Prieto Curiel, R., Pappalardo, L., Gabrielli, L. & Bishop, S. R. Gravity and scaling laws of city to city migration. A survey of hybrid deep learning methods for traffic flow prediction. Balcan, D. et al. Surv. For each origin location (e.g., li), n input vectors x(li,lj) with j=1,. Growth, innovation, scaling, and the pace of life in cities. (c, f, i) Average relative improvement over five independent experiments in terms of CPC (in percentage) of DG with respect to G for each region of interest of side size 25km in England, Italy, and New York State. Article An analytical framework to nowcast well-being using mobile phone data. We acknowledge the OpenStreetMap contributors, OpenStreetMap data are available under the Open Database License and licensed as CC BY-SA https://creativecommons.org/licenses/by-sa/2.0/. in IJCAI, 35563563 (AAAI Press, 2018). Nature 484, 96 (2012). Find stories, updates and expert opinion. Foody, G. et al.) Disasters 29, 370385 (2005). Inf. In this way, there could be multiple empty strings in memory, in contrast with the formal theory definition, for which there is only one possible empty string. Int. 5), one of the most relevant features with large Shapely values is the geographic distance: as expected, a large distance between origin and destination contributes to a reduction of flow probability, while a small distance leads to an increase. Zipf, G. K. The p 1 p 2/d hypothesis: on the intercity movement of persons. Data Sci. The CPC in each region of interest is the average CPC over the runs in which that region of interest has been selected in at least one test set. b Architecture of Deep Gravity. Specifically, the output of hidden layer h is given by the vector z(0)(li,lj)=a(W(0)x(li,lj)) for the first layer (h=0) and z(h)(li,lj)=a(W(h)z(h1)(li,lj)) for h>0, where W are matrices whose entries are parameters learned during training. It is not possible to aggregate or disaggregate flows between OAs onto flows between locations of the same size (e.g., using a squared tessellation) without introducing significant distortions in the data or obtaining aggregated locations of size much larger than the area of the largest OA. Following a bumpy launch week that saw frequent server trouble and bloated player queues, Blizzard has announced that over 25 million Overwatch 2 players have logged on in its first 10 days. The 20112020 decade warmed to an average 1.09 C [0.951.20 C] compared to the pre-industrial baseline (18501900). In fact, when the generated total outflow is equal to the real total outflow, the denominator becomes 2i,jyr(li,lj) and the CPC measures the fraction of all trips that were assigned to the correct destination, i.e., the fraction of correct predictions or accuracy. Mobile phone data for informing public health actions across the COVID-19 pandemic life cycle. Each location may have several Points Of Interest (POIs, the colored points), extracted from OpenStreetMap (OSM). 8, 440 (2019). They are obtained by composing a combination of variables and their average change depending on the presence or absence of the variables to determine the importance of a single variable based on game theory79. It uses mathematics, physics, and chemistry in order to explain their origin and evolution.Objects of interest include planets, moons, stars, nebulae, galaxies, and comets. Am. Karemera, D., Oguledo, V. I. To reduce the training time, we use negative sampling and consider up to 512 randomly selected destinations for each origin location. Notably, in 1946 George K. Zipf proposed a model to estimate mobility flows, drawing an analogy with Newtons law of universal gravitation46. For instance, locations having a large number of food facilities, retail, and industrial zones are predicted to attract commuters. We've developed a suite of premium Outlook features for people with advanced email and calendar needs. In this work, we propose Deep Gravity, an effective model to generate flow probabilities that exploits many features (e.g., land use, road network, transport, food, health facilities) extracted from voluntary geographic data, and uses deep neural networks to discover non-linear relationships between those features and mobility flows. Netw. Social vulnerability and migration in the wake of disaster: the case of hurricanes katrina and rita. Tanaka, Y., Iwata, T., Kurashima, T., Toda, H. & Ueda, N. Estimating latent people flow without tracking individuals. MATH Simini, F., Barlacchi, G., Luca, M. & Pappalardo, L. Deep gravity (1.1.0). From a global perspective (Fig. 1b). The rich set of integrated ABAP testing and analysis tools ensure functional and formal correctness of ABAP code, guarantee quality and robustness, and offer support for custom code migration to SAP S/4HANA and the cloud. Cities are complex and dynamic ecosystems that define where people live, how they move around, whom they interact with, and how they consume services1,2,3,4,5. Note that DGs improvement on G is a common characteristic (see Fig. Xie, P. et al. Popul. The Python code of Deep Gravity is available at github.com/scikit-mobility/DeepGravity. PubMed Central Traffic congestion, domestic migration, and the spread of infectious diseases are processes in which the presence of mobility flows induces a net change of the spatial distribution of some quantity of interest (e.g., vehicles, population, pathogens). The loss function of Deep Gravity is the cross-entropy: where y(li,lj)/Oi is the fraction of observed flows from li that go to lj and pi,j is the models probability of a unit flow from li to lj. Multiple independent instrumental datasets show that the climate system is warming. & Misra, A. Among all relevant problems in the study of human mobility, the generation of mobility flows, also known as flow generation14,15,17, is particularly challenging. Policy 94, 34 42 (2020). The Italian census covers 15,003,287 commuters with an average flow of 2.07 and a standard deviation of 4.27. 