Dr Harut Shahumyan

UCD School of Geography, Planning and Environmental Policy harutyun.shahumyan@ucd.ie

Spatial Pattern Analysis and Opportunity Mapping for Ireland

November 9, 2012

Geographic Information System

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Homes

School Districts

Streets

Zip Codes

Cities

Counties

Geographic Information System

“Dublin’s Place in the Irish and Global Economy, 2012”

Project Objective:

To examine Dublin’s place and role in the national and global economies through evidence based research and to deliver a research package of evidence to support investment in the Dublin city region.

Some policy questions to answer

• Where are the best locations nationally and regionally to ensure best return for investment of scare fiscal resources?

• What geographic areas are exhibiting clustering effects which may assist further and viable business formation and the growth of a knowledge economy?

• Where should future services such as in the areas of health, education and transport be located based on current patterns of development and opportunity?

Datasets and Sources

• Central Statistics Office (CSO)

• AnPost GeoDirectory

• Economic and Social Research Institute (ESRI)

• Ordinance Survey Ireland (OSI)

• Irish Social Science Data Archive (ISSDA)

• National Transport Authority (NTA)

• Environmental Protection Agency (EPA)

• Department of the Environment, Community & Local Government (DECLG)

• Department of Social Protection (DSP)

• Department of Education and Skills (DES)

• Higher Education Authority (HEA)

• Enterprise Ireland (EI)

• IDA Ireland

• Companies Registration Office (CRO)

• Office of Public Works (OPW)

• Other Public Agencies

Organisations Distribution in GeoDirectory

• GeoDirectory 2011 – 300,000 organisations

– NACE Rev2 classification: 19 broad sections & more than 1300 classes.

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Top Activities

Nationally GDR

POWCAR 2006 Employees’ Distribution

As part of the Census 2006 the place of work details of all employed persons who undertook a journey to work were geo-coded. — 9 industrial classes

Spatial Statistics

• Comprises a set of techniques for describing and modelling spatial data.

• In many ways extend what the mind and eyes do, intuitively, to assess spatial patterns, distributions, trends, processes and relationships.

• Unlike traditional (non-spatial) statistical techniques, spatial statistical techniques actually use space – area, length, proximity, orientation, or spatial relationships – directly in their mathematics.

Spatial Statistics toolbox in ArcGIS

• Includes both statistical functions and general purpose utilities.

• Helps to determine features’ geographic distribution, implement pattern analysis, mapping clusters and modelling spatial relationship.

• Core functionality (not an extension)

• Available at all license Levels

Mapping Clusters: Hot Spot Analysis

Identify where spatial clustering occurs, and where spatial outliers are located:

– Where are the sharp boundaries between affluence and poverty?

– Where do we find anomalous spending patterns?

– Where do we see unexpectedly high rates of diabetes?

– Where the most jobs are concentrated?

– Where do we see significantly high rates of unemployment?

Sector A: Agriculture, forestry, fishing

Hot/Cold-Spots Density

Sector F: Construction

Job Density 2006 (Morgenroth, 2008) Organisations Density 2011

Sector F: Construction

Hot/Cold-Spots Density

Wholesale and retail trade Accommodation and Food Services

ICT Sector Professional and Scientific Sectors

Opportunity Mapping

• Opportunity mapping is an approach to conceptualise and visualize the varying levels of access to the opportunities which exist throughout different places and regions.

• Having high access to opportunity means having the ability to obtain a quality education, being able to have a safe place to live, having employment options, having access to transport network, health services, and more.

People with 3rd level education in 2006

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Households by owner occupied with no mortgage in 2006

Buildings within 1 km from a bus stop in 2011

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Average Distance to GPs

Distribution of Primary Schools

Indicators: Economic

Indicator Effect Source

Number of potential employers Positive GeoDirectory

Change of number of employers Positive GeoDirectory

Number of employed people Positive POWCAR

Number of jobs Positive POWCAR

Average commute distance to work Negative POWCAR

Unemployment rate Negative SAPS

Unemployed people with high level of education Positive SAPS

Unemployed people with PHD degree Positive SAPS

Unemployed people with no formal or primary

education only

Negative SAPS

Age dependency ratio Negative SAPS

Proximity to main roads Negative Tele Atlas

Bus service coverage1 Positive NTA, GeoDirectory

Proximity to rail, Dart and LUAS Negative RPA, NTA

Change in Industrial or commercial units areas Positive CORINE

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Indicators: Education

Indicator Effect Source

People with primary education only Negative SAPS

People with 3rd level of qualification Positive SAPS

People with PhD Positive SAPS

Primary and secondary schools nearby Positive GeoDirectory

Tertiary education institutes nearby Positive GeoDirectory

Proximity to main roads Negative Tele Atlas

Bus service coverage Positive NTA, GeoDirectory

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Indicators: Neighbourhood

Indicator Effect Source

Population increase Positive SAPS

Household vacancy rate Positive SAPS

Proximity to parks and open spaces Negative CORINE

Proximity to coast Negative OSI

Persons on Live Register Negative LR

Average persons per room Negative DI

Private households by owner occupied with no

mortgage

Positive SAPS

Permanent private households rented Positive SAPS

Professional, managerial and technical workers Positive SAPS

Rate of semi-skilled or unskilled people Negative SAPS

Crime rate Negative AIRO

Change in Absolute Deprivation Index Score Positive DI

Relative Deprivation index Score 2006 Positive DI

Proximity to main roads Negative Tele Atlas

Bus service coverage Positive NTA, GeoDirectory

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Opportunity Index

• The indicators were analysed relative to the other EDs within the country by standardizing through the use of z-scores.

• A z-score is a statistical measure that quantifies the distance (measured in standard deviations) a data point is from the mean of a data set:

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, 2

1

2

1

Xxn

Sxn

XwhereS

XxZ

n

j

j

n

j

j

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xj is the attribute value for features j and n is equal to the total number of features.

Neighbourhood Opportunity

Education Opportunity

Economic Opportunity

Comprehensive Opportunity

Opportunity and Deprivation

Source: Haase and Pratschke, 2008

• To provide advice to GIS users on data management, processing and application development and to counsel the prospective and novice users with regard to furthering their GIS knowledge.

• Open to all UCD staff, researchers and students, with priority given to the College of Human Sciences.

• An appointment is necessary.

Thursdays, 3pm - 5pm E003A, Newman Building

Phones: 1 716 8103 / 2714 Harutyun.Shahumyan@ucd.ie

GeoSAL offers a range of project, implementation, research and industry-focused services including:

Map design and printing

Large format scanning, digitizing and geo-referencing

Data conversion, collection, integration, interpolation

Geo-database design and deployment

Spatial analysis and statistics

Geo-spatial modeling

Spatial random sampling

GIS application development

Web-based GIS applications

Global Positioning Systems (GPS) field inventory

Training and knowledge transfer

Technical support

Assistance in proposal writing

Individual and project-based research consulting

Harutyun.Shahumyan@ucd.ie