No full-text available
To read the full-text of this research,
you can request a copy directly from the authors.
The Use of Big Mobile Data to Gain Multilayered Insights for Syrian Refugee Crisis
Abstract
This study aims to shed light on various aspects of refugees’ lives in Turkey using mobile call data records of Türk Telekom, enriched with numerous local data sets. To achieve this, we made use of several statistical and data mining techniques in addition to a novel methodology to find home and work-time anchors of mobile phone users we developed. Our results showed that refugees are highly mobile as a survival strategy—a significant number of whom work as seasonal workers. Most prefer to live in relatively low-status neighborhoods, close to city transport links and fellow refugees. The ones living in these neighborhoods appear to be introverts, living in a closed neighborhood. However, the middle- and upper-class refugees appear to be the opposite. Fatih, İstanbul was found as an important hub for refugees. Finally, the officially registered refugee numbers do not reflect the real refugee population in Turkey. Due to their high mobility, refugees lag behind in keeping up-to-date information about their residential address, resulting in a significant discrepancy between the official numbers and the real numbers. We think that policymakers can benefit from the proposed methods in this study to develop real-time solutions for the well-being of refugees.
... According to official figures, Turkey is home to 3.6 million Syrian refugees living mostly in big cities with many unsettling impacts not just for Istanbul but for many other cities. Of these refugees, more than 15% (560,000) live in Istanbul, usually in areas where rents and living costs are low and accessibility is high (Kılıç et al. 2019). ...
Contrary to trends in many European countries, income inequality in Turkey, measured by the Gini coefficient, has declined between 1994 and 2014, with a small but consistent increase since then. Turkish income inequality is among the highest in OECD countries, with levels not lower than 0.4. This chapter will examine residential socio-economic segregation in Istanbul against the backdrop of this relatively stable and high-income inequality. The chapter shows signs that residential segregation is on the rise. Istanbul has undergone a radical change in the 2000s thanks to active intervention by the state in the real estate market by opening up large pieces of land in the outskirts and gentrifying inner-city areas once occupied by unauthorized settlements that once were home to the poor. Dynamics of urban development, fueled by rapid urban sprawl in peri-urban areas and ceaseless gentrification of inner-city areas, gave way to diverse patterns of segregation depending on the already existing divisions and physical geography of cities. Given the lack of neighbourhood level data on either occupations or income, this chapter analyses segregation through indices based on fertility and educational level, which we know from detailed household microdata are closely correlated with income. On the basis of 2000 and 2017 neighbourhood data, we show that in Istanbul, there is a clearly visible pattern where the poor are progressively pushed further to the city limits, while some parts of built-up areas once home to middle classes, were recaptured by the poor. The result in some parts of the city is a juxtaposition of seemingly conflicting patterns: parts of the inner city were reclaimed by the poor while some parts were gentrified led by the nascent urban elite. The urban periphery was partly occupied by the bourgeoning middle classes and was also home to the urban poor who were displaced by urban transformation projects.
... Roaming data enables analysis of cross-border mobility with a more precise temporal resolution and the ability to distinguish visitor groups (including one-day visitors, commuters, etc.), including those not captured in official tourism statistics (Saluveer et al., 2020). Outbound roaming data has been applied to study cross-border mobility, for example in Estonia (Masso et al., 2019; or in the mobility of Syrian refugees (Kılıç et al., 2019). ...
In addition to permanent migration, different forms of cross-border mobility were on the rise before the COVID-19 pandemic, ranging from tourism to job-related commuting. In this paper ethno-linguistic differences in cross-border mobility using the activity space framework are considered. New segregation theories emphasise that segregation in one part of the activity space (e.g. in residential neighbourhood) affects the segregation in other parts of the activity space (e.g. in workplace), and that spatial mobility between activity locations is equally important in the production and reproduction of ethnic inequalities. Until now, segregation in activity spaces has been studied by focusing on daily activities inside one country. In reality, an increasing number of people pursue their activities across different countries, so that their activity spaces extend beyond state borders, which can have important implications for the functioning of ethno-linguistic communities and the transfer of inequalities from one country to another. This study takes advantage of mobility data based on mobile phone use, and the new avenues provided for the study of ethno-linguistic differences in temporary cross-border mobility. Such data allow the study of different cross-border visitor groups—tourists, commuters, transnationals, long-term stayers—by providing the means to measure the frequency of visits and time spent abroad, and to link together the travel of each person over several years. Results show that members of the ethno-linguistic minority population in Estonia make more trips than members of the ethno-linguistic majority, and they also have higher probability of being tourists and cross-border commuters than the majority population, paying frequent visits to their ancestral homelands. The connections between ethno-linguistic background and temporary cross-border mobility outlined in this study allows for future discussion on how (in)equalities can emerge in transnational activity space and what implications it has for segregation.
