Methods of analysing change over time and space Ian Gregory (University of Portsmouth) & Paul Ell (Queens University, Belfast)

Slides:



Advertisements
Similar presentations
Multilevel modelling short course
Advertisements

Comparing Results from the England and Wales, Scotland and Northern Ireland Longitudinal Studies: Health and Mortality as a case study Census Microdata.
Vital Statistics an invaluable resource for health, demographic & population geography research Paul Norman.
Gender Ratios in Global Migrations, Data collection funded by the National Science Foundation and the National Institutes of Health Trent Alexander.
Methods of interpolating data to create long-run time series Ian Gregory (University of Portsmouth) & Paul Ell (Queens University, Belfast)
What would you use the data for? Straightforward secondary analysis –To assess theoretical accounts –To quantify characteristics or behaviours –To challenge.
The Census Area Statistics Myles Gould Understanding area-level inequality & change.
SADC Course in Statistics Basic summaries for demographic studies (Session 03)
1 Voting With Their Feet: Migration Patterns Under The Celtic Tiger, Peter Connell 1 and Dennis G. Pringle 2 1. Information System Services,
Our Approach: Use a separate regression function for different regions. Problem: Need to find regions with a strong relationship between the dependent.
Apex predators and human populations as structuring agents on coral reefs Jonathan L.W. Ruppert, Laurent Vigliola, Marie-Josée Fortin and Mark G. Meekan.
11 Pre-conference Training MCH Epidemiology – CityMatCH Joint 2012 Annual Meeting Intermediate/Advanced Spatial Analysis Techniques for the Analysis of.
The micro-geography of UK demographic change Paul Norman School of Geography, University of Leeds Understanding Population Trends and Processes.
Module 36: Correlation Pitfalls Effect Size and Correlations Larger sample sizes require a smaller correlation coefficient to reach statistical significance.
Geographical Information Systems for historical research Achievements and methodologies Dr. Ian Gregory, Associate Director Centre for Data Digitisation.
Department of Geography University of Portsmouth Fundamentals of GIS: What is GIS? Dr. Ian Gregory, Department of Geography, University of Portsmouth.
Spatial Interpolation
Why Is It There? Getting Started with Geographic Information Systems Chapter 6.
Census-based measures of fertility, mortality, and migration Hist 5011.
Geography and Geographical Analysis using the ONS Longitudinal Study Christopher Marshall & Julian Buxton CeLSIUS.
Migration, methodologies and health inequality SEED Group
Measuring local segregation in Northern Ireland Chris Lloyd, Ian Shuttleworth and David McNair School of Geography, Queen’s University, Belfast ICPG, St.
1 Dr. Ian Gregory Temporal GISes of Changing Administrative Boundaries: European Comparisons Dr. Ian Gregory, Department of Geography, University of Portsmouth.
GIS in Spatial Epidemiology: small area studies of exposure- outcome relationships Robert Haining Department of Geography University of Cambridge.
Introduction to GIS: Lecture #7 (GIS Analysis) GIS Analysis Describing Attributes Statistical Analysis Spatial Description Spatial Analysis Searching for.
Why Geography is important.
GIS 2, Final Project: Creating a Dasymetric Map for Two Counties in Minnesota By: Hamidreza Zoraghein Melissa Cushing Caitlin Lee Fall 2013.
Tse-Chuan Yang, Ph.D The Geographic Information Analysis Core Population Research Institute Social Science Research Institute Pennsylvania State University.
IS415 Geospatial Analytics for Business Intelligence
How do cancer rates in your area compare to those in other areas?
BC Jung A Brief Introduction to Epidemiology - IV ( Overview of Vital Statistics & Demographic Methods) Betty C. Jung, RN, MPH, CHES.
Human Geography Population
KNOMAD, Migration Seminar New York, April World Population Prospects: an overview of the migration component François Pelletier United Nations.
The micro-geography of UK demographic change Paul Norman School of Geography, University of Leeds understanding population trends and processes.
Computing SubLHIN Population Projections in the South East Region August 2014 Update.
