

Cumulative cases of cholera From week 39-2001 To week 17-2002 in Katanga Level of representation

Distribution of cases of PERTUSSIS Lebanon, as of week 2003-15

# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #

Cases/100,000/Y

0.178 – 0.554 0.555 – 0.872 0.873 – 1.741 1.742 – 3.554 No report

Population density inhab/SqKm by district in Katanga

Incidence rate of cholera cases per thousand From week 39-2001 to week 17-2002 in Katanga Selection of denominator

Selection of geographical unit- changing in denominator

Selection of geographical unit- changing in denominator

Finally, what data do you want map

• Exact location, individual cases
• Aggregated data

Specific cases Geocoded to identify their exact location according to hospital Location in Brooklyn, NY Hospitals Cases

Aggregated meningitis cases in Burkina, 2002, expressed as AR/100000.

Mapping clusters

Sheng Ji Restaurant Tong De Li Restaurant

Guangzhou Institute Respiratory Diseases

TCM Clinic Tea Stall Pearl River

Shared Passageway

Case 2 Case 3

(Dec 23)

Case 4

Hospital

Cases representation

Distribution of residents by clinical status Nursing home, Delaware, USA, 1992.

Place

Steps in Surveillance Analysis

Descriptive Analysis Place

• Dot maps for count of cases
• Administrative area maps for rates

– Choice of administrative areas – Rates to account for population – Standardised rates to account for population structure

• “Raster” maps for sentinel surveillance
• GIS when case coordinates available

Notification of Tuberculosis in France, 1996 4-Week Period Ending 31/12/1996

Descriptive Analysis of Place Use of Rates

• Count of cases does not represent risk
• Administrative areas have different

populations

• Population may vary over time

– Seasons – Population influx (refugees)

• Rates allow to compare risk
• Choice of administrative areas

(Problem of small numbers of cases)

• Choice of ranges

Notification Rate of Tuberculosis in France, 1996

Cases/100,000

Descriptive Analysis of Place

• Mortality/1000

– Region A, 12.97 – Region B, 7.15

• People dye more in Region A?
• Population age structure

– Region A, 24.7% over 64 years – Region B, 9.2% over 64 years

Distribution of Death by Falls by Province, Canada, 1998

Age Standardized Rate per 100,000 Crude deaths rate per 100,000

Maps for Sentinel Systems Incidence of diarrhea in France, 1995

Cases / 100,000 population

Source: Réseau National Télématique des Maladies Transmissibles

Place Testing for Hypothesis

• Remove effect of age

– Standardisation if needed

• Detect clusters

– Different techniques

• Eg. Test for spatial correlation by nearest neighbour
• Eg. Test for spatial correlation by contiguity analysis
• Generate hypothesis about risk factors

– Overlaying exposure and outcome – Test for cross-correlation

Mapping diseases

• Diseases mapping plays an important role in

monitoring community’s health

• It is a very useful tool in surveillance and alert
• Mapping has at least four major roles for PH

– Monitoring the spread of infectious disease in

• rder to identify the cause

– Monitoring health service access and use – Generating hypothesis about diseases causation

• r for identifying clusters

– Can incorporate ecological analysis

Power of maps

• Using maps to present geographic data

allow us to see:

– Location and extent – Spatial distribution: Pattern, density and Concentration – Spatial interaction:connectivity, accessibility, agglomeration

Power of maps

• Easy to answer these questions

– What is the size of the place interested – What is there at this point – What kind of distribution does it make – Is it found throught the study area?

Limitation of maps

• Map can only answer WHAT and WHERE but

not effectively to answer HOW and WHY

• Map is not efficient to answer these

questions:

– Why has it spread or diffused in this particular way – What geographic factors have constrained its spread – How far would it expand – What else is there spatially associeted with that phenomenon

But…you can empower them:

• Maps can effectively empowered if they can:

– Be used interactively and turned on or off – Integrate with different types of data (aerial photos, satellite imegeries, attribute table, etc.) – Be superimposed to see the spatial relationships with each other – Create spatial patterns based on surveyed data – Display data according to selection criteria