3a-c). The Indo-Aryan migrations were the migrations into the Indian subcontinent of Indo-Aryan peoples, an ethnolinguistic group that spoke Indo-Aryan languages, the predominant languages of today's North India, Pakistan, Nepal, Bangladesh, Sri Lanka and the Maldives.Indo-Aryan population movements into the region from Central Asia are considered to have started after 2000 BCE, as a Int. By submitting a comment you agree to abide by our Terms and Community Guidelines. (3) is proportional to the cross-entropy loss, \(H=-{\sum }_{i}{\sum }_{j}\frac{y({l}_{i},{l}_{j})}{{O}_{i}}{{{{{{\mathrm{ln}}}}}}}\,{p}_{i,j}\), of a shallow neural network with an input of dimension two and a single linear layer followed by a softmax layer. As suggested by the value of CPC computed on the flows in that region of interest, DGs network of flows is visually more similar to the real ones than Gs one, both in terms of structure and distribution of flow values. Rossi, A., Barlacchi, G., Bianchini, M. & Lepri, B. Modelling taxi drivers behaviour for the next destination prediction. Sirbu, A. et al. The network is trained for 20 epochs with the RMSprop optimizer with momentum 0.9 and learning rate 5106 using batches of size 64 origin locations. We show some insights provided by SHAP for global explanations in the three countries considered (Fig. vol. Although an overall CPC=0.32 may seem low, we should consider that human mobility is a highly complex system: on the one hand, the number of factors influencing the decision underlying peoples displacements are far more than those captured by the available features; on the other hand, mobility flows have an intrinsic random component and hence the prediction of a single event cannot be determined in a deterministic way. Sci. he commuting data for Italy are freely available at http://datiopen.istat.it/datasetPND.php and https://www.istat.it/it/archivio/104317. ACM Trans. The performance of all models does not change significantly if we use regions of interest of 10 by 10 km2. & Krumm, K. Land use inference from mobility traces. According to this interpretation, the gravity model is a linear classifier based on two explanatory variables, i.e., population and distance. Public Health 8, 31343143 (2011). Despite these country-specific differences, we observe a clear pattern in our results that is valid for all countries: G has always the worst performance and DG has always the best performance, while MFG and NG have intermediate performances. Article 33, 39353942 (AAAI Press, 2019). Planet dump. Deep Gravity originates from the observation that the state-of-the-art model of flow generation, the gravity model15,46,47,72, is equivalent to a shallow linear neural network. Half of these regions are used to train the models and the other half are used for testing. These explanations should take into account the peculiarities of flow generation (e.g., spatiality, networking), providing more suitable global and local explanations of mobility flows. Nair, V. & Hinton, G. E. Rectified linear units improve restricted boltzmann machines. Finally, we show how to explain the contribution of each feature to Deep Gravitys prediction for a single origin-destination pair. https://digital-strategy.ec.europa.eu/en/library/ethics-guidelines-trustworthy-ai. 6, eabc0764 (2020). in ACM SIGKDD Explorations Newsletter, 119 (Association for Computing Machinery, 2019). Find photo galleries with beautiful, provoking images on latest news stories on NBCNews.com. https://doi.org/10.1038/s41467-021-26752-4, DOI: https://doi.org/10.1038/s41467-021-26752-4. Circles (DG), triangles (NG), and squares (MFG) indicate the average CPC for a decile. The network has 15 hidden layers of dimensions 256 (the bottom six layers) and 128 (the other layers) with LeakyReLu74 activation function, a. We observe that Deep Gravity still outperforms all its competitors even when using a smaller region of interest size. The fact that populations and distance are more relevant than other geographic features in Italy and New York State explains why the Nonlinear Gravity model (NG) outperforms the Multi-Feature Gravity model (MFG) in these two countries: a deep-learning model that is able to capture the existing nonlinear relationship between populations and distance can accurately predict the flow probabilities, while the other geographic features only bring a marginal contribution. the sustainable development goals as a network of targets. 