This paper investigates the effects of refugee inflow on the voting behavior of natives. I employ a difference in differences strategy to identify the voter reaction against the sudden inflow of Syrian refugees to Turkey and use unique data provided by Turk Telecom to proxy natives’ refugee exposure. Data coming from mobile phone usage track the mobility of refugees throughout the year and enables the construction of refugee exposure measures across small geographical areas. I show a small drop in the government party vote share that announced an ‘open door’ policy for refugees. Furthermore, there is evidence for the reaction heterogeneity based on the socio-economic development levels of refugee-hosting areas. While there is a null response in ethnically Kurdish and economically lagging East, a negative reaction is concentrated in socioeconomically more developed areas. Then, exploring the effects in rural and urban areas reveals an interesting divide in the refugee exposure patterns of hosting areas. While in rural areas, natives react against the refugee presence, in urban areas, they react when the mobility (visibility) increases. Anecdotal evidence might help to put findings in context. Manipulating the concerns of voters on Syrians’ receiving citizenship and demographic change might be regarded as two main strategies used by opposition parties. These strategies help convince the secular and conservative-nationalist voters in socioeconomically developed areas. (JEL codes: J01, J08, and J15)
Urban landscapes present a variety of socio-topological environments that are associated to diverse human activities. As the latter affect the way individuals connect with each other, a bound exists between the urban tissue and the mobile communication demand. In this paper, we investigate the heterogeneous patterns emerging in the mobile communication activity recorded within metropolitan regions. To that end, we introduce an original technique to identify classes of mobile traffic signatures that are distinctive of different urban fabrics. Our proposed technique outperforms previous approaches when confronted to ground-truth information, and allows characterizing the mobile demand in greater detail than that attained in the literature to date. We apply our technique to extensive real-world data collected by major mobile operators in ten cities. Results unveil the diversity of baseline communication activities across countries, but also evidence the existence of a number of mobile traffic signatures that are common to all studied areas and specific to particular land uses.
In this study, with Singapore as an example, we demonstrate how we can use mobile phone call detail record (CDR) data, which contains millions of anonymous users, to extract individual mobility networks comparable to the activity-based approach. Such an approach is widely used in the transportation planning practice to develop urban micro simulations of individual daily activities and travel; yet it depends highly on detailed travel survey data to capture individual activity-based behavior. We provide an innovative data mining framework that synthesizes the state-of-the-art techniques in extracting mobility patterns from raw mobile phone CDR data, and design a pipeline that can translate the massive and passive mobile phone records to meaningful spatial human mobility patterns readily interpretable for urban and transportation planning purposes. With growing ubiquitous mobile sensing, and shrinking labor and fiscal resources in the public sector globally, the method presented in this research can be used as a low-cost alternative for transportation and planning agencies to understand the human activity patterns in cities, and provide targeted plans for future sustainable development.
Witten and Tibshirani (2010) proposed an algorithim to simultaneously find clusters and select clustering variables, called sparse K-means (SK-means). SK-means is particularly useful when the dataset has a large fraction of noise variables (that is, variables without useful information to separate the clusters). SK-means works very well on clean and complete data but cannot handle outliers nor missing data. To remedy these problems we introduce a new robust and sparse K-means clustering algorithm implemented in the R package RSKC. We demonstrate the use of our package on four datasets. We also conduct a Monte Carlo study to compare the performances of RSK-means and SK-means regarding the selection of important variables and identification of clusters. Our simulation study shows that RSK-means performs well on clean data and better than SK-means and other competitors on outlier-contaminated data.
