Comparing Internal Migration Around the GlobE (IMAGE): The Effects of Scale and Pattern

Transcription

1 Comparing Internal Migration Around the GlobE (IMAGE): The Effects of Scale and Pattern International Conference on Population Geographies, June 2013 Martin Bell, Elin Charles Edwards 1 John Stillwell, Konstantinos Daras 2 Marek Kupiszewski 3, Dorota Kupiszewska 4 and Yu Zhu 5 1. The University of Queensland, Australia 2. The University of Leeds, UK 3. Institute of Geography and Spatial Organization, PAS, Poland 4. International Organization for Migration, Poland 5. Fujian Normal University, China

2 The IMAGE Project An international collaborative program which aims to provide a robust basis for comparing internal migration between countries around the world Funded by Australian Research Council Discovery Project Project duration: The IMAGE Global Inventory of Internal Migration data The IMAGE Repository of Internal Migration data The IMAGE Studio Computes internal migration metrics Addresses key methodological issues

3 Outline Update on the IMAGE Inventory Update on IMAGE Repository Introduction to the IMAGE Studio Comparison of metrics for 15 countries with a focus on intensity, distance and impact Investigation of the MAUP scale effects and pattern effects Conclusions and next steps

4 IMAGE Inventory of Internal Migration Data Meta-data Collection Instrument Form of data Time interval Spatial framework Characteristics Values Census/Register/Survey Transitions/Events/Duration 1,2,5,other, undefined All moves, # of zones Age, sex Sources Systematic mining of census forms, surveys and websites Review of published papers and reports Advice from IMAGE project collaborators and country experts Survey of national statistical agencies

5 Summary of Countries Collecting Internal Migration Data by Region and Source Region Countries Data sources Census Register Survey Africa Asia Europe Latin America North America Oceania TOTAL

6 Building the IMAGE Repository of Internal Migration Data Collections Data assembled in the Repository National counts of all moves (by age) Origin-destination matrices (aggregate) Marginal totals (aggregate) Populations at risk Digital boundaries Number of zones Countries < > TOTAL 94

7 The IMAGE Studio The IMAGE studio is a flexible suite of software adaptable to a range of country-specific data inputs organized as a set of four linked subsystems: i. Data Preparation, ii. Spatial Aggregation, iii. Computation of Internal Migration Indicators, iv.spatial Interaction Modelling

8 IMAGE Studio: Framework

9 IMAGE Studio Subsystem Interfaces Data Preparation Spatial Aggregation Internal Migration Indicators Spatial Interaction Modelling

10 Why spatial aggregation? Every country has unique Basic Spatial Units (BSUs) different size (area and population) and shape (boundaries) Migration indicators depend upon how space is divided: MAUP (Openshaw 1984) Scale component: How does the indicator vary according to the number of Aggregated Spatial Regions (ASRs)? Pattern component: How does the indicator vary according to the configuration of ASRs at any spatial scale? We address the MAUP using a system which aggregates BSUs in a stepwise manner to identify the scale effect At each step, a series of random configurations of ASRs are produced to capture the pattern effect

11 Aggregation procedure Original Data Prepare boundary data, migration flow matrix and populations at risk for BSUs Manual Input Set step size, number of configurations and spatial aggregation method Data preparation Clean Data Store Data Generate a contiguity matrix for BSUs Set step size and number of configurations at each level Choose spatial aggregation method True True For Each level For Each conf. False False Store Data Run Spatial Aggregation Algorithm F

12 IRA-wave Algorithm Basic Spatial Units (16 BSUs) ) Select all neighbouring areas ) Select 2 random seeds ) Assign the selected areas to region Final Aggregation to Aggregate Spatial Regions (2 ASRs)

13 Example of aggregation: Germany 412 BSUs 200 ASRs 150 ASRs 100 ASRs 50 ASRs 10 ASRs

14 Comparisons between countries Sample of 15 countries with larger numbers of Basic Spatial Units Cross-national comparisons on three dimensions using six indicators Analysis of scale effects and pattern effects on each indicator Spatial aggregation using IRA wave with steps of 10 and 100 iterations at each step At each scale step, we take the indicator mean of the 100 configurations but also capture variation from the coefficient of variation or the maximum and minimum values for each set of ASRs

15 Sample countries Country Data type Year # BSU 1 Ghana 5yr Transition Brazil 5yr Transition Chile 5yr Transition Ecuador 5yr Transition Honduras 5yr Transition Mexico 5yr Transition ,439 7 Philippines 5yr Transition ,622 8 Canada 5yr Transition South Korea 5yrTransition Australia 1yr/5yr Transition United Kingdom 1yrTransition/Event 2001, Belgium Event Finland Event Germany Event Sweden Event