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Transp. ,n. These probabilities are computed in three steps (see Fig. Environ. The economy of Ohio nominally would be the 25th-largest global economy behind Sweden and ahead of Nigeria according to the 2013 World Bank projections, and the 24th-largest global economy behind Sweden and ahead of Norway according to the 2013 International Monetary Fund projections. Use our site search. Since 1950, the number of cold PLoS ONE 5, e13541 (2010). Moreover, a future improvement of the model may consist in analyzing whether we can apply geographic transferability on other scales: Can we use rural areas flows to generate flows in cities? Note that the sum over i of the cross-entropies of different origin locations follows from the assumption that flows from different locations are independent events, which allows us to apply the additive property of the cross-entropy for independent random variables. Warfare refers to the common activities and characteristics of types of war, or of wars in general. J. Artif. The images or other third party material in this article are included in the articles Creative Commons license, unless indicated otherwise in a credit line to the material. Consistent individualized feature attribution for tree ensembles. Syst. Inf. The generated flow between two locations is then obtained by multiplying the probability (i.e., the models output) and the origins total outflow. https://doi.org/10.1007/s41060-020-00224-2 (2020). In particular, all models have a CPC25km around 0.03 higher than CPC10km (see Supplementary Fig. We collect information about the geographic features of each location from OpenStreetMap (OSM)70,71, an online collaborative project aimed to create an open source map of the world with geographic information collected from volunteers. This is due to newswire licensing terms. Videos, games and interactives covering English, maths, history, science and more! Human mobility modelling has important applications in these research areas. in Proc. Structural semantic models for automatic analysis of urban areas. De Nadai, M., Xu, Y., Emmanuel, L., Gonzlez, M. C. & Lepri, B. Socio-economic, built environment, and mobility conditions associated with crime: a study of multiple cities. Dev. Phys. USA 104, 73017306 (2007). Nat. In particular, in New York State there are 5367 CTs. 41, 647665 (2014). Transforming our world: the 2030 agenda for sustainable development. This example illustrates how DGs predictions for individual flows depend on the various geographical variables considered, and that the most relevant features for a specific origin-destination pair can differ from the most relevant features overall (Fig. Anal. Behav. Salah, A. PubMed Central A Deep Gravity model for mobility flows generation. CAS Google Scholar. To investigate the performance of the model in high and low populated regions, we split each countrys regions of interest into ten equal-sized groups, i.e., deciles, based on their population, where decile 1 includes the regions of interest with the smaller population and decile 10 includes the regions of interest with the larger population, and we analyze the performance of the four models in each decile (Fig. Cevik, S. Going Viral: A Gravity Model of Infectious Diseases and Tourism Flows. Knowl. Figure2a-c compares real flows with flows generated by DG and G on a region of interest in England. The gravity model of migration is a model in urban geography derived from Newton's law of gravity, and used to predict the degree of migration interaction between two places. Finally, scores are transformed into probabilities using a softmax function, \({p}_{i,j}={e}^{s({l}_{i},{l}_{j})}/{\sum }_{k}{e}^{s({l}_{i},{l}_{k})}\), which transforms all scores into positive numbers that sum up to one. Given the nine England major cities, i.e., the so-called Core Cities76 and London, the training dataset contains the locations and the information of eight cities and the test set contains information on the city excluded from the training. To investigate the model generalization capability, we design specific training and testing datasets so that a city is never seen during the training phase. Sci. The interpretation of the SHAP value for variable value j is: the value of the jth variable contributed j to the prediction of a particular instance compared to the average prediction for the dataset66. Jayarajah, K., Tan, A. ,n are created, one for each location in the region of interest that could be a potential destination. 32, 17451755 (2000). & Ramasco, J. J. Ruktanonchai, N. W. et al. The improvement of DG over G is again spread in all areas of the two countries (Fig. Article Human migration: the big data perspective. Foundations of Linear and Generalized Linear Models (John Wiley & Sons, 2015). Anal. 5) and for local explanations for an origin-destination pair in England (Fig. Finally, in New York State, there are 41,070,279 commuters with an average of 66.86 people traveling between CTs and a standard deviation of 364.58. The UK Census covers 30,008,634 commuters with an average flow of 1.78 and a standard deviation of 3.21. The effects of ruralurban migration on corporate innovation: evidence from a natural experiment in china. And can we use a model trained on an entire country to generate flows on a different one? Our global writing staff includes experienced ENL & ESL academic writers in a variety of disciplines. Google Scholar. Get the latest international news and world events from Asia, Europe, the Middle East, and