Mobile data has allowed us to sense urban dynamics at scales and granularities not known before, helping urban planners to cope with urban growth. A frequently used kind of dataset are Call Detail Records (CDR), used by telecommunication operators for billing purposes. Being an already extracted and processed dataset, it is inexpensive and reliable. A common assumption with respect to geography when working with CDR data is that the position of a device is the same as the Base Transceiver Station (BTS) it is connected to. Because the city is divided into a square grid, or by coverage zones approximated by Voronoi tessellations, CDR network events are assigned to corresponding areas according to BTS position. This geolocation may suffer from non negligible error in almost all cases. In this paper we propose "Antenna Virtual Placement" (AVP), a method to geolocate mobile devices according to their connections to BTS, based on decoupling antennas from its corresponding BTS according to its physical configuration (height, downtilt, and azimuth). We use AVP applied to CDR data as input for two different tasks: first, from an individual perspective, what places are meaningful for them? And second, from a global perspective, how to cluster city areas to understand land use using floating population flows? For both tasks we propose methods that complement or improve prior work in the literature. Our proposed methods are simple, yet not trivial, and work with daily CDR data from the biggest telecommunication operator in Chile. We evaluate them in Santiago, the capital of Chile, with data from working days from June 2015. We find that: (1) AVP improves city coverage of CDR data by geolocating devices to more city areas than using standard methods; (2) we find important places (home and work) for a 10% of the sample using just daily information, and recreate the population distribution as well as commuting trips; (3) the daily rhythms of floating population allow to cluster areas of the city, and explain them from a land use perspective by finding signature points of interest from crowdsourced geographical information. These results have implications for the design of applications based on CDR data like recommendation of places and routes, retail store placement, and estimation of transport effects from pollution alerts.
We exploit the regional variation in the unexpected (or forced) inflow of Syrian refugees as a natural experiment to estimate the impact of immigration on consumer prices in Turkey. Using a difference-in-differences strategy and a comprehensive data set on the regional prices of CPI items, we find that general level of consumer prices has declined by approximately 2.5 percent due to immigration. Prices of goods and services have declined in similar magnitudes. We highlight that the channel through which the price declines take place is the informal labor market. Syrian refugees supply inexpensive informal labor and, thus, substitute the informal native workers especially in informal labor intensive sectors. We document that prices in these sectors have fallen by around 4 percent, while the prices in the formal labor intensive sectors have almost remained unchanged. Increase in the supply of informal immigrant workers generates labor cost advantages and keeps prices lower in the informal labor intensive sectors.
The article introduces a model for the location of meaningful places for mobile telephone users, such as home and work anchor points, using passive mobile positioning data. Passive mobile positioning data is secondary data concerning the location of call activities or handovers in network cells that is automatically stored in the memory of service providers. This data source offers good potential for the monitoring of the geography and mobility of the population, since mobile phones are widespread, and similar standardized data can be used around the globe. We developed the model and tested it with 12 months' data collected by EMT, Estonia's largest mobile service provider, covering more than 0.5 million anonymous respondents. Modeling results were compared with population register data; this revealed that the developed model described the geography of the population relatively well, and can hence be used in geographical and urban studies. This approach also has potential for the development of location-based services such as targeting services or geographical infrastructure.
Enschede, the Netherlands johan.koolwaaij@telin.nl Abstract— Existing context-aware mobile applications often rely on location information. However, raw location data such as GPS coordinates or GSM cell identifiers are usually meaningless to the user and, as a consequence, researchers have proposed different methods for inferring so-called places from raw data. The places are locations that carry some meaning to user and to which the user can potentially attach some (meaningful) semantics. Examples of places include home, work and airport. A lack in existing work is that the labeling has been done in an ad hoc fashion and no motivation has been given for why places would be interesting to the user. As our first contribution we use social identity theory to motivate why some locations really are significant to the user. We also discuss what potential uses for location information social identity theory implies. Another flaw in the existing work is that most of the proposed methods are not suited to realistic mobile settings as they rely on the availability of GPS information. As our second contribution we consider a more realistic setting where the information consists of GSM cell transitions that are enriched with GPS information whenever a GPS device is available. We present four different algorithms for this problem and compare them using real data gathered throughout Europe. In addition, we analyze the suitability of our algorithms for mobile devices.
The socioeconomic status of a population or an individual provides an understanding of its access to housing, education, health
or basic services like water and electricity. In itself, it is also an indirect indicator of the purchasing power and as such
a key element when personalizing the interaction with a customer, especially for marketing campaigns or offers of new products.
In this paper we study if the information derived from the aggregated use of cell phone records can be used to identify the
socioeconomic levels of a population. We present predictive models constructed with SVMs and Random Forests that use the aggregated
behavioral variables of the communication antennas to predict socioeconomic levels. Our results show correct prediction rates
of over 80% for an urban population of around 500,000 citizens.
