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.
Technological advances have enabled the emerge of ‘Big Data’through the production, processing, analysis and storage of large volumes of digital data. Data that could not previously be stored or used to be captured using analog devices can now be digitally recorded. This chapter identifies and discusses the existing and future challenges and opportunities of Big Data for human geography. Big Data offer high geographic and temporal granularity, extensive coverage and instant information to transform our understanding of human interactions and our social world. At the same time, Big Data present major epistemological, methodological and ethical challenges which need to be addressed to realise these opportunities. I identify the key challenges and actions for the future of human geography emerging from the use of Big Data.
Immigration is a key ingredient for social cohesion and economic development. Yet, it is often portrayed as a major threat to national identity, values, economic stability and security, resulting in acts of intolerance, discrimination, racism, xenophobia and violent extremism. Understanding how misperceptions towards immigration are formed and shaped is key to address combat mis-representations of immigrants. Typically attitudes towards immigration are studied based on qualitative and nationally representative surveys but they offer low population coverage, coarse geographical resolution and slow data collection. Social media offers dynamic and open space to better understand experiences and public opinion about immigration. While some bias exists, social media data are produced at unprecedented temporal frequency, geographical granularity and is accessible in real time. This paper aims to measure and better understand attitudes towards immigration in Chile using Twitter data. Key findings indicate that negative attitudes emerge from a reduced number of users, and are more commonly manifested and intensify during negative immigrant news reflecting arguments of job competition and stricter immigration regulation. Positive attitudes are expressed by a more diffused number of users and are predominantly express to manifest support during specific events reflecting supportive arguments for immigrants’ human and civil rights.
Immigration is a key ingredient for social cohesion and economic development. Yet, it is often portrayed as a major threat to national identity, values, economic stability and security, resulting in acts of intolerance, discrimination, racism, xenophobia and violent extremism. Understanding how misperceptions towards immigration are formed and shaped is key to address combat mis-representations of immigrants. Typically attitudes towards immigration are studied based on qualitative and nationally representative surveys but they offer low population coverage, coarse geographical resolution and slow data collection. Social media offers dynamic and open space to better understand experiences and public opinion about immigration. While some bias exists, social media data are produced at unprecedented temporal frequency, geographical granularity and is accessible in real time. This paper aims to measure and better understand attitudes towards immigration in Chile using Twitter data. Key findings indicate that negative attitudes emerge from a reduced number of users, and are more commonly manifested and intensify during negative immigrant news reflecting arguments of job competition and stricter immigration regulation. Positive attitudes are expressed by a more diffused number of users and are predominantly express to manifest support during specific events reflecting supportive arguments for immigrants’ human and civil rights.
In 2020, countries around the world started facing unprecedented challenges of understanding and tackling the spread and impacts of COVID-19. Amid vital efforts to understand the public health and economic of the pandemic, the rapid spread of discrimination and xenophobia against specific population groups, especially migrants and individuals of Asian descent, has largely been neglected. Understanding public attitudes towards migration is essential to counter discrimination against immigrants and promote social cohesion. Traditional data sources typically used to monitor public opinion— ethnographies, interviews, or surveys - usually rely on small samples, but they are slow to collect, take time to process and become available particularly in a pandemic setting. 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, and accessible in real-time. Drawing on Twitter data, this study seeks to measure shifts in public sentiment opinion about migration in the pre- and pandemic periods (December 2019-April 2020) in five countries (UK, US, Spain, Italy, Germany), drawing on a dataset of 34.92 million Tweets and natural language processing. Results show an increase of immigration-related Tweets along with rising numbers of COVID-19 cases during national lockdowns in all five observed countries; yet, we found no evidence of a significant increase in anti-immigration sentiment in our sample as rises in the volume of negative messages are offset by comparable increases in positive messages. Our results also indicate that the debate around migration is highly polarized and that a distinctive set of topics tends to dominate this debate in the five countries in our sample.
Immigration is a key ingredient for social cohesion and economic development. Yet, it is often portrayed as a major threat to national identity, values, economic stability and security, resulting in acts of intolerance, discrimination, racism, xenophobia and violent extremism. Understanding how misperceptions towards immigration are formed and shaped is key to address combat mis-representations of immigrants. Typically attitudes towards immigration are studied based on qualitative and nationally representative surveys but they offer low population coverage, coarse geographical resolution and slow data collection. Social media offers dynamic and open space to better understand experiences and public opinion about immigration. While some bias exists, social media data are produced at unprecedented temporal frequency, geographical granularity and is accessible in real time. This paper aims to measure and better understand attitudes towards immigration in Chile using Twitter data. Key findings indicate that negative attitudes emerge from a reduced number of users, and are more commonly manifested and intensify during negative immigrant news reflecting arguments of job competition and stricter immigration regulation. Positive attitudes are expressed by a more diffused number of users and are predominantly express to manifest support during specific events reflecting supportive arguments for immigrants’ human and civil rights.
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.
Administrative areas do not accurately reflect the contemporary spatial manifestation of labour market linkages. Local labour market areas (LLMAs) have been shown to provide a better representation of geographic labour market activity. Traditionally LLMAs are delineated based on commuting flow data. However, commuting data are expensive to collect, are sporadically collected in developed countries and rarely available in less developed countries. Yet, recent advances in computing capacity and increased availability of satellite imagery offers a unique opportunity to generate LLMAs in poor environment context in a cheap, frequent and automated way. This papers aims to develop an automated satellite-based approach to define LLMAs.
This presentation summarises collaborative work on internal migration in Latin American cities. It also proposes ways how twitter data and machine learning can be used to measure internal migration and an approach to measure the relative importance …