more. PubMed In this regard, as depicted by the explanations extracted from DG, the impact of the voluntary geographic information and nonlinearity varies from country to country: while in England geographic information plays the strongest role in the models performance, the nonlinearity predominates in Italy and New York State. in Adjunct Proceedings of the 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2019 ACM International Symposium on Wearable Computers, 10081013 (Association for Computing Machinery, 2019). International political economy, a sub-category of international relations, studies issues and impacts from for example international conflicts, One example of such an econometric model is the gravity equation. In the latter, we use single observations or smaller sets of observations (e.g., a specific decile) to both understand which features played a role in a specific prediction or, more in general, if the set of features used to predict flows in different deciles vary and how much it changes. The commuting data for England are freely available at https://census.ukdataservice.ac.uk/use-data/guides/flow-data.aspx and https://census.ukdataservice.ac.uk/use-data/guides/boundary-data. Zhang, C. et al. Sci. 3, Supplementary Table2, and Supplementary Note3). M.L. A unified approach to interpreting model predictions. ADS SHAP values allow us to give both a global and local explainability of Deep Gravity. 25th international conference on world wide web, 413423 (International World Wide Web Conferences, 2016). in Proc. Shapley values consist in the average of the marginal contributions across all the permutations of the players solving a game. MATH SHAP (SHapley Additive exPlanations) applies a game theoretic approach to explain the output of any machine learning model79. Gonzlez, M. C., Hidalgo, C. A. A Deep Gravity model for mobility flows generation, \({{{{{{{\mathcal{T}}}}}}}}=\{{l}_{i}:i=1,,n\}\), \({l}_{i}\cap {l}_{j}={{\emptyset}},\ \forall i\,\ne\, j\), \(\mathop{\bigcup }\nolimits_{i = 1}^{n}{l}_{i}=R\), $${{CPC}}=\frac{2{\sum }_{i,j}min({y}^{g}({l}_{i},{l}_{j}),{y}^{r}({l}_{i},{l}_{j}))}{{\sum }_{i,j}{y}^{g}({l}_{i},{l}_{j})+{\sum }_{i,j}{y}^{r}({l}_{i},{l}_{j})}$$, $$\bar{y}({l}_{i},{l}_{j})={O}_{i}{p}_{ij}={O}_{i}\frac{{m}_{j}^{{\beta }_{1}}f({r}_{ij})}{{\sum }_{k}{m}_{k}^{{\beta }_{1}}f({r}_{ik})}$$, $${{Log}}\; L(\beta | y)\propto {{{{{{\mathrm{ln}}}}}}}\,\left(\mathop{\prod}\limits_{i,j}{p}_{ij}^{y({l}_{i},{l}_{j})}\right)= \, \mathop{\sum}\limits_{i,j}y({l}_{i},{l}_{j}){{{{{{\mathrm{ln}}}}}}}\,\frac{{m}_{j}^{{\beta }_{1}}f({r}_{ij})}{{\sum }_{k}{m}_{k}^{{\beta }_{1}}f({r}_{ik})}\\ = \, \mathop{\sum}\limits_{i,j}y({l}_{i},{l}_{j}){{{{{{\mathrm{ln}}}}}}}\,\frac{{e}^{\beta \cdot x({l}_{i},{l}_{j})}}{{\sum }_{k}{e}^{\beta \cdot x({l}_{i},{l}_{k})}}$$, \(x({l}_{i},{l}_{j})={{concat}}[{x}_{j},{{{{{{\mathrm{ln}}}}}}}\,{r}_{ij}]\), \({x}_{j}={{{{{{\mathrm{ln}}}}}}}\,{m}_{j}\), \(H=-{\sum }_{i}{\sum }_{j}\frac{y({l}_{i},{l}_{j})}{{O}_{i}}{{{{{{\mathrm{ln}}}}}}}\,{p}_{i,j}\), \({p}_{i,j}={e}^{s({l}_{i},{l}_{j})}/{\sum }_{k}{e}^{s({l}_{i},{l}_{k})}\), $$H=-\mathop{\sum}\limits_{i}\mathop{\sum}\limits_{j}\frac{y({l}_{i},{l}_{j})}{{O}_{i}}{{{{{{\mathrm{ln}}}}}}}\,{p}_{i,j},$$, \(G={\{{R}_{ij}\}}_{i = 1,..,{L}_{x};j = 1,,{L}_{y}}\), https://doi.org/10.1038/s41467-021-26752-4. Correspondence to DG improves especially where current models are unrealistic. ADS Article While both DG and G underestimate the flows, DG captures the overall structure of the flow network more accurately than G. We obtain similar results for Italy and New York State (Table1): DG performs significantly better than the other models, with an improvement in terms of global CPC over G of 66% (Italy) and 1076% (New York State). Google Scholar. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. It relies on the Shapley values from game theory81, which connect optimal credit allocation with local explanations. SHAP is based on game theory81 and estimates the contribution of each feature based on the optimal Shapley value79, which denotes how the presence or absence of that feature change the model prediction of a particular instance compared to the average prediction for the dataset66 (see Methods for details). ADS J. Note that the negative of loglikelihood in Eq. Measuring objective and subjective well-being: dimensions and data sources. Natl Acad. Pappalardo, L., Ferres, L., Sacasa, M., Cattuto, C. & Bravo, L. Evaluation of home detection algorithms on mobile phone data using individual-level ground truth. This research used resources of the Argonne Leadership Computing Facility, which is a DOE Office of Science User Facility supported under Contract DE-AC02-06CH11357. Measuring mobility, disease connectivity and individual risk: a review of using mobile phone data and health for travel medicine. Sci. Proc. & Giannotti, F. Using big data to study the link between human mobility and socio-economic development. This allows us to observe that, while in Italy and New York State the nonlinear relationship between population and distance captured by the model provides the strongest contribution to predict the flow probability, in England the interplay between the various geographic features plays a key role in boosting the models predictions. Google Scholar. We use OpenStreetMap (OSM) data to compute the 18 geographic features for the origin and 18 for the destination. Each region of interest is further split into locations using Output Areas (OAs, for England), Census Areas (CAs, for Italy), or Census Tracts (CTs, for New York State). (2019). LDG produces average CPCs that are remarkably close to the DGs ones (see Supplementary Fig. We use SHapley Additive exPlanations (SHAP)79,80 to understand how the input geographic features contribute to determine the output of Deep Gravity. This opens the doors to a set of intriguing questions regarding the models geographical explainability and transferability14. Peer reviewer reports are available. We prefer neural networks over other machine learning models because they are the natural extension of the state-of-the-art model for flow generation, i.e., the singly constrained gravity model15,47, which corresponds to a multinomial logistic regression that is formally equivalent to a linear neural network with one softmax layer. . in Advances in Neural Information Processing Systems (eds. & Barabsi, A.-L. A universal model for mobility and migration patterns. The introduction of the geographic features (MFG) and of nonlinearity and hidden layers (DG) leads to a significant improvement of the overall performance. Get the most important science stories of the day, free in your inbox. Soto, V., Frias-Martinez, V., Virseda, J. Features are reported on the vertical axis, sorted from the most relevant on top to the least relevant on the bottom. Res. Unfortunately, information about real commuting flows at country level are provided by official statistics bureaus at the level of OAs only, which are administrative units of different shapes. Adv. Typically, the deterrence function f(rij) can be either an exponential, \(f(r)={e}^{{\beta }_{2}r}\), or a power-law function, \(f(r)={r}^{{\beta }_{2}}\), where 2 is another parameter. ,vq that define the polygon boundary. Return to the home page. We thank Daniele Fadda for his support on data visualization and plots design. Sci. PubMed . in Thirty-First AAAI Conference on Artificial Intelligence (AAAI Press, 2017). Each flow in Deep Gravity is hence described by 39 features (18 geographic features of the origin and 18 of the destination, distance between origin and destination, and their populations). Google Scholar. We find similar results by testing the model on Newcastle, Liverpool and Nottingham. That is, we cannot use a subset of the flows between the locations in the region of interest neither historical information to generate other flows in the same region. ADS All authors contributed to interpreting the results and writing the paper. Given a tessellation, \({{{{{{{\mathcal{T}}}}}}}}\), over a region of interest R, and the total outflows from all locations in \({{{{{{{\mathcal{T}}}}}}}}\), we aim to estimate the flows, y, between any two locations in \({{{{{{{\mathcal{T}}}}}}}}\). All values of features for a given location (excluding distance) are normalized dividing them by the locations area. https://doi.org/10.1145/3236009 (2018). In England, MFG outperforms NG, as opposed to Italy and New York State where NG outperforms MFG (Fig. collected the data for US. The ability to accurately describe the dynamics of these processes depends on our understanding of the characteristics of the underlying spatial flows and it is crucial to make cities and human settlements inclusive, safe, resilient, and sustainable43,44,45. Our experiments, conducted on mobility flows in England, Italy, and New York State, show that Deep Gravity achieves a significant increase in performance, especially in densely populated regions of interest, with respect to the classic gravity model and models that do not use deep neural networks or geographic data. This setting allows us to discover whether we can generate flows for a city where no flows have been used to train the model, a peculiarity that we cannot fully investigate if the model partially see a city (e.g., use some of the city flows during the training phase). The latest Lifestyle | Daily Life news, tips, opinion and advice from The Sydney Morning Herald covering life and relationships, beauty, fashion, health & wellbeing Rev. The interpretation of the flow generation problem as a classification problem allows us to naturally extend the gravity models shallow neural network introducing hidden layers and nonlinearities. Distribution of Shapely values for all features in Deep Gravity for England (a), Italy (b), and New York State (c). Natl Acad. Article Visit the U.S. Department of State Archive Websites page. In England, however, in contrast with the usual assumption of the gravity model that the flow probability is an increasing function of the population, we find that population has a mixed effect, with high values of the populations feature (red points in Fig. 3 (Vsp, 1990). Science 369, 14651470 (2020). Deep Gravity is a geographic agnostic model able to generate flows between locations for any urban agglomeration, given the availability of appropriate information such as the tessellation, the total outflow per location, the population in the locations, and the information about POIs. All models are generally more accurate on scarcely populated regions, which have fewer locations and trips destinations are thus easier to predict. This model, known as the gravity model, is based on the assumption that the number of travelers between two locations (flow) increases with the locations populations while decreases with the distance between them15,47. Regarding Italy, the Italian national statistics bureau (ISTAT) defines 402,678 nonoverlapping polygons known as Census Areas (CAs), which cover the entire country. Welcome to Patent Public Search. 