People spend most of their time at a few key locations, such as home and work. Being able to identify how the movements of
people cluster around these “important places” is crucial for a range of technology and policy decisions in areas such as
telecommunications and transportation infrastructure deployment. In this paper, we propose new techniques based on clustering
and regression for analyzing anonymized cellular network data to identify generally important locations, and to discern semantically
meaningful locations such as home and work. Starting with temporally sparse and spatially coarse location information, we
propose a new algorithm to identify important locations. We test this algorithm on arbitrary cellphone users, including those
with low call rates, and find that we are within 3 miles of ground truth for 88% of volunteer users. Further, after locating
home and work, we achieve commute distance estimates that are within 1 mile of equivalent estimates derived from government
census data. Finally, we perform carbon footprint analyses on hundreds of thousands of anonymous users as an example of how
our data and algorithms can form an accurate and efficient underpinning for policy and infrastructure studies.
Big Data for Good: Unlocking Privately-Held Data to the Benefit of the Many - Volume 9 Issue 2 - Alberto ALEMANNO
The research question to be answered in this paper is to what extent Istanbul provides Syrian refugees with a feeling of security and safety despite the practical difficulties of everyday life such as working conditions, exclusion, xenophobia and exploitation. The main premise of the paper is that historical, cultural and religious forms of affinity are likely to particularly attach the Sunni Muslim Arab Syrians originating from Aleppo province to Istanbul. This paper is expected to contribute to the discipline of refugee studies by shedding light on the historical elements and agency that are often neglected in such analyses. Based on the findings of a qualitative and quantitative study conducted by the Support to Life Association among Syrian refugees in Istanbul in the last quarter of 2015 and the first quarter of 2016, this article aims to delineate the strong attachment of the Syrian refugees to the city of Istanbul.
This paper examines how innovations involving big data are helping to solve some of the greatest challenges facing the world today. Focusing primarily on the developing world, this paper explores how the large volumes of digital information, increasingly available in these contexts, can help decision-makers better address problems as big as poverty, illness, conflict, migration, corruption, natural disasters, climate change, and pollution, among other areas. This paper argues that the information vacuum that still exists in many developing countries makes the potential for impact from big data much greater in these contexts. Through a series of case studies, the authors demonstrate how big data can be used to address pressing social and environmental challenges in developing countries. The authors present research questions that could not have been addressed in the absence of dramatic recent increases in data volume, variety, and velocity. They then extrapolate from these questions, and discuss the nature of the technological changes that now allow decision-makers in developing countries to leapfrog from data poverty to big data, and permit innovative solutions to the aforementioned challenges. The authors emphasize the importance of looking beyond the current focus, in the literature, on storing, analyzing, and creating commercial value from big data. Instead, they point to the importance of innovativeness in identifying, integrating, disseminating, and applying new sources of data to execute actions that in turn generate product, service, process, and business model innovations that are impactful due to big data. The authors argue that academic researchers have an important role to play in helping the world harness the potential for big data innovations, through validation, visualization, and verification of such data.
The United States prides itself on being a nation of immigrants, and the country has a long history of successfully absorbing people from across the globe. The integration of immigrants and their children contributes to our economic vitality and our vibrant and ever changing culture. We have offered opportunities to immigrants and their children to better themselves and to be fully incorporated into our society and in exchange immigrants have become Americans - embracing an American identity and citizenship, protecting our country through service in our military, fostering technological innovation, harvesting its crops, and enriching everything from the nation's cuisine to its universities, music, and art. Today, the 41 million immigrants in the United States represent 13.1 percent of the U.S. population. The U.S.-born children of immigrants, the second generation, represent another 37.1 million people, or 12 percent of the population. Thus, together the first and second generations account for one out of four members of the U.S. population. Whether they are successfully integrating is therefore a pressing and important question. Are new immigrants and their children being well integrated into American society, within and across generations? Do current policies and practices facilitate their integration? How is American society being transformed by the millions of immigrants who have arrived in recent decades? To answer these questions, this new report from the National Academies of Sciences, Engineering, and Medicine summarizes what we know about how immigrants and their descendants are integrating into American society in a range of areas such as education, occupations, health, and language. © 2015 by the National Academy of Sciences. All rights reserved.
