Changing neighborhoods, shifting connections: mapping relational geographies of gentrification using social media data

Poorthuis, Ate; Shelton, Taylor; Zook, Matthew

doi:10.1080/02723638.2021.1888016

Cited by 16 publications

(9 citation statements)

References 57 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We used 130-km wide hexagonal cells to map tweets at the global scale (shown in Figure 5) and 65-km wide for tweets at the national (US) scale (shown in Figure 6). The number of tweets within the hexagon units for both global and national maps were normalized by calculating the odds ratio and taking the lower bound of the 99.9% confidence for each unit, which is expressed as follows where p i is the number of tweets in hexagon i related to the #migrantcaravan, p is the sum of all tweets about the #migrantcaravan; r i is the number of random tweets in hexagon i and r is the sum of all random tweets (for description and application of this normalization methods, see Shelton et al., 2014; Poorthuis et al., 2016, 2020). We used a dataset consisting of 315,029 random geotagged tweets sent globally between 2012 and 2016 to normalize the tweets in the global map (Figure 5), and 820,587 random geotagged tweets sent from North America in 2014 to normalize the tweets in the US map (Figure 6).…”

Section: Methodsmentioning

confidence: 99%

The digital life of the #migrantcaravan: Contextualizing Twitter as a spatial technology

Walker

Boamah²

2020

Big Data & Society

View full text Add to dashboard Cite

The Central American migrant caravans of 2018 are best understood as having been precipitated by entangled multi-scalar geopolitical histories among the United States, Mexico, Guatemala, Honduras, and El Salvador. Unsurprisingly, the migrants traveling north to the United States garnered widespread attention on social media. So much so that the reaction to the caravan accelerated plans to deploy troops to the US southern border and deny Central Americans the opportunity to seek asylum. This example showcases how the digital world can have exponential material effects. While coverage on border security and migration has been extensive, within political geography, such concerns have rarely been paired with social media. In this article, we take as our object of analysis the digitality or “digital life” of the migrant caravan. Mapping the patterns of migrant caravan-related tweeting paired with the exploration of Twitter’s networked dimensions reveals the platform to be a fundamentally spatial technology. Rather than reflect, refract or distort, Twitter produces and (its power) is in turn produced through spatial mechanisms. We present multiple cartographic visualizations in support of this claim and highlight the ways in which a contextual knowledge of the subject under study—the migrant caravan—can further inform analyses of Big Data.

show abstract

Section: Methodsmentioning

confidence: 99%

The digital life of the #migrantcaravan: Contextualizing Twitter as a spatial technology

Walker

Boamah²

2020

Big Data & Society

View full text Add to dashboard Cite

show abstract

“…Sentiment analysis also requires specific caution when interpreting results (Plunz et al, 2019; Roberts et al, 2019) since data often reflect self‐selection biases. However, as Poorthuis et al (2022) note, such limits should not invalidate use. Rather, researchers may draw on “qualitative and experiential knowledge to contextualize and enrich what can be inferred” (Poorthuis et al, 2022, p. 978).…”

Section: Understanding Concern For Urban Treesmentioning

confidence: 99%

“…However, as Poorthuis et al (2022) note, such limits should not invalidate use. Rather, researchers may draw on “qualitative and experiential knowledge to contextualize and enrich what can be inferred” (Poorthuis et al, 2022, p. 978). We keep the need for such care in mind, employing an approach that works with the strengths of the emails and combines qualitative coding, statistical and sentiment testing, and spatial analysis.…”

Section: Understanding Concern For Urban Treesmentioning

confidence: 99%

Emotional geographies of an urban forest: Insights from an email‐a‐tree initiative

Atchison,

Brennan‐Horley,

Phillips

et al. 2023

Geographical Research

View full text Add to dashboard Cite

International and national policies are being used to prioritise increases in urban forest coverage and diversity, support equitable access to urban greenspaces, and advance sound environmental governance outcomes. Yet, the relationship between people’s feelings about urban trees and public policy remains under‐examined. Drawing on a unique dataset from an email‐a‐tree initiative, the objectives of this study were to identify how concern and connection for urban trees is expressed and determine how insights from the initiative might inform urban forest governance. We examined emails sent to trees using a mixed‐methods approach that included qualitative coding, VADER sentiment analysis, and statistical and spatial analyses. We identified and considered three themes based on emails sent by members of the public and municipal tree inventory data. Those themes were location, age and loss, and type. In accord with other studies, overall sentiment for urban trees was positive and underscored people’s strong connections with trees, often based on routine and repeated engagements. However, people noticed and were concerned about a limited range of trees and tree types in urban settings. Based in Melbourne, Australia, our case study shows how examining feelings for trees helps residents and researchers understand urban tree relationships and gauge how those designing public engagement programs might learn from such an initiative to create meaningful opportunities for active participation in governance.

show abstract

“…Such data have gained significant popularity as a way to investigate the spatial, social, and temporal characteristics of urban spaces (Li et al, 2019; Martí et al, 2019; Shaw and Sui, 2020). In addition, researchers have used these data to infer socio-geographical relations between people and places and use these relations to shine light on a number of urban issues such as individual activity spaces (Ayala-Azcárraga et al, 2019; Jurdak et al, 2015), social interaction (Prasetyo et al, 2016), gentrification (Poorthuis et al, 2021), and social inequality (Pendall and Hedman, 2016; Shelton et al, 2015).…”

Section: Related Workmentioning

confidence: 99%

Categorizing urban space based on visitor density and diversity: A view through social media data

Chuang

Chen

Poorthuis

2022

Environment and Planning B: Urban Analytics and City Science

Self Cite

View full text Add to dashboard Cite

Analyses of urban spaces have often stressed the importance of both the density and diversity of the people they attract. However, the diversity of people is a challenging concept to operationalize within the context of urban spaces, which is why many evaluations of urban space have relied primarily on density-based measures. We argue that a focus on only one of the two aspects misses important aspects of the variety of urban spaces in our cities. To address this, we design a methodology that evaluates both the density and diversity of human behavior in urban spaces based on geosocial media data. We operationalize density as the frequency of tweets from visitors to a particular location and diversity as the variety of the home neighborhoods of those visitors. Taking Singapore as a test case, we identify networks between the home neighborhoods of 28k Twitter users based on 2.2 million geolocated tweets collected between 2012 and 2016. Based on these data, we categorize the urban landscape of Singapore into four “performance” categories, namely High-Density/High-Diversity, High-Density/Low-Diversity, Low-Density/High-Diversity, and Low-Density/Low-Diversity. Our findings illustrate that this combined indicator provides useful nuance compared to differentiation between well and less performing spaces based on density alone. By enabling a categorization of urban spaces that fits closer to the diversity of human behavior in these spaces, human mobility data sets, such as the social media data we use, open the door to a practical evaluation of the design and planning of our heterogeneous urban environment.

show abstract

Changing neighborhoods, shifting connections: mapping relational geographies of gentrification using social media data

Cited by 16 publications

References 57 publications

The digital life of the #migrantcaravan: Contextualizing Twitter as a spatial technology

The digital life of the #migrantcaravan: Contextualizing Twitter as a spatial technology

Emotional geographies of an urban forest: Insights from an email‐a‐tree initiative

Categorizing urban space based on visitor density and diversity: A view through social media data

Contact Info

Product

Resources

About