Analyzing Refugee Migration Patterns Using Geo-tagged Tweets

Hübl, Franziska; Cvetojevic, Sreten; Hochmair, Hartwig H.; Paulus, Gernot

doi:10.3390/ijgi6100302

Cited by 34 publications

(27 citation statements)

References 47 publications

(58 reference statements)

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“…This is the case of [28] in which the authors employ georeferenced data for about 62,000 individuals to estimate a set of US internal migration flows. Again, in [29], researchers analyze the behavior of migrants from the Middle East and Northern Africa to Europe by considering their collective mobility and its evolution in time. They highlight that one of the main problems in their study is the scarcity of geo-tagged tweets, resulting in difficulties in unfolding refugees' spatiotemporal patterns.…”

Section: Related Workmentioning

confidence: 99%

Assessing Refugees’ Integration via Spatio-Temporal Similarities of Mobility and Calling Behaviors

Alfeo

Cimino

Lepri

et al. 2019

IEEE Trans. Comput. Soc. Syst.

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In Turkey the increasing tension, due to the presence of 3.4 million Syrian refugees, demands the formulation of effective integration policies. Moreover, their design requires tools aimed at understanding the integration of refugees despite the complexity of this phenomenon.In this work, we propose a set of metrics aimed at providing insights and assessing the integration of Syrians refugees, by analyzing a real-world Call Details Records (CDRs) dataset including calls from refugees and locals in Turkey throughout 2017. Specifically, we exploit the similarity between refugees' and locals' spatial and temporal behaviors, in terms of communication and mobility in order to assess integration dynamics.Together with the already known methods for data analysis, we use a novel computational approach to analyze spatiotemporal patterns: Computational Stigmergy, a bio-inspired scalar and temporal aggregation of samples. Computational Stigmergy associates each sample to a virtual pheromone deposit (mark). Marks in spatiotemporal proximity are aggregated into functional structures called trails, which summarize the spatiotemporal patterns in data and allows computing the similarity between different patterns.According to our results, collective mobility and behavioral similarity with locals have great potential as measures of integration, since they are: (i) correlated with the amount of interaction with locals; (ii) an effective proxy for refugee's economic capacity, thus refugee's potential employment; and (iii) able to capture events that may disrupt the integration phenomena, such as social tensions.

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Section: Related Workmentioning

confidence: 99%

Assessing Refugees’ Integration via Spatio-Temporal Similarities of Mobility and Calling Behaviors

Alfeo

Cimino

Lepri

et al. 2019

IEEE Trans. Comput. Soc. Syst.

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“…Results were generally verified with travel surveys or census data [9,32], and it was concluded that working and commercially related tweets or topics gave a better estimate. For long-term and even larger spatial scale movement patterns, analysis around migration was explored, such as that in [33,34]. Although significantly limited by the sample size of valid tweets, long-term historical tweets data is still useful for exploratory analysis and inferring trends.…”

Section: Related Work-mining Spatial Patterns From Geo-tweetsmentioning

confidence: 99%

Profiling the Spatial Structure of London: From Individual Tweets to Aggregated Functional Zones

Zhong

Zeng

et al. 2018

IJGI

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Knowledge discovery about people and cities from emerging location data has been an active research field but is still relatively unexplored. In recent years, a considerable amount of work has been developed around the use of social media data, most of which focusses on mining the content, with comparatively less attention given to the location information. Furthermore, what aggregated scale spatial patterns show still needs extensive discussion. This paper proposes a tweet-topic-function-structure framework to reveal spatial patterns from individual tweets at aggregated spatial levels, combining an unsupervised learning algorithm with spatial measures. Two-year geo-tweets collected in Greater London were analyzed as a demonstrator of the framework and as a case study. The results indicate, at a disaggregated level, that the distribution of topics possess a fair degree of spatial randomness related to tweeting behavior. When aggregating tweets by zones, the areas with the same topics form spatial clusters but of entangled urban functions. Furthermore, hierarchical clustering generates a clear spatial structure with orders of centers. Our work demonstrates that although uncertainties exist, geo-tweets should still be a useful resource for informing spatial planning, especially for the strategic planning of economic clusters.

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“…This condition is marked by the Makassar City development center zone as the core city connected with Maros City, Sungguminasa City and Takalar City which continue to grow and their influence on the surrounding urban suburbs. The structure of urban agglomeration space has been considered important factors affecting regional functions and sustainable development [29].…”

Section: Introductionmentioning

confidence: 99%

Land Use Change, Spatial Interaction, and Sustainable Development in the Metropolitan Urban Areas, South Sulawesi Province, Indonesia

Surya

Ahmad

Sakti

et al. 2020

Land

100

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Metropolitan Urban Mamminasata South Sulawesi, Indonesia as the object of study is explored in the core-peripheral spatial interaction towards the formation of suburban service centers. The problems raised in this study are (i) is there a relationship/correlation between spatial integration, spatial interaction, and urban agglomeration to the formation of urban activity systems and (ii) how the formation of urban activity systems works as a determinant of economic growth, land use change and environmental quality degradation towards sustainable development in the metropolitan city of Mamminasata. Comparative studies of suburban areas have been carried out over three time periods (2001, 2015 and 2019). Data elaboration on observations, surveys and documentation is done to describe urban dynamics in terms of economic, social and environmental aspects. Path analysis is used to address direct effects, indirect effects, differences, and dependencies between urban elements. The gravity model is used to analyze the spatial interactions of the core city with the periphery. The study results show that spatial integration, spatial interaction and urban agglomeration have a positive effect on the system of urban activity and economic growth in the outskirts of the Mamminasata Metropolitan area. The results of this study recommend policy makers and urban planners that land use change, spatial integration and urban spatial interactions on the spatial scale of metropolitan cities to require the implementation of sustainable development concepts oriented towards saving the environment, ensuring fairness in economic access and creating social cohesion, in line with meeting national Metropolitan city development targets by 2030.

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Analyzing Refugee Migration Patterns Using Geo-tagged Tweets

Cited by 34 publications

References 47 publications

Assessing Refugees’ Integration via Spatio-Temporal Similarities of Mobility and Calling Behaviors

Assessing Refugees’ Integration via Spatio-Temporal Similarities of Mobility and Calling Behaviors

Profiling the Spatial Structure of London: From Individual Tweets to Aggregated Functional Zones

Land Use Change, Spatial Interaction, and Sustainable Development in the Metropolitan Urban Areas, South Sulawesi Province, Indonesia

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