In recent years, call detail records (CDRs) have been widely used in human mobility research. Although CDRs are originally collected for billing purposes, the vast amount of digital footprints generated by calling and texting activities provide useful insights into population movement. However, can we fully trust CDRs given the uneven distribution of people’s phone communication activities in space and time? In this article, we investigate this issue using a mobile phone location dataset collected from over one million subscribers in Shanghai, China. It includes CDRs (~27%) plus other cellphone-related logs (e.g., tower pings, cellular handovers) generated in a workday. We extract all CDRs into a separate dataset in order to compare human mobility patterns derived from CDRs vs. from the complete dataset. From an individual perspective, the effectiveness of CDRs in estimating three frequently used mobility indicators is evaluated. We find that CDRs tend to underestimate the total travel distance and the movement entropy, while they can provide a good estimate to the radius of gyration. In addition, we observe that the level of deviation is related to the ratio of CDRs in an individual’s trajectory. From a collective perspective, we compare the outcomes of these two datasets in terms of the distance decay effect and urban community detection. The major differences are closely related to the habit of mobile phone usage in space and time. We believe that the event-triggered nature of CDRs does introduce a certain degree of bias in human mobility research and we suggest that researchers use caution to interpret results derived from CDR data.
An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article.
We consider the problem of clustering observations using a potentially large set of features. One might expect that the true underlying clusters present in the data differ only with respect to a small fraction of the features, and will be missed if one clusters the observations using the full set of features. We propose a novel framework for sparse clustering, in which one clusters the observations using an adaptively chosen subset of the features. The method uses a lasso-type penalty to select the features. We use this framework to develop simple methods for sparse K-means and sparse hierarchical clustering. A single criterion governs both the selection of the features and the resulting clusters. These approaches are demonstrated on simulated data and on genomic data sets.
Online real estate ads
- Hürriyet Emlak
Hürriyet Emlak: Online real estate ads. https://www.hurriyetemlak.com (2018). Accessed 8
August 2018
Migration, settlement and daily life patterns of Syrian urban refugees through time geography: a case of Önder neighborhood
- G Eraydın
Eraydın, G.: Migration, settlement and daily life patterns of Syrian urban refugees through
time geography: A case of Önder neighborhood. Unpublished PhD Thesis, Middle East
Technical University (2017)
Turkey badly needs a long-term plan for Syrian refugees
- G A Asik
Asik, G.A.: Turkey badly needs a long-term plan for Syrian refugees. Harvard Business
Review. https://hbr.org/2017/04/turkey-badly-needs-a-long-term-plan-for-syrian-refugees
(2017). Accessed 14 September 2018
The impact of Syria’s refugees on Southern Turkey
- S Cagaptay
- B Menekse
Cagaptay, S., Menekse, B.: The impact of Syria's refugees on Southern Turkey. Policy Focus
130. Washington, DC: The Washington Institute For Near East Policy.
http://www.washingtoninstitute.org/uploads/Documents/pubs/PolicyFocus130_Cagaptay_R
evised3s.pdf (2014). Accessed 31 August 2018
Yoksulluk nöbetinden yoksulların rekabetine: Türkiye’de mevsimlik tarımsal üretimde yabancı göçmen işçiler mevcut durum raporu
- S Dedeoğlu
Dedeoğlu, S.: Yoksulluk nöbetinden yoksulların rekabetine: Türkiye'de mevsimlik tarımsal
üretimde yabancı göçmen işçiler mevcut durum raporu. http://www.ka.org.tr/TumYayinlar
(2016). Accessed 12 September 2018
Beyond humanitarianism: Addressing the urban, self-settled refugees in Turkey
- I Stock
- M Aslan
- J Paul
- V Volmer
- T Faist
Stock, I., Aslan, M., Paul, J., Volmer, V., Faist, T.: Beyond humanitarianism: Addressing the
urban, self-settled refugees in Turkey. Bielefeld: COMCAD (2016)
Yoksulluğa dair: Bildiklerimiz, az bildiklerimiz, bilmediklerimiz
- O Işık
- E Ataç
Işık, O., Ataç, E.: Yoksulluğa dair: Bildiklerimiz, az bildiklerimiz, bilmediklerimiz. Birikim
269(268):66-86 (2011)
Syrian refugees in Turkey: the long road ahead. Migration Policy Institute
- A İçduygu
Syrian refugees in Turkey: The long road ahead
- A İçduygu
İçduygu, A.: Syrian refugees in Turkey: The long road ahead. Washington DC: Migration
Policy Institute (2015)
- A A Salah
- A Pentland
- B Lepri
- E Letouzé
- P Vinck
- Y A De Montjoye
- X Dong
- Ö Dağdelen
Salah, A.A., Pentland, A., Lepri, B., Letouzé, E., Vinck, P., de Montjoye, Y.A., Dong, X.,
Dağdelen, Ö.: Data for Refugees: The D4R Challenge on mobility of Syrian refugees in
Turkey. arXiv preprint arXiv:1807.00523 (2018)