scholarly journals GIScience and neighborhood change: Toward an understanding of processes of change

2021 ◽  
Author(s):  
Elizabeth C. Delmelle
Keyword(s):  
Neighborhood Change ◽  
Processes Of Change

scholarly journals GIScience and Neighborhood Change: Toward and Understanding of Processes of Change

2021 ◽  
Author(s):  
Elizabeth C. Delmelle
Keyword(s):  
Urban Landscape ◽  
Neighborhood Change ◽  
Future Research ◽  
Processes Of Change ◽  
Temporal Scales ◽  
Full Spectrum ◽  
Cross Sectional ◽  
Spatial And Temporal Scales ◽  
Future Research Agenda ◽  
Simple Mapping

Processes of neighborhood change are the result of the unfolding of events and decisions by multiple actors operating at varying spatial and temporal scales, enabled and constrained upon an unequal urban landscape. The contributions of GIScience towards understanding these processes has evolved from the simple mapping of static, cross-sectional maps towards an embrace of novel data and methods that enable longitudinal trajectories to be extracted and neighborhood futures to be predicted. In this article, I review these advancements and chart a course forward that considers a future research agenda that is critically cognizant of the potentials and perils of new data sources and method, is representative of the full spectrum of processes operating both visibly and invisibly that give rise to observed neighborhood outcomes, and considers their varying spatial and temporal scales.

Neighborhood change: intra-urban migration and changing neighborhood use

1985 ◽  
Vol 3 (1) ◽  
pp. 189-200 ◽  
Author(s):  
P. C. J. Everaers ◽  
M. W. A. Maas
Keyword(s):  
Neighborhood Change ◽  
Urban Migration

scholarly journals Monitoring streets through tweets: Using user-generated geographic information to predict gentrification and displacement

2021 ◽  
pp. 239980832110253
Author(s):  
Karen Chapple ◽  
Ate Poorthuis ◽  
Matthew Zook ◽  
Eva Phillips
Keyword(s):  
Big Data ◽  
San Francisco ◽  
Census Data ◽  
San Francisco Bay Area ◽  
Demographic Data ◽  
Activity Patterns ◽  
Neighborhood Change ◽  
Housing Affordability ◽  
Bay Area ◽  
Media Data

The new availability of big data sources provides an opportunity to revisit our ability to predict neighborhood change. This article explores how data on urban activity patterns, specifically, geotagged tweets, improve the understanding of one type of neighborhood change—gentrification—by identifying dynamic connections between neighborhoods and across scales. We first develop a typology of neighborhood change and risk of gentrification from 1990 to 2015 for the San Francisco Bay Area based on conventional demographic data from the Census. Then, we use multivariate regression to analyze geotagged tweets from 2012 to 2015, finding that outsiders are significantly more likely to visit neighborhoods currently undergoing gentrification. Using the factors that best predict gentrification, we identify a subset of neighborhoods that Twitter-based activity suggests are at risk for gentrification over the short term—but are not identified by analysis with traditional census data. The findings suggest that combining Census and social media data can provide new insights on gentrification such as augmenting our ability to identify that processes of change are underway. This blended approach, using Census and big data, can help policymakers implement and target policies that preserve housing affordability and protext tenants more effectively.

“Remember, this is Brightmoor”: Historical Violence, Neighborhood Experiences, and the Hysteresis of Street Life

2021 ◽  
pp. 107808742110169
Author(s):  
Sharon Cornelissen
Keyword(s):  
Middle Class ◽  
Public Life ◽  
Drug Market ◽  
Neighborhood Change ◽  
Neighborhood Violence ◽  
Street Life ◽  
Farmers Market ◽  
Life Styles ◽  
Black Poor

Drawing on three years of fieldwork, this article explains the emergence and persistence of two conflicting styles of street life in Brightmoor, a depopulated, majority Black, poor Detroit neighborhood facing early gentrification. As most longtimers were inured to historical neighborhood violence, they tended to act vigilantly in public, even after recent crime declines. By contrast, White newcomers, most of whom had moved from middle-class neighborhoods, often defied vigilance such as by organizing a farmers' market across from an open-air drug market. They mobilized aspirational public life as a means for changing the neighborhood and end in itself. To explain these conflicting styles, this article theorizes the cultural mechanism of “ the hysteresis of street life.” Styles of street life, shaped by residents' unequal historical neighborhood experiences, tend to linger under conditions of gradual neighborhood change. It also shows how the hysteresis of street life may contribute to the reproduction of inequalities.

Vegetation ecology with anthropic drivers and consequences

2021 ◽  
pp. 030913332199937
Author(s):  
George P Malanson ◽  
Michelle L Talal ◽  
Elizabeth R Pansing ◽  
Scott B Franklin
Keyword(s):  
Climate Change ◽  
Invasive Species ◽  
Plant Communities ◽  
Fire Ecology ◽  
Progress Report ◽  
Processes Of Change ◽  
Community Classification ◽  
The Status ◽  
Plant Community Classification ◽  
Anthropogenic Drivers

Current research on vegetation makes a difference in people’s lives. Plant community classification is a backbone of land management, plant communities are changing in response to anthropogenic drivers, and the processes of change have impacts on ecosystem services. In the following progress report, we summarize the status of classification and recent research on vegetation responses to pollution, especially nitrogen deposition, invasive species, climate change, and land use and direct exploitation. Two areas with human feedbacks are underscored: fire ecology and urban ecology. Prominent questions at the current research frontier are highlighted with attention to new perspectives.

Prediction of Outcome for Veterans with Post-Traumatic Stress Disorder Using Constructs from the Transtheoretical Model of Behaviour Change

2007 ◽  
Vol 41 (7) ◽  
pp. 590-597 ◽  
Author(s):  
Kathleen Rooney ◽  
Caroline Hunt ◽  
Leanne Humphreys ◽  
David Harding ◽  
Miriam Mullen ◽  
...  
Keyword(s):  
Transtheoretical Model ◽  
Post Traumatic Stress Disorder ◽  
Traumatic Stress ◽  
Symptom Severity ◽  
Stress Disorder ◽  
Readiness To Change ◽  
Processes Of Change ◽  
Post Traumatic Stress ◽  
Predictive Utility ◽  
Post Traumatic

Objective: Post-traumatic stress disorder (PTSD) is a disabling condition, sometimes unresponsive to treatment. The aim of the present study was to examine the predictive utility of constructs from the transtheoretical model of behaviour change (TTM) known to predict outcome for other disorders. Method: A sample of 50 veterans presenting for a PTSD treatment programme provided data for this longitudinal study. Variables were assessed at four time-points during the treatment programme. Multiple regression and mixed-effects regression were utilized to determine the predictive utility of variables from the TTM. Results: Allocated stage of change at the time of a 2 day introduction programme predicted follow-up symptom severity, but changes therein during treatment did not predict changes in symptom severity. However, changes in the continuous readiness-to-change variable and behavioural processes of change were predictive of such changes. Conclusions: Despite some difficulties in the application of the TTM to PTSD, the model does appear to predict treatment outcome. Veterans who have increased readiness to change and who make more use of behavioural processes of change are likely to have improved outcomes.

scholarly journals The Transtheoretical Model in Weight Management: Validation of the Processes of Change Questionnaire

Obesity Facts ◽  
2011 ◽  
Vol 4 (6) ◽  
pp. 433-442 ◽  
Author(s):  
Ana Andrés ◽  
Carmina Saldaña ◽  
Juana Gómez-Benito
Keyword(s):  
Weight Management ◽  
Transtheoretical Model ◽  
Processes Of Change
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