6). In particular, we select 15 regions of interest corresponding to London, eight to Leeds, seven to Sheffield, five to Birmingham, four to Bristol, Liverpool, Manchester and Newcastle, and three to Nottingham. J. J. Environ. Exclusive stories and expert analysis on space, technology, health, physics, life and Earth developed this work prior joining Amazon. Science 368, 493497 (2020). 49, 521545 (2020). Finally, a softmax function is used to transform the scores into probabilities pi,j, which are positive numbers that sum up to one. Assessing the impact of coordinated covid-19 exit strategies across europe. PubMed CAS Int. 23, 176187 (2015). De Nadai, M. et al. b, c Shapely values for the two flows between E00137201 (population of about 238 individuals) and E00137194 (population of about 223 individuals). Nodes are geographic points, stored as latitude and longitude pairs, which represent points of interests (e.g., restaurants, schools). https://doi.org/10.1007/s11079-021-09619-5 (Springer, 2021). Deep Gravity assigns different probabilities for the two flows and the Shapely values indicate that various geographical features are more relevant than the population in this case. . The average CPC slightly decreases when tested on Bristol and Sheffield, while it does not change significantly with respect to DG on Leeds, Birmingham and Manchester. 83. For each variable, these points are randomly jittered along the vertical axis to make overlapping ones visible. Data Sci. Simini, F., Barlacchi, G., Luca, M. et al. Guidotti, R. et al. Agresti, A. & Barabsi, A.-L. Understanding individual human mobility patterns. 5a). Barbosa, H. et al. Earth is the third planet from the Sun and the only astronomical object known to harbor life.While large volumes of water can be found throughout the Solar System, only Earth sustains liquid surface water.About 71% of Earth's surface is made up of the ocean, dwarfing Earth's polar ice, lakes, and rivers.The remaining 29% of Earth's surface is land, consisting of continents and islands. Byrne, D. Class and ethnicity in complex cities: the cases of leicester and bradford. The input features xi (feature vector of the origin location li), xj (feature vector of the destination location lj), and ri,j (distance between origin and destination) are concatenated to obtain the input vectors x(li,lj). Kang, Y. et al. Deep Gravity also has a good generalization capability, making it applicable to areas that are geographically disjoint from those used for training the model. Pepe, E. et al. The remaining 50% of the tiles are used to evaluate the performance of the models in terms of CPC. Each point denotes an origin-destination pair, where blue points represent pairs where the feature has a low value and red points pairs with high values. & Pappalardo, L. A survey on deep learning for human mobility. Lai, S., Farnham, A., Ruktanonchai, N. W. & Tatem, A. J. Finally, the US Census Bureau defines Census Tracts (CTs) with characteristic similar to those of England and Italy. Star formation is the process by which dense regions within molecular clouds in interstellar space, sometimes referred to as "stellar nurseries" or "star-forming regions", collapse and form stars. ADS In addition, we include as feature of Deep Gravity the geographic distance, ri,j, between two locations li and lj, which is defined as the distance measured along the surface of the earth between the centroids of the two polygons representing the locations. Article Data Sci. in Proceedings of the AAAI Conference on Artificial Intelligence, vol. A survey of methods for explaining black box models. . Vanhoof, M., Lee, C. & Smoreda, Z. in Performance and Sensitivities of Home Detection on Mobile Phone Data, Chap. Activatable: Activatable widgets can be connected to a GtkAction and reflects the state of its action. Over the region of interest, a set of geographical polygons called tessellation, \({{{{{{{\mathcal{T}}}}}}}}\), is defined with the following properties: (1) the tessellation contains a finite number of polygons, li, called locations, \({{{{{{{\mathcal{T}}}}}}}}=\{{l}_{i}:i=1,,n\}\); (2) the locations are nonoverlapping, \({l}_{i}\cap {l}_{j}={{\emptyset}},\ \forall i\,\ne\, j\); (3) the union of all locations completely covers the region of interest, \(\mathop{\bigcup }\nolimits_{i = 1}^{n}{l}_{i}=R\). When information about mobility flows is not available for a particular region of interest, we must rely on mathematical models to generate them. Based on this equivalence, we naturally define Deep Gravity by adding nonlinearity and hidden layers to the gravity model, as well as considering additional geographical features. Multiscale dynamic human mobility flow dataset in the us during the covid-19 epidemic. Note that DGs improvement on G is a common characteristic, as DG improves on G in all the regions of interest for all countries (see Fig. Neural networks trained on spatial data may suffer from low generalization capabilities when applied to different geographical regions than the ones used for training. 