16 3 1Y transition Total Migrants 12 5Y transition Migrants (Millions) Australia UK 10 Migrants (Millions) Australia Canada Mexico Ghana Philippines Brazil Chile Ecuador Honduras S Korea Number of ASRs Number of ASRs 3 Events 2.5 Migrants (Millions) Belgium* Finland Germany Sweden Number of ASRs

17 Indicators of Internal Migration Dimensions identified in Bell et al. (2002) Journal of the Royal Statistical Society A Migration Intensity Migration Distance Migration Connectivity Migration Impact 1 Crude Migration Intensity 2 Standardized Migration Intensity 3 Gross Migraproduction Rate 4 Migration Expectancy 5 Courgeau s K 6 Peak Migration Intensity 7 Age at Peak Intensity 8 Mean/Median Distance Moved 9 Distance Decay Parameter 10 Index of Migration Connectivity 11 Index of Migration Inequality 12 Migration Weighted Gini 13 Coefficient of Variation 14 Migration Effectiveness Index 15 Aggregate Net Migration Rate

18 Comparing Migration Intensities A migration intensity is the proportion of a population changing residence in a specified time interval Encompasses both migration rates and probabilities Crude migration Intensity (CMI) is the migration count (M) divided by the population at risk (P): CMI R = M R / P where R is the number of ASRs Prior work (Long 1991; Bogue et al. 2010; Courgeau 1973)

19 Building on Courgeau s k Value of CMI n depends on the number of zones (n) Courgeau (1973) plotted CMI at multiple scales to define a linear relationship (k) Courgeau et al. (2012) plots CMI against ln [average households (H) per zone (n)] Algebraically, CMI n = a + b ln (H/n) When H/n = 1 (i.e. average of 1 household per zone) then CMI n = a (representing overall mobility) Simultaneously addresses the scale and pattern components of MAUP for migration intensities

20 Using the Courgeau et al. (2012) method year event Estimated overall mobility (a) Crude Migration Intensity (%) Sweden_E Germany_E Belgium_E Finland_E Ln (No of Households / No of ASRs)

21 CMIs using Courgeau s method Country R 2 Estimated CMIs (all moves) ranked Observed CMIs (all moves) Event Finland Event Belgium Event Sweden Event Germany Y transition Australia Y transition UK Y transition Australia Y transition Chile Y transition Canada Y transition South Korea Y transition Ghana Y transition Brazil Y transition Ecuador Y transition Honduras Y transition Mexico Y transition Philippines

22 Australia, 1 year transition Australia 1Y Australia(obs) Crude Migration Intensity (%) y = x y = x x ln (No of Households / No of ASRs) The relationship between CMI and distance may not always be linear

23 Comparing Migration Distance Many studies have identified the negative influence of distance on migration since Ravenstein s law in 1885 indicating that The majority of migrants go only a short distance including: Stewart (1941); Zipf (1946); Lee (1966); Lowry (1966); Wilson (1967); Tobler (1970); Stillwell (1978); Fotheringham (1980); Flowerdew (1982); Plane (1984);.. and more recently: ODPM (2002); Fotheringham et al. (2004); Kalogirou (2005);. Dennett and Wilson (2011);... Range of different model formulations and calibration methods for capturing the frictional effect of distance

24 Spatial Interaction Model (SIM) Modelling would typically involve calibrating a model for a selected set of BSUs, e.g. fitting a doubly constrained SIM: M ij = A i O i B j D j d ij -β where O i = the out-migration from zone i to all other zones D j = the in-migration to zone j from all other zones A i and B j = balancing factors that ensure the constraints are satisfied d ij - β = a linear distance decay function with parameter β Mean distance migrated (MDM) is computed directly based on inter-bsu migration flow and distance matrices MDM = Σ i j Σ j i M ij d ij / Σ i j Σ j i M ij

25 Key question What happens to the β parameter and MDM and when we progressively aggregate each set of Basic Spatial Units (BSUs) to Aggregated Spatial Regions (ASRs)? At each scale step, we take the mean β and MDM values of the 100 configurations but also capture variation from the maximum and minimum values for each set of ASRs These sets of values for different spatial levels tell us more about the inverse migration v distance relationship than values for single geographies

26 Mean Mean Distance Distance Migrated Migrated (Km) (Km) Mean Migration Distance by number of ASRs Finland UK Germany Finland Sweden Sweden UK Australia Number of ASRs

