GEOG110 Migration
Migration
Migration is a key process in human geography.
Cartogram: Distorts the normal land area map for something like population or income.
Borders and Migration:
Post-COVID Migration Surge: Borders closed, then a surge in migration.
Recent Shift: Record numbers leaving New Zealand, mainly to Australia.
Political Contentiousness: Migration is a politically sensitive topic.
Randomness: Passport access is somewhat random, influenced by birth and history.
Border Artificiality: Borders are artificially constructed and control movement.
Detroit Case Study
Detroit is a case study of population loss impact, leading to social and spatial inequalities.
Population loss consequences: Abandonment and decay of physical infrastructure.
Historical Context: Detroit was the fourth largest city in the US due to the automotive industry, followed by population decline.
Potential Solutions for Population Loss:
Regeneration, urban renewal, central city living promotion.
Job creation schemes and entrepreneurial activity.
Tax reduction to stimulate growth.
Upskilling and reskilling initiatives.
Urban farming.
Christchurch Example
Earthquake Impact: Population loss in specific areas.
Population Changes: Population rising and falling in areas like the Avon Corridor and growth in Halswell, Rolleston.
Impact on Geographies: Migration impacts social, political, and cultural geographies.
Urban Migration Models
Explain and evaluate models of urban migration and neighborhood change.
Understand consequences of migration.
Understand the concept of statistical significance.
Forced vs. Voluntary Migration
Forced: Due to civil war, famine, natural disasters.
Voluntary: Due to job relocation, retirement, personal choice.
Theoretical Models
Value Expectancy Model: People move expecting improvement in living environment.
Stress Model: People move when stressors exceed tolerance.
Neighborhood Change Model: Neighborhoods go through stages from stable to nonviable.
Value Expectancy Model
Decision-making: Based on individual, household, cultural, social norms, personal traits, opportunities, and information.
Stress Model
Localized considerations: Triggered by events like lease expiration, job loss, traffic.
Neighborhood Change Model
Five stages: Stable, minor decline, clear decline, heavily deteriorated, nonviable.
Driven by property prices.
Statistical Significance
Determine association between two variables.
5% significance level: Relationship is not by chance.
P-value: Denoted as . If , not statistically significant. If , statistically significant.
P-Value Examples
If significance level is 0.050.05, then:
0.010.01 is significant because it is less than 0.050.05, indicating a strong statistical association.
0.10.1 is not significant because it is greater than 0.050.05, suggesting the relationship could be due to random chance.
0.050.05 is significant because it is equal to the set significance level, representing the threshold for statistical significance.
Real-Life Correlation Examples
Perfect Positive Correlation: r=1, p-value=0.01 (significant). This indicates that as one variable increases, the other variable also increases perfectly, with a very low probability that the relationship is due to chance.
Perfect Negative Correlation: r=-1, p-value=0.02 (significant). This shows a perfect inverse relationship; as one variable increases, the other decreases perfectly, also with a low chance of being random.
No Significance: Scattered points, p-value>0.05 (not significant). This means there is no discernible pattern between the variables, and the high p-value suggests any apparent relationship is likely due to random variation.
Deprivation and Population Change
Negative correlation and significant relationship (e.g., r = -0.2, p < 0.05).
As population declines, deprivation is generally higher, and vice versa.
Selective Migration: People move to more affluent areas as they improve economically.
UK Migration Example
Changes in London: In some parts of London, the population of white British residents decreased from 80% in 2001 to 50% by 2011.
Data showed many people were choosing to retire and move to the countryside after their houses increased in value.