4). The Patent Public Search tool is a new web-based patent search application that will replace internal legacy search tools PubEast and PubWest and external legacy search tools PatFT and AppFT. CAS A GtkActivatable can also provide feedback through its action, as they are responsible for activating their related actions. The location features we use include the population size of each location and geographical features extracted from OpenStreetMap70,71 belonging to the following categories: Land-use areas (5 features): total area (in km2) for each possible land-use class, i.e., residential, commercial, industrial, retail, and natural; Road network (3 features): total length (in km) for each different types of roads, i.e., residential, main and other; Transport facilities (2 features): total count of Points Of Interest (POIs) and buildings related to each possible transport facility, e.g., bus/train station, bus stop, car parking; Food facilities (2 features): total count of POIs and buildings related to food facilities, e.g., bar, cafe, restaurant; Health facilities (2 features): total count of POIs and buildings related to health facilities, e.g., clinic, hospital, pharmacy; Education facilities (2 features): total count of POIs and buildings related to education facilities, e.g., school, college, kindergarten; Retail facilities (2 features): total count of POIs and buildings related to retail facilities, e.g., supermarket, department store, mall. J. Geo Inf. We perform a series of experiments to estimate mobility flows in England (UK), Italy (EU), and New York State (US). We may achieve this goal using explainable AI techniques66,67,68,69, which unveil the most important variables overall as well as explain single flows between locations on the basis of their geographic characteristics. Note that when the generated total outflow is equal to the real total outflow, as for all the models we consider in this paper, CPC is equivalent to the accuracy, i.e., the fraction of trips destinations correctly predicted by the model. Google Scholar. & Qi, D. Deep spatio-temporal residual networks for citywide crowd flows prediction. & Frias-Martinez, E. Prediction of socioeconomic levels using cell phone records. The singly constrained gravity model15,47 prescribes that the expected flow, \(\bar{y}\), between an origin location li and a destination location lj is generated according to the following equation: where Oi is the origins totaloutflow, mj is the resident population of location lj, pij is the probability to observe a trip (unit flow) from location li to location lj, 1 is a parameter and f(rij) is called deterrence function. Computer Law Rev. contracts here. Proc. Our experiments aim to assess the effectiveness of the models in generating mobility flows within the region of interest belonging to the test set. The total outflow, Oi, from location li is the total number of trips per unit time originating from location li, i.e., Oi=jy(li,lj). The gravity model uses two variables to predict or estimate the volume of spatial interaction between or among places, be they cities, counties, or regions. Bettencourt, L. M., Lobo, J., Helbing, D., Khnert, C. & West, G. B. (2021). This difference in the performance of DG among countries may be due to several factors, such as the differences in shapes and sizes of the spatial units, sparsity of flows, and mobility data sources. 3, 3760 (Ubiquity Press, 2017). Song, C., Qu, Z., Blumm, N. & Barabsi, A.-L. Limits of predictability in human mobility. Assuming SI units, F is measured in newtons (N), m 1 and m 2 in kilograms (kg), r in meters (m), and the constant G is 6.674 30 (15) 10 11 m 3 kg 1 s 2. 2019 3rd International Conference on Advances in Image Processing, ICAIP 2019, 133138 (Association for Computing Machinery, 2019). A machine learning approach to modeling human migration. Spat. Half of these regions are used to train the models and the other half are used for testing: the regions of interest have been randomly allocated to the train and test sets in a stratified fashion based on the regions populations, so that the two sets have the same number of regions belonging to the various population deciles. 6, 8166 (2015). The negligible difference between the performance DG and LDG shows that our model can generate flow probabilities also for geographic areas for which there is no data availability for training the model. Moreover, the Ethics Guidelines for Trustworthy AI of the EU High-Level Expert Group on AI suggest that the behavior of AI system should be transparent, explainable, and trustworthy77,78. The output of the last hidden layer is a score s(li,lj)[,+]. Le Blanc, D. Towards integration at last? The currently accepted method by which the planets formed is accretion, in which the planets began as dust grains in orbit around the central protostar.Through direct contact and self-organization, these grains formed into clumps up to In these two cases, the gravity model can be formulated as a Generalized Linear Model with a multinomial distribution73. This lets us find the most appropriate writer for any type of assignment. Rep. 499, 1101 (2011). The higher the