27 Decay parameter by number of ASRs Beta Value UK Germany Sweden 1 Finland Australia Number of ASRs

28 Decay parameters using SIM Event 1yr MDM > 200 km MDM < 200 km GROUP Mean Beta Value A/A Country Data type # BSU All BSUs 50 ASRs ASRs ASRs 1 Ecuador 5yr Transition South Korea 5yr Transition Ghana 5yr Transition Philippines 5yr Transition 1, Honduras 5yr Transition Brazil 5yr Transition Mexico 5yr Transition 2, a Australia 5yr Transition Chile 5yr Transition Canada 5yr Transition United Kingdom 1yr Transition b Australia 1yr Transition Belgium Event Germany Event Finland Event Sweden Event

29 Comparing migration impact The most significant aspect of internal migration is how it alters the spatial distribution of populations Does impact vary at different spatial scales and between countries? How do we measure this impact? Aggregate net migration rate ANMR = 100 * 0.5 i D i -O i / P D i = inflows to i, O i = outflows from i, P = total population ANMR represents the net system-wide redistribution per 100 persons

30 Aggregate Net Migration Rate by number of ASRs year events Aggregate Net Migration Rate Finland Germany Number of ASRs

31 Aggregate Net Migration Rate by ln (number of ASRs) year events Aggregate Net Migration Rate Aggregate Net Migation Rate ln (Number of ASRs) year transition Belgium Finland Germany Sweden Australia Ghana Honduras Brazil Mexico Ecuador Chile ln (Number of ASRs) Philippines

32 Determinants of ANMR ANMR = MEI * CMI / 100 where Migration Effectiveness Index (MEI) measures the overall degree of symmetry between inflows and outflows within a migration system MEI = 100 * i D i -O i / i (D i +O i ) MEI captures the net system-wide redistribution per 100 migrants

33 Migration Effectivness Index by number of ASRs

34 Relationship between ANMR, CMI and MEI ANMR = MEI * CMI / 100 =

35 CMI and MEI values for 50 and 100 ASR for 5 year transition data by ANMR value No of ASR No of ASR ANMR CMI MEI ANMR CMI MEI South Korea 0,47 9,08 5,13 South Korea 0,59 10,25 5,72 Philippines 0,51 2,80 17,95 Philippines 0,60 3,21 18,78 Mexico 0,81 4,60 17,55 Mexico 0,98 5,12 19,23 Ghana 0,90 5,26 17,02 Brazil 1,11 5,54 20,06 Brazil 0,91 4,86 18,64 Ghana 1,28 5,88 21,78 Australia 1,11 14,32 7,74 Australia 1,31 16,72 7,83 Honduras 1,26 4,81 26,26 Honduras 1,37 5,33 25,69 Ecuador 1,60 6,84 23,44 Ecuador 1,77 7,43 23,80 Chile 1,84 12,10 15,21 Chile 2,23 13,52 16,52 Canada 2,96 10,83 27,32 Canada 3,18 12,25 26,02

36 Patterns of population redistribution (BSUs) Belgium Brazil Net Migration Rate ( per 1000) Net Migration Rate ( per 1000) > Population density (persons per km2) Population density (persons per km2)

37 Conclusions The simplest solution to the MAUP is to pretend it does not exist and hope that the results being produced for ad hoc zoning systems will still be meaningful or least interpretable (Openshaw, 1984)

38 MAUP: Scale Effect Conclusions There are systematic regularities in the behaviour of summary migration indicators in relation to scale These regularities can be useful in estimating parameters at other spatial scales These regularities break down at very coarse levels of aggregation e.g. <30 regions This can be problematic for countries which do not have data at fine levels of spatial disaggregation

39 Conclusions MAUP: Pattern Effect The pattern effect becomes more problematic the smaller the number of ASRs, because variability increases Observed values of internal migration indicators based on standard statistical geographies may be outliers with respect to the mean configurations of zones at equivalent spatial scales and are therefore misleading for crossnational comparisons Both the scale and pattern effects of the MAUP do matter. Our results show they impact on migration in systematic ways. However, these manifest differently between countries.

40 Intensity Conclusions CMI varies systematically with spatial scale Scale effects can be exploited to generate a measure of aggregate population mobility which is comparable across countries Distance The frictional effect of distance varies between countries Countries exhibit systematic variations in the frictional effect of distance which may rise, fall, or remain stable with increasing distance Impact ANMR varies systematically with the CMI with changing scale The MEI is relatively stable except at low numbers of ASRs Differences in the ranking of countries on the ANMR compared with their ranking on the CMI are due to variations in the MEI

41 Next steps How much closer are we to methods for crossnational comparison? CMI: Closer use Courgeau given multiple data points Distance and Impact: Closer - as we now know that distance and impact vary in a well behaved fashion MAUP: explore ASRs based on Objective Functions Equality (e.g. ASRs with equal populations) Similarity (e.g. ASRs with similar population densities) Develop league tables based on key indicators Investigate how the internal migration indicators vary for population sub-groups and over time We invite collaborations using the IMAGE Studio.