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We present a data visualisation approach to support the rapid humanitarian response during the 2024 Valparaíso wildfires in Chile. Combining Meta user location data for population displacement, NASA FIRMS satellite imagery for wildfire locations, and census data, we identify key origins and destinations of displaced people during recent wildfires in Valparaíso, Chile. Our choropleth maps reveal spatial patterns of movement and socioeconomic factors, demonstrating the value of integrating diverse data sources for near real-time crisis response.

Estudios previos en países del Norte Global han documentado un descenso de los movimientos internos de población durante la pandemia de COVID-19, acompañado de un aumento de los desplazamientos desde las grandes ciudades hacia áreas menos densamente pobladas. Sin embargo, no hay evidencias consistentes sobre el efecto de la pandemia en los movimientos de población a través de la jerarquía urbana en el Sur Global. En esta investigación se utilizan datos de telefonía móvil de usuarios de Facebook entre abril de 2020 y mayo de 2022 para analizar cómo cambiaron las intensidades y los patrones de movilidad de larga distancia (>100 km) entre diferentes categorías de densidad de población durante la COVID-19 en México. Los resultados muestran un descenso del 40% en los movimientos de usuarios en el periodo de abril a diciembre de 2020. Al contrario que en los países desarrollados, no se identificó un aumento en los desplazamientos desde las grandes ciudades, sino un descenso generalizado tanto en los flujos de salida como en los de entrada en todas las categorías de densidad de población, excepto en los movimientos interrurales. Además, la mayor reducción se registró en los desplazamientos desde y hacia las grandes ciudades, que cayeron 50%. Al igual que en el Norte Global, las intensidades y los patrones de movilidad recuperaron progresivamente los valores y tendencias observados con anterioridad a la COVID-19 durante 2021 y 2022, aunque las grandes ciudades mostraron una recuperación más lenta y siguieron registrando movimientos de salida y entrada ligeramente inferiores aún en mayo de 2022.

While the number of international students attending UK universities has been increasing in recent years, the 2021/22 and 2022/23 academic years saw a decline in applications from EU-domiciled students. However, the extent and varying impact of this decline remain to be estimated and disentangled from the impacts of the COVID-19 pandemic. Using difference-in-differences (DID) in a hierarchical regression framework and Universities and Colleges Admissions Service (UCAS) data, we aim to quantify the decline in the number of student applications post-Brexit. We find evidence of an overall decline of 65% in the 2021 academic year in successful applications from EU students as a result of Brexit. This decline is more pronounced for non-Russell Group institutions, as well as for Health and Life Sciences and Arts and Languages. Furthermore, we explore the spatial heterogeneity of the impact of Brexit across EU countries of origin, observing the greatest effects for Poland and Germany, though this varies depending on institution type and subject. We also show that higher rates of COVID-19 stringency in the country of origin led to greater applications for UK higher education institutions. Our results are important for government and institutional policymakers seeking to understand where losses occur and how international students respond to external shocks and policy changes. Our study quantifies the distinct impacts of Brexit and COVID-19 and offers valuable insights to guide strategic interventions to sustain the UK’s attractiveness as a destination for international students.

Immigration is one of the most salient topics in public debate. Social media heavily influences opinions on immigration, often sparking polarized debates and offline tensions. Studying 220,870 immigration-related tweets in the UK, we assessed the extent of polarization, key content creators and disseminators, and the speed of content dissemination. We identify a high degree of online polarization between pro and anti-immigration communities. We found that the anti-migration community is small but denser and more active than the pro-immigration community with the top 1% of users responsible for over 23% of anti-immigration tweets and 21% of retweets. We also discovered that anti-immigration content spreads also 1.66 times faster than pro-immigration messages and bots have minimal impact on content dissemination. Our findings suggest that identifying and tracking highly active users could curb anti-immigration sentiment, potentially easing social polarization and shaping broader societal attitudes toward migration.

ABSTRACTWe assess if a 'donut effect' has occurred in Spain during the COVID-19 pandemic. Our results show a synchronized 30% drop in housing purchases during the national lockdown in 2020, followed by a significant increase from 2021 across the rural-urban continuum, including core cities, suburbs, towns and rural areas, exceeding pre-pandemic levels. We thus found no evidence of a ‘donut effect’ in Spain.

