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Springer Professional. Hint Swipe to navigate through the articles of this issue Close hint. Abstract Understanding what factors predict whether an urban migrant will end up in a deprived adobe acrobat reader dc 3d-inhalte aktivieren free or not could help prevent the exploitation of vulnerable individuals.
This study leveraged pseudonymized mobile money interactions combined with cell phone data to shed light on urban migration patterns and deprivation in Tanzania. A machine learning approach was then utilized to predict which migrants will move to poorer areas of the city, making them arguably more vulnerable to poverty, unemployment and exploitation.
The names, descriptions, and sources of all the features engineered and collated for this research. However, such urban settings can also be unstable, with a higher cost of living, leaving individuals potentially isolated and vulnerable [ 1 ].
At a national level, when urban migration rates exceed investment in job creation activities, the result is upward pressure on job competition leading to increased unemployment and a risk of exploitation—with some migrants being left in poverty and without support [ 2 ]. Despite this there remains limited understanding of the how such fast paced migration is impacting communities on the ground. Measurement remains the key challenge, with surveys such as national censuses which typically run every 10 years becoming rapidly out-of-date [ 4 ].
This raises serious logistical difficulties regarding how to locate, capture and study the impacts of adobe acrobat reader dc 3d-inhalte aktivieren free urban migration—particularly in lower-income countries and at sub-national levels.
Mobile phones provide a potential solution here. Due to historically under-developed land-line infrastructures, mobile devices have become ubiquitous across Africa and Asia, even in poorer or geographically isolated regions [ 5 ]. Billions of people now carry such devices, supporting the analysis of broad movement patterns through time and space.
The use of personal data rightly raises important ethical considerations and requirements [ 67 ]; yet if handled correctly, safely and respectfully, call records and other digital traces have the potential to reveal otherwise unavailable insights about short term, seasonal, and quickly changing domestic migration patterns.
A contributor to the increase of urban migration in Africa and Asia is thought to be the uptake of mobile money [ 11 ]; mobile money allows migrants to quickly and securely transfer funds home to their families without reliance on formal banking infrastructures which are often unavailable to them.
Over the last 10 years, use of mobile money has consequently become common-place in many lower-income countries, with uptake expanding further during the COVID pandemic [ 12 ]. Qualitative literature suggests that new migrants represent a particularly vulnerable community: a lack of opportunities in particular neighbourhoods increases the risk of poverty, exploitation, and even indentured labour and trafficking [ 220 ].
Yet migrants are not a homogeneous group. The exact mechanisms by which some individuals are left vulnerable have not been quantitatively examined, nor the characteristics of those most likely to end up in high-poverty, high-unemployment neighbourhoods, where social and economic impacts are most deleterious. Whilst slums, and the vast poorly serviced informal settlements that now make adobe acrobat reader dc 3d-inhalte aktivieren free the majority of East African cities, have been linked to increased exploitation [ 9 ], digital data traces such as those adobe acrobat reader dc 3d-inhalte aktivieren free cell phones and mobile money interactions offer the potential to quantitatively address this, and shed new light on these issues.
In this research we use Tanzania as a case study to explore the associated behaviours and predictors of migration to poorer urban areas within a low-income country. By leveraging pseudonymized mobile money transactions and cell phone data from a commercial mobile network provider, combined with survey data, we examine domestic migration patterns in Tanzania, and model corresponding vulnerabilities that can exist. Features derived from the call data and mobile money interactions, as well as open source socio-demographics information on the region an individual has migrated from, are used to generate predictive models; variable importance analysis is then used to interrogate the resulting model, to consider the characteristics of higher-risk urban migration.
With individuals migrating to deprived neighbourhoods hypothesised as being those most adobe acrobat reader dc 3d-inhalte aktivieren free to risk [ 21 ], the goals of this study adobe acrobat reader dc 3d-inhalte aktivieren free twofold: 1 Understand the differences, visible in digital traces, between migrating to a poorer urban area compared to a more affluent area by statistically comparing social and economic measures after individuals have moved.
It is hypothesised that individuals migrating to poorer areas will have less money coming into their account potentially indicating a financial vulnerability. Other indicators of risk for these migrants may include reduced social connections or contacts. The following section further expands relevant literature about mobile money, cell phone data, and migration. We then present our methods, explaining how urban migration can be detected using call detail records, how we engineer and select relevant features, and our modelling method.
The results section follows, outlining findings from two different types of analyses 1 using statistical tests to compare the social and economic differences between migrants who moved to poorer compared to richer areas of Dar es Salaam, and 2 the accuracy and interpretation of a prediction model, built to predict which migrants will end up in the more deprived areas of Dar es Salaam.
In the final section, we discuss adobe acrobat reader dc 3d-inhalte aktivieren free results and their limitations, as well as suggesting avenues for future investigation. Research has also shown that mobile money can help protect against the effects of negative shocks, such as flooding, due to increased capacity to receive financial support.
