publications

Barriers in cities, such as administrative boundaries, natural obstacles, railways or major roads are thought to induce segregation. However, the empirical knowledge about this phenomenon is limited. Here, we present a network science framework to assess barriers to urban mobility along their hierarchy, across residential areas and visited amenities. Using GPS mobility data, we construct a network of blocks from the sequence of individual stays in a major European city. A community detection algorithm allows us to partition this network into non-overlapping areas of dense mobility clusters, in which the effect of transportation hubs can be tuned with a parameter. We apply the Symmetric Area Difference index to quantify the overlap between these mobility clusters and the polygons of urban area separated by barriers. Reducing the effect of transportation hubs results in smaller scale mobility clusters that fit better to lower rank administrative or road barriers compared to their higher rank pairs. We find that characteristic urban barriers can replace each other in dividing mobility clusters of different scales. Next, we define the Barrier Crossing Ratio, the fraction of barrier crossings that link mobility clusters. An event study of bridge closure demonstrates that the Barrier Crossing Ratio can capture barrier effects even on small spatial scales. The decomposition of this indicator by origins and destinations suggests a significantly higher impact of barriers on those who live closer to the city center and smaller impact on visits to complex amenities. These results contribute to the ongoing discourse on urban segregation, emphasizing the importance of barriers to urban mobility in shaping interactions and mixing.

Researchers face the trade-off between publishing mobility data along with their papers while protecting the privacy of the individuals. In addition to the anonymization process, other techniques, such as spatial discretization and location concealing or removal, are applied to achieve these dual objectives. The primary research question is whether concealing the observation area is an adequate form of protection or whether human mobility patterns in urban areas are inherently revealing of location. The characteristics of the mobility data, such as the number of activity records in a given spatial unit, can reveal the silhouette of the urban landscape, which can be used to infer the identity of the city in question. The presented locating method was tested on multiple cities using different open datasets and coarser spatial discretization units. While publishing mobility data is essential for research, concealing the observation area is insufficient to prevent the identification of the urban area. Instead of obscuring the observation area, noise should be added to the trajectories to mitigate privacy risks regarding the individuals.

Cities host diverse people and their mixing is the engine of prosperity. In turn, segregation and inequalities are common features of most cities and locations that enable the meeting of people with different socio-economic status are key for urban inclusion. In this study, we adopt the concept of economic complexity to quantify the sophistication of amenity supply at urban locations. We propose that neighborhood complexity and amenity complexity are connected to the ability of locations to attract diverse visitors from various socio-economic backgrounds across the city. We construct the measures of amenity complexity based on the local portfolio of diverse and non-ubiquitous amenities in Budapest, Hungary. Socio-economic mixing at visited third places is investigated by tracing the daily mobility of individuals and by characterizing their status by the real-estate price of their home locations. Results suggest that measures of ubiquity and diversity of amenities do not, but neighborhood complexity and amenity complexity are correlated with the urban centrality of locations. Urban centrality is a strong predictor of socio-economic mixing, but both neighborhood complexity and amenity complexity add further explanatory power to our models. Our work combines urban mobility data with economic complexity thinking to show that the diversity of non-ubiquitous amenities, central locations, and the potentials for socio-economic mixing are interrelated.

In this study, call detail records (CDR), covering Budapest, Hungary, are processed to analyze the circadian rhythm of the subscribers. An indicator, called wake-up time, is introduced to describe the behavior of a group of subscribers. It is defined as the time when the mobile phone activity of a group rises in the morning. Its counterpart is the time when the activity falls in the evening. Inhabitant and area-based aggregation are also presented. The former is to consider the people who live in an area, while the latter uses the transit activity in an area to describe the behavior of a part of the city. The opening hours of the malls and the nightlife of the party district are used to demonstrate this application as real-life examples. The proposed approach is also used to estimate the working hours of the workplaces. The findings are in a good agreement with the practice in Hungary, and also support the workplace detection method. A negative correlation is found between the wake-up time and mobility indicators (entropy, radius of gyration): on workdays, people wake up earlier and travel more, while on holidays, it is quite the contrary. The wake-up time is evaluated in different socioeconomic classes, using housing prices and mobile phones prices, as well. It is found that lower socioeconomic groups tend to wake up earlier.