Global warming is increasing the frequency of extreme weather events leading to an increased risk of large-scale population displacements. Since June 2022, Pakistan has recorded destructive flash flooding resulting from melting glaciers and torrential monsoon rainfall. Emergency responses have documented flood-related deaths, injuries and damaged infrastructure – less is known about population displacements resulting from recent floods. Information about these populations and mobility is critical to ensure the appropriate delivery of humanitarian assistance where it is most needed. Lack of granular spatial data in real time has been a key barrier. This article uses digital footprint data from Meta Facebook to identify the patterns of population displacement in Pakistan in near-real time.

Abstract Low fertility is set to worsen economic problems in many developed countries, and maternity, paternity, and parental leave have emerged as key pro-natal policies. Gender inequity in the balance of domestic and formal work has been identified as a key driver of low fertility, and leave can potentially equalise this balance and thereby promote fertility. However, the literature contends that evidence for the effect of leave on fertility is mixed. We conduct the first systematic review on this topic. By applying a rigorous search protocol, we identify and review empirical studies that quantify the impact of leave policies on fertility. We focus on experimental or quasi-experimental studies that can identify causal effects. We identify 11 papers published between 2009 and 2019, evaluating 23 policy changes across Europe and North America from 1977 to 2009. Results are a mixture of positive, negative, and null impacts on fertility. To explain these apparent inconsistencies, we extend the conceptual framework of Lalive and Zweimüller (2009), which decomposes the total effect of leave on fertility into the “current-child” and “future-child” effects. We decompose these into effects on women at different birth orders, and specify types of study design to identify each effect. We classify the 23 studies in terms of the type of effect identified, revealing that all the negative or null studies identify the current-child effect, and all the positive studies identify the future-child or total effect. Since the future-child and total effects are more important for promoting aggregate fertility, our findings show that leave does in fact increase fertility when benefit increases are generous. Furthermore, our extensions to Lalive and Zweimüller’s conceptual framework provide a more sophisticated way of understanding and classifying the effects of pro-natal policies on fertility. Additionally, we propose ways to adapt the ROBINS-I tool for evaluating risk of bias in pro-natal policy studies.

Drawing on register records from 2019 to 2021, we analyse the impact of COVID-19 on internal and international migration across the 50 Spanish provinces (NUTS-3 regions). Our results show that net-international migration declined in all the provinces during the pandemic, particularly in high-population-density areas. Certain depopulated provinces registered significant positive net-internal migration rates, while the most populous areas displayed population losses through internal migration. Generally, the total migration balance decreased in most provinces, primarily driven by the drop in international migration. Changes in internal and international migration persisted over time, although patterns tended to converge to pre-pandemic levels in late 2021.

Previous studies have examined the impact of COVID-19 on mortality and fertility. However, little is known about the effect of the pandemic on constraining international migration. We use Eurostat and national statistics data on immigration and ARIMA time-series models to quantify the impact of COVID-19 on immigration flows in 15 high-income countries by forecasting their counterfactual levels in 2020, assuming no pandemic, and comparing these estimates with observed immigration counts. We then explore potential driving forces, such as stringency measures and increases in unemployment moderating the extent of immigration change. Our results show that immigration declined in all countries, except in Finland. Yet, significant cross-national variations exist. Australia (60%), Spain (45%) and Sweden (36%) display the largest declines, while immigration decreased by between 15% and 30% in seven countries, and by less than 15% in four nations where results were not statistically significant. International travel restrictions, mobility restrictions and stay-at-home requirements exhibit a relatively strong relationship with declines in immigration, although countries with similar levels of stringency witnessed varying levels of immigration decline. Work and school closings and unemployment show no relationship with changes in immigration.

This editorial describes improvements REGION implemented in the first months of 2022. Although they are not directly visible to authors or readers, they are important for the quality of the journal and for the visibility of the articles published in REGION.

The spatial distribution of activities and agents within cities, conceptualised as an urban function, profoundly affects how different areas are perceived and lived. This dataset introduces the concept of functional signatures - contiguous areas of a similar urban function delineated based on enclosed tessellation cells (ETC) - and applies it to the area of Great Britain. ETCs are granular spatial units, which capture function based on interpolations from open data inputs stretching from remote sensing to land use, census and points of interest data. The spatial extent of each signature type is defined by grouping ETCs using cluster analysis, based on similarity between their functional profiles, inferred by the data linked to each cell. This approach results in a dataset that reflects urban function as a composite of aspects, rather than a singular use, and is built up from granular spatial units. Furthermore, the underlying data are sourced from available open data products, which together with a method and code fully available, yields a fully reproducible pipeline and makes our dataset and open data product.Both the final classification composed of 17 types of functional signatures and the underlying data collected on the level of enclosed tessellation cells are included in the release and described in this report.