For low-lying countries such as Bangladesh, programmes such as forecast-based financing which use weather forecasts to trigger early actions such as cash transfers can help reduce the impact of a natural disaster. Increased resilience to negative shocks has the potential to make reactive or the forced migration of whole families less common; instead increasing the reliance on remittance payments from just a few individuals most commonly young males who have migrated to a city for work.
Tanzania was one of the earliest adopters of mobile adobe acrobat reader dc 3d-inhalte aktivieren free, and since its launch in adoption rates have been high [ adobe acrobat reader dc 3d-inhalte aktivieren free ].
Inalmost a third of active mobile money accounts in East Africa were in Tanzania [ 24 ]. It is not only one of the most populated in Africa, but also one of the fastest growing [ 2728 ].
The region in which Dar es Salaam is situated is one of the more affluent regions in Tanzania; yet it is also characterized by a far higher unemployment rate than the rest of the country and an increasing Gini-index a measure of the distribution of income across a population [ 26 ], reflecting the growing disparity between its rich and poor adobe acrobat reader dc 3d-inhalte aktivieren free.
A key driver of domestic migration to urban areas in Tanzania is low rural income, most commonly in agricultural sectors. After migrating to urban areas, one study found that Yet the higher cost of doing business in Dar es Salaam means that very few new businesses survive [ 26 ].
Such questions are extremely difficult to address via direct surveying, a method that often misses those who are most vulnerable [ 31 ] and most at risk from exploitation such as human trafficking and forced labour [ 2 ].
Even for the migrants who are able to save money, getting to a point of stability can take time, with individuals encountering a plethora of problems in the meantime, such as living in poverty, poor health, or unsafe conditions [ 30 ].
As a result, many migrants can be left without access to social support or afford adequate housing. In such regions, migration has been viewed by the local population as detrimental to society, contributing to shortages of housing, infrastructure, and services [ 32 ] and subsequently causing migrants to be viewed unfavourably and discriminated against.
These factors can leave urban migrants more vulnerable to deprivation, homelessness, disease and violence [ 2133 ]. Migrant women, especially those who are undocumented, are also more likely to experience labour market exploitation and are at greater risk of kidnap or trafficking [ 34 ]. Yet little is known about what factors might help to inform support services as adobe acrobat reader dc 3d-inhalte aktivieren free which migrants will end up in vulnerable circumstances—whether that be poverty, unsafe and unsanitary conditions, or exploitation.
With the fast changing urban landscape in many African and Asian countries, collecting data using surveys such as the national census can prove difficult logistically, are expensive, and can yield inaccurate or out-dated results [ 438 ]. Most censuses occur every ten years, have low granularity, and the validity of the information is rapidly outdated [ 3940 ]. In particular, shorter term migration patterns or seasonal migration is not captured, both of which are highly prevalent in developing adobe acrobat reader dc 3d-inhalte aktivieren free [ 4 ].
Over the years, several migration studies have identified the scarcity of reliable data available for quantitative analysis as a challenge to be overcome, particularly in developing countries [ 394243 ]. Novel data types such as digital traces have been proposed as proxies for traditional census data; assisting in analysis of urban migration in countries where such surveying is challenging [ 44 ].
As previously detailed, mobile phones are now ubiquitous in Africa and Asia, with the billions of people carrying such devices. The data produced from peoples interactions with mobile phones reflects real behaviour rather than self-reported behaviour, as in censuses and other surveys.
Yet despite multi-modal data from different contexts improving prediction accuracy [ 58 ], no research, to the best of our knowledge, has attempted to use this data combined with mobile money transactions to assess the characteristics and potential social and economic consequences of urban migration to the migrant themselves; nor examined the factors that implicate the deprivation level of where people migrate to within urban settings.
While academic research on mobile money and migration has previously focused on the rural communities left behind, one prior work [ 59 ] has shown that social networks in a destination location can strongly impact adobe acrobat reader dc 3d-inhalte aktivieren free success http://replace.me/9916.txt a migration.
This study expands this isolated crack designer 1.10.5 free, examining not only how often, but for whom urban migration is likely beneficial. Pseudonymized transactional data shared by a leading Tanzanian mobile network operator, comprising of i mobile phone call data records; and ii mobile financial services or mobile money data which can be linked to the call records.
Using these call records, migrants to Dar es Salaam were identified. From both the call and mobile money data, associated features were engineered and used to 1 measure the differences in mobile money and call activity between those moving to a poorer versus richer subward, and adobe acrobat reader dc 3d-inhalte aktivieren free predict the likelihood a given individual would migrate to некоторые alternatives logic pro x free download мой area of deprivation in Dar es Salaam.
An extensive street survey administered by the authors посетить страницу источник provide ground-truth measurements for deprivation levels across subwards in Dar es Salaam. This data was used, in combination with the call records, to label whether a migrant moved to a poorer or more affluent part of the city—the сайтец, pixelmator ipad raw files free download удивило variable in the prediction model.
This data allowed us to track movement patterns of individuals over time. Call detail records represent читать majority of mobile phone activity in Tanzania. The call data was pseudonymized before being received, so that individuals were only linkable by a unique identifier.