The analysis of human movement patterns based on mobile network data makes it possible to examine a very large population cost-effectively and has led to several discoveries about human dynamics. However, the application of this data source is still not common practice. The goal of this study was to analyze the commuting tendencies of the Budapest Metropolitan Area using mobile network data as a case study and propose an automatized alternative approach to the current, questionnaire-based method, as commuting is predominantly analyzed by the census, which is performed only once in a decade in Hungary. To analyze commuting, the home and work locations of cell phone subscribers were determined based on their appearances during and outside working hours. The detected home locations of the subscribers were compared to census data at a settlement level. Then, the settlement and district level commuting tendencies were identified and compared to the findings of census-based sociological studies. It was found that the commuting analysis based on mobile network data strongly correlated with the census-based findings, even though home and work locations were estimated by statistical methods. All the examined aspects, including commuting from sectors of the agglomeration to the districts of Budapest and the age-group-based distribution of the commuters, showed that mobile network data could be an automatized, fast, cost-effective, and relatively accurate way of analyzing commuting, that could provide a powerful tool for sociologists interested in commuting.

In this study, Call Detail Records (CDRs) covering Budapest for the month of\ \ June in 2016 were analyzed. During this observation period, the 2016 UEFA European\ \ Football Championship took place, which significantly affected the habit of the\ \ residents despite the fact that not a single match was played in the city. We\ \ evaluated the fans\u2019 behavior in Budapest during and after the Hungarian matches\ \ and found that the mobile phone network activity reflected the football fans\u2019\ \ behavior, demonstrating the potential of the use of mobile phone network data\ \ in a social sensing system. The Call Detail Records were enriched with mobile\ \ phone properties and used to analyze the subscribers\u2019 devices. Applying the\ \ device information (Type Allocation Code) obtained from the activity records,\ \ the Subscriber Identity Modules (SIM), which do not operate in cell phones, were\ \ omitted from mobility analyses, allowing us to focus on the behavior of people.\ \ Mobile phone price was proposed and evaluated as a socioeconomic indicator and\ \ the correlation between the phone price and the mobility customs was found. We\ \ also found that, besides the cell phone price, the subscriber age and subscription\ \ type also had effects on users\u2019 mobility. On the other hand, these factors\ \ did not seem to affect their interest in football.

In this article, we explore the relationship between cellular phone data and housing prices in Budapest, Hungary. We determine mobility indicators from one months of Call Detail Records (CDR) data, while the property price data are used to characterize the socioeconomic status at the Capital of Hungary. First, we validated the proposed methodology by comparing the Home and Work locations estimation and the commuting patterns derived from the cellular network dataset with reports of the national mini census. We investigated the statistical relationships between mobile phone indicators, such as Radius of Gyration, the distance between Home and Work locations or the Entropy of visited cells, and measures of economic status based on housing prices. Our findings show that the mobility correlates significantly with the socioeconomic status. We performed Principal Component Analysis (PCA) on combined vectors of mobility indicators in order to characterize the dependence of mobility habits on socioeconomic status. The results of the PCA investigation showed remarkable correlation of housing prices and mobility customs.

Advancement of accurate models for predicting real estate price is of utmost\ \ importance for urban development and several critical economic functions. Due\ \ to the significant uncertainties and dynamic variables, modeling real estate has\ \ been studied as complex systems. In this study, a novel machine learning method\ \ is proposed to tackle real estate modeling complexity. Call detail records (CDR)\ \ provides excellent opportunities for in-depth investigation of the mobility characterization.\ \ This study explores the CDR potential for predicting the real estate price with\ \ the aid of artificial intelligence (AI). Several essential mobility entropy factors,\ \ including dweller entropy, dweller gyration, workers\u2019 entropy, worker gyration,\ \ dwellers\u2019 work distance, and workers\u2019 home distance, are used as input\ \ variables. The prediction model is developed using the machine learning method\ \ of multi-layered perceptron (MLP) trained with the evolutionary algorithm of particle\ \ swarm optimization (PSO). Model performance is evaluated using mean square error\ \ (MSE), sustainability index (SI), and Willmott\u2019s index (WI). The proposed\ \ model showed promising results revealing that the workers\u2019 entropy and the\ \ dwellers\u2019 work distances directly influence the real estate price. However,\ \ the dweller gyration, dweller entropy, workers\u2019 gyration, and the workers\u2019\ \ home had a minimum effect on the price. Furthermore, it is shown that the flow\ \ of activities and entropy of mobility are often associated with the regions with\ \ lower real estate prices.