Over the past two decades, family structures have diversified. International migration has led to a rise in the number of families in which at least one parent is foreign-born. Increases have also been observed in both the rate of partnership separation, leading to a greater number of single-parent households and an increase in the number of families where grandparents have assumed caring responsibilities for their grandchildren. Evidence indicates a strong relationship between family structure and children's educational outcomes. Parental involvement is well documented as a key ingredient for the educational success of children. Drawing on Taiwanese multi-wave survey data (Taiwan Assessment of Student Achievement) and a machine-learning-based propensity score algorithm for multiple treatments, this paper aims to determine the various relationships between children from different household structures (two-parent households, skipped generation households, single-parent households, and immigrant households) and their cognitive knowledge (measured by test scores). Key findings reveal that children from skipped generation households achieve the lowest performance scores and that those from immigrant households tend to perform even better than children from traditional two-parent households in certain disciplines. Our results suggest that policy interventions targeted at providing remedial education and/or financial assistance are needed to support children from skipped generation families to redress existing educational disadvantages in Taiwan.

Internal migration has replaced fertility and mortality as the primary demographic process shaping the spatial distribution of populations within countries. While a rich comparative literature has examined the intensity, composition and spatial impacts of population movement in Europe, the spatial structure of internal migration flows is less well understood. We present a flow map of internal migration flows within 38 European countries using the most recent data available. The graphic reveals the major role of national capital cities in the internal migration system, and an array of distinctive patterns of internal migration fostering population concentration in Northern, Central and Eastern Europe, and population deconcentration in Western and Southern Europe.

COVID-19 is unique in that it is the first global pandemic occurring amidst a crowded information environment that has facilitated the proliferation of misinformation on social media. Dangerous misleading narratives have the potential to disrupt ‘official’ information sharing at major government announcements. Using an interrupted time-series design, we test the impact of the announcement of the first UK lockdown (8–8.30 p.m. 23 March 2020) on short-term trends of misinformation on Twitter. We utilise a novel dataset of all COVID-19-related social media posts on Twitter from the UK 48 hours before and 48 hours after the announcement (n = 2,531,888). We find that while the number of tweets increased immediately post announcement, there was no evidence of an increase in misinformation-related tweets. We found an increase in COVID-19-related bot activity post-announcement. Topic modelling of misinformation tweets revealed four distinct clusters: ‘government and policy’, ‘symptoms’, ‘pushing back against misinformation’ and ‘cures and treatments’.

Large-scale coordinated efforts have been dedicated to understanding the global health and economic implications of the COVID-19 pandemic. Yet, the rapid spread of discrimination and xenophobia against specific populations has largely been neglected. Understanding public attitudes toward migration is essential to counter discrimination against immigrants and promote social cohesion. Traditional data sources to monitor public opinion are often limited, notably due to slow collection and release activities. New forms of data, particularly from social media, can help overcome these limitations. While some bias exists, social media data are produced at an unprecedented temporal frequency, geographical granularity, are collected globally and accessible in real-time. Drawing on a data set of 30.39 million tweets and natural language processing, this article aims to measure shifts in public sentiment opinion about migration during early stages of the COVID-19 pandemic in Germany, Italy, Spain, the United Kingdom, and the United States. Results show an increase of migration-related Tweets along with COVID-19 cases during national lockdowns in all five countries. Yet, we found no evidence of a significant increase in anti-immigration sentiment, as rises in the volume of negative messages are offset by comparable increases in positive messages. Additionally, we presented evidence of growing social polarization concerning migration, showing high concentrations of strongly positive and strongly negative sentiments.