Using these, the call data was able to be attached to mobile financial services data, also from the same commercial provider. Mobile money data consisted of a log every time a customer of the service sent or received money, or checked their balance.
The data used in this study covered a total of , call events, and 48, transactions from 27, mobile phone subscribers in the Dar es Salaam region over the year Rankings were assigned from 75, comparative judgements made by local participants, whom we refer to henceforth as judges. To collect the data, a participatory approach was used to quantify knowledge and opinions of local residents on the ground in Dar es Salaam. Adobe acrobat reader dc 3d-inhalte aktivieren free carry out the judgements, a web interface was designed so http://replace.me/10256.txt judges could be shown images of pairs of subwards and asked to compare the affluence.
At the start of the survey, judges were asked to identify areas of the city they were familiar with. Then, during the judging process, judges had the option to indicate either i which of the two subwards they felt was more affluent, ii that the subwards were roughly equal in affluence, or iii that they were unfamiliar with at least one of the two adobe acrobat reader dc 3d-inhalte aktivieren free.
For further information on the methods used adobe acrobat reader dc 3d-inhalte aktivieren free obtaining the ranks from comparative judgements see [ 61 ]. Judges were recruited through word of mouth by students at local universities, NGOs, and via a local taxi driver association. Data was collected in situ over two weeks in August via 17 data collection sessions each lasting two hours.
At the start of each session, judges received a 15 minute training session in English and Swahili, and accompanying written instructions were also provided. Ethical approval for the study and its data collection process was obtained from the Nottingham University Business School ethical review committee, application reference No. The end goal was to label anonymous individuals in the data who we could be fairly certain, given their geo-located and timestamped call data, had migrated to Dar es Salaam приведу ссылку the time frame we were interested in.
To make the labelling process more efficient, we first cleaned the data to remove individuals we were certain we were not interested in including in our sample due to poor quality data, or their data not fitting our definition of migration using some filtering rules.
These rules were carefully constructed after interrogating the data, and were designed to prioritize data quality over data quantity. Specifically, we were interested in identifying anonymous individuals, with good quality data, who had manual adobe after effects cc 2018 permanently or semi-permanently to Dar es Salaam in the middle third of the year, from anywhere outside of the Dar region but still within Tanzania.
To eliminate individuals who obviously did not fit this definition, we first mined the call detail records using the following rules: 1. This was to eliminate individuals who might have moved at the start or the end of the year, and thus have inadequate data for adobe acrobat reader dc 3d-inhalte aktivieren free fair before and after moving comparison.
This prevented us from capturing people who commuted to Dar es Salaam for work, or who were only visiting for a short stay. Finally, to ensure the potential migrants had sufficient mobile money data for us to analyse and engineer features from, individuals had to have at least 10 or more mobile money transaction logs triggered by either receiving or sending money, or checking their balance.
Using these filters, from the 27, individuals, a sample of potential migrants to Dar es Salaam was extracted, along with estimated move dates. Discussions were had prior to labelling as to what constituted a valid case of migration and move date, and what did not.
Examples of the two types посетить страницу 3D plots used in this stage which were rotatable for the labeller adobe acrobat reader dc 3d-inhalte aktivieren free the provided interface can be viewed in Fig. All graphs in Fig. Graph B shows an exemplar migrant affiliated with the University of Dar es Salaam. Graph D illustrates how both a broad work and a home location might be identified in the data. Figure 1 Examples of the visualisations of call detail records used by human labellers to determine whether it could be accurately estimated whether and when someone migrated to Dar es Salaam.
The estimated move date is depicted by the pink hyper-plane. Each graph is a different individual, and each translucent dot represents a mobile phone call. All examples were labelled as correctly identifying migration to Dar es Salaam, with the exception of F which was deemed to have too large of an overlap due to what looks like regular commuting before migrating. Перейти на страницу that, in addition to the adobe acrobat reader dc 3d-inhalte aktivieren free nature of the data, to ensure differential privacy was strictly observed we restricted location resolution in Dar es Salaam to one of the subwards with subwards having an average of approximately 15, inhabitants each.
Nonetheless broad movement patterns could still be labelled, with Graph E showing an example of someone who visited their previous home region for an extended period after moving to Dar es Salaam.
Adobe acrobat reader dc 3d-inhalte aktivieren free
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Adobe acrobat reader dc 3d-inhalte aktivieren free
A list of these features can be found in Additional file 1. Three classes of classification algorithm were evaluated: logistic regression, decision trees, and random forests—all chosen for their interpretable variable importance outputs. If a single move date could not be confidently determined from the visualisations then the individual was excluded from the sample. Likewise, certain lands in Malaysia and Indonesia have inceptisol soils. The next screenshot shows the highlight options that are available. In: IEEE international продолжить on big data big data. Features are ordered by the absolute coefficient value, with the most predictive adobe acrobat reader dc 3d-inhalte aktivieren free first.