Internal migration flows in Northern Ireland: exploring patterns and motivations in a divided society Gemma Catney PhD Research Student Centre for Spatial.
Moderation & Mediation
1 POPULATION PROJECTIONS Session 2 - Background & first steps Ben Jarabi Population Studies & Research Institute University of Nairobi.
Uneven Intraurban Growth in Chinese Cities: A Study of Nanjing Yehua Dennis Wei Department of Geography and Institute of Public and International Affairs.
Using IPUMS.org Katie Genadek Minnesota Population Center University of Minnesota The IPUMS projects are funded by the National Science.
Why Is It There? Getting Started with Geographic Information Systems Chapter 6.
Spatial Data Analysis Yaji Sripada. Dept. of Computing Science, University of Aberdeen2 In this lecture you learn What is spatial data and their special.
Methodology for producing the revised back series of population estimates for Julie Jefferies Population and Demography Division Office for.
 Using Data for Demographic Analysis Country Course on Analysis and Dissemination of Population and Housing Census Data with Gender Concern October.
Sub-regional Workshop on Census Data Evaluation, Phnom Penh, Cambodia, November 2011 Evaluation of Census Data using Consecutive Censuses United.
Taking ‘Geography’ Seriously: Disaggregating the Study of Civil Wars. John O’Loughlin and Frank Witmer Institute of Behavioral Science University of Colorado.
© Copyright McGraw-Hill Correlation and Regression CHAPTER 10.
1 Research Methods Festival 2008 Zhiqiang Feng 1,2 and Paul Boyle 1 1 School of Geography & Geosciences University of St Andrews 2 The Centre for Census.
Sub-regional Workshop on Census Data Evaluation, Phnom Penh, Cambodia, November 2011 Evaluation of Age and Sex Distribution United Nations Statistics.
Comparison between Census and WIPR data Date: 21 September 2004.
United Nations Workshop on Revision 3 of Principles and Recommendations for Population and Housing Censuses and Evaluation of Census Data, Amman 19 – 23.
Grid-based Map Analysis Techniques and Modeling Workshop
An ecological analysis of crime and antisocial behaviour in English Output Areas, 2011/12 Regression modelling of spatially hierarchical count data.
2014-based National Population Projections Paul Vickers Office for National Statistics 2 December 2015.
The micro-geography of UK demographic change Paul Norman School of Geography, University of Leeds understanding population trends and processes.
Overview of Census Evaluation through Demographic Analysis Pres. 3 United Nations Regional Workshop on the 2010 World Programme on Population and Housing.
Patterns and Trends CE/ENVE 424/524. Classroom Situation Option 1: Stay in Lopata House 22 pros: spacious room desks with chairs built in projector cons:
Demographic models Lecture 2. Stages and steps of modeling. Demographic groups, processes, structures, states. Processes: fertility, mortality, marriages,
United Nations Regional Workshop on the 2010 World Programme on Population and Housing Censuses: Census Evaluation and Post Enumeration Surveys, Bangkok,
INTRODUCTION Despite recent advances in spatial analysis in transport, such as the accounting for spatial correlation in accident analysis, important research.
Why Is It There? Chapter 6. Review: Dueker’s (1979) Definition “a geographic information system is a special case of information systems where the database.
South West Public Health Observatory Using GIS in Public Health Luke Hounsome South-West Public Health Observatory.
Jean-Luc LIPATZ INSEE - France 2007/10 Using gridded census data to analyze socio-spatial structure of french cities Short history of grids in the INSEE.
Demographic Analysis Migration: Estimation Using Residual Methods -
Mortality: Introduction, Measurements
Meng Lu and Edzer Pebesma
Demographic Analysis and Evaluation
Spatial Data Analysis: Intro to Spatial Statistical Concepts
Spatial Data Analysis: Intro to Spatial Statistical Concepts
Overview of Census Evaluation through Demographic Analysis Pres. 3
Presentation transcript:

Methods of analysing change over time and space Ian Gregory (University of Portsmouth) & Paul Ell (Queens University, Belfast)