As cities expand, human mobility has become a central focus of urban planning and policy making to make cities more inclusive and sustainable. Initiatives such as the "15-minutes city" have been put in place to shift the attention from monocentric city configurations to polycentric structures, increasing the availability and diversity of local urban amenities. Ultimately they expect to increase local walkability and increase mobility within residential areas. While we know how urban amenities influence human mobility at the city level, little is known about spatial variations in this relationship. Here, we use mobile phone, census, and volunteered geographical data to measure geographic variations in the relationship between origin-destination flows and local urban accessibility in Barcelona. Using a Negative Binomial Geographically Weighted Regression model, we show that, globally, people tend to visit neighborhoods with better access to education and retail. Locally, these and other features change in sign and magnitude through the different neighborhoods of the city in ways that are not explained by administrative boundaries, and that provide deeper insights regarding urban characteristics such as rental prices. In conclusion, our work suggests that the qualities of a 15-minutes city can be measured at scale, delivering actionable insights on the polycentric structure of cities, and how people use and access this structure.

Satellite imagery is often used to study and monitor Earth surface changes. The open availability and extensive temporal coverage of Landsat imagery has enabled changes in temperature, wind, vegetation and ice melting speed for a period of up to 46 years. Yet, the use of satellite imagery to study cities has remained underutilised, partly due to the lack of a methodological approach to capture features and changes in the urban environment. This notebook offers a framework based on Python tools to demonstrate how to batch-download high-resolution satellite imagery; and enable the extraction, analysis and visualisation of features of the built environment to capture long-term urban changes.

Understanding public opinion towards immigrants is key to prevent acts of violence, discrimination and abuse. Traditional data sources, such as surveys, provide rich insights into the formation of such attitudes; yet, they are costly and offer limited temporal granularity, providing only a partial understanding of the dynamics of attitudes towards immigrants. Leveraging Twitter data and natural language processing, we propose a framework to measure attitudes towards immigration in online discussions. Grounded in theories of social psychology, the proposed framework enables the classification of users’ into profile stances of positive and negative attitudes towards immigrants and characterisation of these profiles quantitatively summarising users’ content and temporal stance trends. We use a Twitter sample composed of 36 K users and 160 K tweets discussing the topic in 2017, when the immigrant population in the country recorded an increase by a factor of four from 2010. We found that the negative attitude group of users is smaller than the positive group, and that both attitudes have different distributions of the volume of content. Both types of attitudes show fluctuations over time that seem to be influenced by news events related to immigration. Accounts with negative attitudes use arguments of labour competition and stricter regulation of immigration. In contrast, accounts with positive attitudes reflect arguments in support of immigrants’ human and civil rights. The framework and its application can inform policy makers about how people feel about immigration, with possible implications for policy communication and the design of interventions to improve negative attitudes.

The classical foundations of migration research date from the 1880s with Ravenstein’s “Laws of migration”, which represent the first comparative analyses of internal migration. While his observations remain largely valid, the ensuing century has seen considerable progress in data collection practices and methods of analysis, which in turn has permitted theoretical advances in understanding the role of migration in population redistribution. Coupling the extensive range of migration data now available with these recent theoretical and methodological advances, we endeavour to advance beyond Ravenstein’s understanding by examining the direction of population redistribution and comparing the impact of internal migration on patterns of human settlement in 27 European countries. Results show that the overall redistributive impact of internal migration is low in most European countries but the mechanisms differ across the continent. In Southern and Eastern Europe migration effectiveness is above average but is offset by low migration intensities, whereas in Northern and Western Europe high intensities are absorbed in reciprocal flows resulting in low migration effectiveness. About half the European countries are experiencing a process of concentration toward urbanised regions, particularly in Northern, Central and Eastern Europe, whereas countries in the West and South are undergoing a process of population deconcentration. These results suggest that population deconcentration is now more common than it was in the 1990s when counterurbanisation was limited to Western Europe. The results show that 130 years on, Ravenstein’s law of migration streams and counter-streams remains a central facet of migration dynamics, while underlining the importance of simple yet robust indices for the spatial analysis of migration. * This article belongs to a special issue on “Internal Migration as a Driver of Regional Population Change in Europe: Updating Ravenstein”.

This study explores longitudinal relationships between material, psycho-social and behavioural social determinants of health and multimorbidity of people aged 50 years or older in England. We used data from the English Longitudinal Study of Ageing collected biannually between 2002 and 2015. Apart from the basic measure of multimorbidity (two or more diseases within a person) we constructed two distinct measures of health in order to take into account the biology of ageing (complex multimorbidity and multiple functional limitations).We found that the likelihood of multimorbidity and multiple functional limitations was consistently associated with the levels of household wealth, sense of control over one's life, physical activity and loneliness. Larger health inequalities were observed when health was measured as complex multimorbidity and multiple functional limitations than basic multimorbidity. Compared to the population group with the highest wealth, those with the lowest wealth had 47% higher odds of basic multimorbidity (95% C.I. 1.34-1.61), 73% higher odds of complex multimorbidity (95% C.I. 1.52-1.96) and 90% higher odds of having 10 or more functional limitations (95% C.I. 1.59-2.26). We did not find a dose-response relationship between alcohol consumption, smoking and multimorbidity but rather evidence of people in ill health actively moderating their health behaviour.We suggest that materialist models of multimorbidity and functional limitation at older age can not, on their own, explain the health inequalities as the behavioural and psycho-social factors play an important role. Policies aiming to reduce the risk of multimorbidity and functional limitation should address the issue at these three levels simultaneously, using the existing national infrastructure of General Practices. Keywords: Multimorbidity, Complex multimorbidity, Multiple functional limitations, Social determinansts of health, Ageing, Health inequalities

This study aimed to estimate the prevalence of three measures of multimorbidity among people aged 50 years or older in England. Beside the basic measure of two or more diseases within a person, we added a measure of three or more affected body systems (complex multimorbidity) and a measure of 10 or more functional limitations. We found that the three health outcomes became more prevalent between 2002 and 2015. They were more common among females than males and were becoming more common among younger age groups. While in 2002, the prevalence of basic multimorbidity overcame 50% from the 70–74 age group upwards, in 2015 it crossed the same threshold in the 65–69 age group. The distribution of multimorbidity and multiple functional limitations were stratified by the amount of household wealth. Multiple functional limitations reflected the largest differences between the most and the least affluent groups (5.9-fold in 2014/2015), followed by the measure of complex multimorbidity (2.8-fold in 2014/2015) and basic multimorbidity (1.9-fold) in 2014/2015.While age acted as a levelling factor for the wealth differences in basic multimorbidity, it had no such effect on the two other outcomes. Our study observed social polarization among multimorbid ageing population in England where complex multimorbidity and multiple functional limitations increase faster and reflect stronger inequality than basic multimorbidity.

Interdisciplinary education has become increasingly prominent as a core instrument to prepare the next generation workforce. Yet, little is known about the impacts of long-term degree-oriented interdisciplinary education on post-graduation outcomes. This paper aims to investigate the influence of long-term degree-oriented interdisciplinary education on graduates' post-graduation plan choices and labour market outcomes using unique administrative micro-data and career-tracking data from a comprehensive research university. Our results indicate that higher levels of engagement in interdisciplinary learning increase the probability of pursuing future study and employment in a field that differs from a graduate's college degree, and are also associated with a higher probability to enter the workforce. Yet, this positive association between interdisciplinary learning and the probability of entering the workforce is specific to graduates from traditional disciplinary programmes. Additionally, our findings suggest that the impact of long-term degree-oriented interdisciplinary education on early labour market outcomes is overall beneficial but varies across degree fields. The findings of our study provided partial evidence in support of the influence of long-term degree-oriented interdisciplinary education on post-graduation plan choices and early labour market outcomes. We suggest that given resource constraints, higher education institutions could consider relaxing module choice restrictions in traditional disciplinary programmes to promote interdisciplinary education. However, it is important to note that higher education institutions should carefully consider the potential trade-off between the breadth of interdisciplinary content and the depth of specialist content, in order to strike a balance.

En el marco del debate sobre las denominadas mutaciones metropolitanas, la diversificación social de la periferia y las tendencias de la segregación socioeconómica residencial, se estima el efecto de la migración interna sobre la composición educativa de ámbitos intrametropolitanos y la segregación residencial, medida con el índice de disimilitud de varios grupos educativos, en Santiago, Chile. Se usa una metodología novedosa desarrollada en CELADE que utiliza microdatos censales. Los resultados indican que la migración contribuye a diversificar socioeducativamente a la periferia y es decisiva para la emergencia de núcleos de alta educación en zonas históricamente desaventajadas de la periferia. Pero, contra la narrativa predominante, la migración no reduce la segregación residencial, principalmente porque disminuye los niveles educativos en el pericentro, más bien pobre y en proceso de pauperización y, en cambio, los aumenta en la zona acomodada.