Advantages of temporal GIS data 1. Integration –Potentially any data with a spatial and a temporal reference can be integrated –Allows new data to be created 2. Analysis –Need to spot broad trends and places/times that show different patterns –Only limited techniques available: Multi-level modelling Geographically Weighted Regression (GWR) 3. Visualisation –Allows exploration of data and presentation of results In all cases we want to make best use of all of the available detail in the data (attribute, spatial and temporal)

Data integration: District-level net migration rates Net migration from the basic demographic equation NM t,t+n = (p t+n – p t ) - (B t,t+n - D t,t + n ) –Age and sex specific population, fertility and mortality data have been published decennially in Britain since the 1850s –Net migration for women aged 5 to 14 at the start of the decade can be calculated as: Females aged 15 to 24 at end of decade minus females aged 5 to 14 at start of decade minus number of deaths in the cohort through the decade –Problem: As net migration is the residual it is highly susceptible to error. In particular, the impact of any boundary changes will appear as migration. –Traditional studies: Most studies of net migration use county-level data to avoid boundary change issues Only use the census so are unable to sub-divide migrants by age/sex

Net migration through areal interpolation Standardise population and mortality data from many dates onto a single set of target units Integrate data from census and Registrar Generals Decennial Supplement Allows us to calculate net migration rates for males and females in ten-year cohorts from ages 5 to 14 to ages 55 to 64 (at start of decade).

Bristol Cheltenham Westbury Net migration rates among the 5 to 14 cohort Standardised time-series

Bristol Cheltenham Westbury Net migration rates among different cohorts in the 1920s Detailed attribute comparisons

Net migration: strengths and weaknesses Strengths: –From the census (comprehensive) –Can compute complete time-series from –Can be integrated with other aggregate information: Pop. density Employment Social class Proximity to coast/areas of natural beauty, etc. Weaknesses: –No information on flows –Low net mig. can be caused by high in and out mig. cancelling each other out –Ecological fallacy when analysing data

Other sources Pooley & Turnbull (1996) –Sample of 75,000 migrations by 16,000 people born created using genealogical societies. –Gives: Where each move was to and from (including grid references) When the move occurred Large amounts of attribute information on employment, family structure, etc. Strengths: –Detailed individual-level info Weaknesses: –Potentially biased sample –Doesnt include the young up to the present

Bringing them together –Both datasets are geo-referenced – can be integrated Allows: –Comparison of individual-level and ecological data (use of multi-level modelling) Tests whether ecological and individual level relationships are consistent Evaluates the accuracy of the sample Therefore: –Integrates different datasets –Makes full use of spatial, attribute and temporal information

Spatial analysis with GWR Global vs local analysis –Global analysis: Gives a single summary statistic or equation for whole study area Average relationship – implies spatial homogeneity –Local analysis: Allows parameters to vary over space Shows how relationships vary geographically Allows spatial heterogeneity

Geographically Weighted Regression Descriptive: Allows the relationship between the variables to vary over space by providing separate intercept and regression coefficients for every location on the map Test as to whether the model shows significant spatial variation Conventional regression: y i =a 0 +a 1 x 1i +a 2 x 2i +ε i GWR: y i =a 0 (u i,v i )+a 1 (u i,v i )x 1i +a 2 (u i,v i )x 2i +ε i –(u i,v i ) represents the coordinates of the ith point and a n (u i,v i ) is the impact of a n (u,v) at the ith point. This is implemented using a distance decay model

Example Global: LTLI i = UNEM i +31.1CROW i -3.5SPF i -22.5SC1 i -5.6DENS i Intercept UNEMDENS SC1

Mapping the R 2 i values Source: Fotheringham et al, 1998

Uses in spatio-temporal analysis –In C19 young women migrated as much as men but the spatial pattern differed significantly because of the different employment opportunities (main employers: domestic service, textiles) Conventional regression: –Mig proportional to DS and Text GWR: –Textiles attract women in Lancs/W. York –DS attracts women to wealthy areas eg West London, Cheltenham, Leamington Spa –Over time this pattern will become more complex and the differences between men and women will reduce