Changes in zonal boundaries and coding schemes severely compromise temporal comparison of data. In Chile, the Population and Housing census is the only comprehensive source of internal migration data, but municipal boundaries and occupation and industry sector coding schemes have undergone various changes which hamper the temporal comparability of census data. This paper presents the CHilean Internal Migration database developed by Rowe and Bell (2013) which provides a temporally consistent framework for the analysis of internal migration over a period covering twenty-five years from 1977 to 2002. Specifically, it offers a hierarchical system of 304 municipalities, 51 provinces and 13 regions, and 10 occupational groups and 11 industrial sectors which are temporally consistent over the 1977-82, 1987-92 and 1997-2002 census intervals. The database can be downloaded from: https://s3.amazonaws.com/geoda/data/CHIM.zip.

Administrative areas are arbitrarily designed and do not necessarily reflect the geographical patterns of socio-economic and labour market activity. Labour market areas (LMAs) are required to analyse spatial labour market activity and provide a framework to guide spatially-explicit employment policy development. This resource describes a data source of a set of recently created labour market areas for Chile.

While considerable progress has been made in understanding the way particular aspects of internal migration, such as its intensity, age profile and spatial impact, vary between countries around the world, little attention to date has been given to establishing how these dimensions of migration interact in different national settings. We use recently developed measures of internal migration that are scale-independent to compare the overall intensity, age composition, spatial impact, and distance profile of internal migration in 19 Latin American countries. Comparisons reveal substantial cross-national variation but cluster analysis suggests the different dimensions of migration evolve systematically to form a broad sequence characterised by low intensities, young ages at migration, unbalanced flows and high friction of distance at lower levels of development, trending to high intensities, an older age profile of migration, more closely balanced flows and lower friction of distance at later stages of development. However, the transition is not linear and local contingencies, such as international migration and political control, often distort the migration-development nexus, leading to unique migration patterns in individual national contexts.

We develop a method based on computer vision and a hierarchical multilevel model to derive an Urban Street Tree Vegetation Index which aims to quantify the amount of vegetation visible from the point of view of a pedestrian. Our approach unfolds in two steps. First, areas of vegetation are detected within street-level imagery using a state-of-the-art deep neural network model. Second, information is combined from several images to derive an aggregated indicator at the area level using a hierarchical multilevel model. The comparative performance of our proposed approach is demonstrated against a widely used image segmentation technique based on a pre-labelled dataset. The approach is deployed to a real-world scenario for the city of Cardiff, Wales, using Google Street View imagery. Based on more than 200,000 street-level images, an urban tree street-level indicator is derived to measure the spatial distribution of tree cover, accounting for the presence of obstructing objects present in images at the Lower Layer Super Output Area (LSOA) level, corresponding to the most commonly used administrative areas for policy-making in the United Kingdom. The results show a high degree of correspondence between our tree street-level score and aerial tree cover estimates. They also evidence more accurate estimates at a pedestrian perspective from our tree score by more appropriately capturing tree cover in areas with large burial, woodland, formal open and informal open spaces where shallow trees are abundant, in high density residential areas with backyard trees, and along street networks with high density of high trees. The proposed approach is scalable and automatable. It can be applied to cities across the world and provides robust estimates of urban trees to advance our understanding of the link between mental health, well-being, green space and air pollution.

This paper investigates the connection between national immigration policy and a society’s attitudes towards immigration. It argues that a country’s immigration policy framework plays an important role in the formation of attitudes towards immigration by shaping the local national context of the receiving country. It examines the influence of a country’s immigration policy framework by contrasting two countries – Australia and Germany – that developed remarkably different immigration policies in response to large immigration movements during the post-war period. We explore attitudes towards immigration on four dimensions: (1) the national economy, (2) the labour market, (3) the national culture, and (4) the level of immigrant influx. The analyses reveal three main findings. First, people in Australia tend to display more positive attitudes towards immigration than in Germany. Second, in both countries, attitudes towards immigration tend to be influenced in a similar way by an individual’s socio-economic background and feelings of national identity (in the form of nationalism and patriotism). Third, immigration policy represents a strong indicator of attitudes towards immigration. We found that the planned integrative immigration policy in Australia supports the formation of more positive attitudes towards immigration by influencing people’s perception on the economic and socio-cultural impacts of immigration.