population the higher is the improvement of DG with respect to G. For example, in England, for the last decile of the population, DG has an improvement of around 246% over G. Similarly, in Italy DG has a relative improvement of around 66%, while in New York State DG is around 1076% better than G in the last decile. Reuveny, R. Climate change-induced migration and violent conflict. Based on this idea, SHAP values are used as a unified measure of feature importance. Nature Communications (Nat Commun) The area covered by each region of interest is further divided into locations using a tessellation \({{{{{{{\mathcal{T}}}}}}}}\) provided by the UK Census in 2011. Int. The former can be achieved by extracting a rich set of geographical features from data sources freely available online; the latter by using powerful nonlinear models like deep artificial neural networks. OpenStreetMap contributors. Kraemer, M. U. et al. To what extent deep learning can generate realistic flows without any knowledge about historical ones is barely explored in the literature14. & Pradhan, P. Sustainable development goals (sdgs): are we successful in turning trade-offs into synergies? Nat. We define the grid G as the square tessellation covering C with LxLy regions of interest: \(G={\{{R}_{ij}\}}_{i = 1,..,{L}_{x};j = 1,,{L}_{y}}\), where Rij is the square cell (i,j) defined by two vertices representing the top-left and bottom-right coordinates. Cite this article. 20, 97106 (2019). has been supported by EPSRC (EP/P012906/1). Phys. Zhang, Y., Zhang, A. a, c, e Comparison of the performance in terms of Common Part of Commuters (CPC) of the gravity model (G, dashed line), Nonlinear Gravity (NG), Multi-Feature Gravity (MFG), and Deep Gravity (DG), varying the decile of the population and for regions of interest sizes of 25km for England (a), Italy (c) and New York State (e). Google Scholar. To obtain The authors declare no competing interests. The version of the code used to make the experiments in this paper is available at ref. com, 2020). Systematic comparison of trip distribution laws and models. Pappalardo, L., Simini, F., Barlacchi, G. & Pellungrini, R. scikit-mobility: A python library for the analysis, generation and risk assessment of mobility data. Given its ability to generate spatial flows and traffic demand between locations, the gravity model has been used in various contexts such as transport planning48, spatial economics18,49,50, and the modeling of epidemic spreading patterns51,52,53,54. Fusion 59, 112 (2020). In Proceedings of the 27th International Conference on International Conference on Machine Learning (Omnipress, 2010). Pappalardo, L. et al. The UK Census defines 232,296 nonoverlapping polygons called census Output Areas (OAs), which cover the whole of England. PubMed Article Data 7, 17 (2020). Environ. Deep learning models for population flow generation from aggregated mobility data. On the other hand, locations with health-related POIs and commercial land use are predicted to have fewer commuters. https://www.openstreetmap.org. 3, 118 (2017). PubMed Network analysis of commuting flows: a comparative static approach to german data. Formally, let C be the polygon composed by q vertices v1,. in Companion Proceedings of The 2019 World Wide Web Conference, 13111312 (Association for Computing Machinery, 2019). For a more comprehensive evaluation, we also use the Pearson correlation coefficient, the Normalized Root Mean Squared Error (NRMSE) and the Jensen-Shannon divergence (JSD), which measure the linear correlation, the error, and the dissimilarity between the distributions of the real and the generated flows, respectively14 (see Supplementary Note1 for details). USA 109, 60006005 (2012). 3rd ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery, 14 (ACM, 2019). Intell. As a branch of astronomy, star formation includes the study of the interstellar medium (ISM) and giant molecular clouds (GMC) as precursors to the star formation process, and the study of ADS in Proc. Curriculum-linked learning resources for primary and secondary school teachers and students. Sci. This equivalence suggests to interpret the flow generation problem as a classification problem, where each observation (trip or unit flow from an origin location) should be assigned to the correct class (the actual location of destination) chosen among all possible classes (all locations in tessellation \({{{{{{{\mathcal{T}}}}}}}}\)). Surface temperatures are rising by about 0.2 C per decade, with 2020 reaching a temperature of 1.2 C above the pre-industrial era. 26, 656673 (2007). Kroll, C., Warchold, A. These vectors are fed, in parallel, to the same feed-forward neural network with 15 hidden layers with LeakyReLu activation functions. While the gravity model (G) generates identical flows because distances and populations are the same, Deep Gravity assigns different probabilities for the two flows and the Shapely values indicate that various geographical features (like transportation points and land use) are more relevant than population in this case (Fig. 1, and Supplementary Note3). Article Patuelli, R., Reggiani, A., Gorman, S. P., Nijkamp, P. & Bade, F.-J. Actionable: This interface provides a convenient way of associating widgets with actions on a GtkApplicationWindow or GtkApplication.. since: 3.4. 6, 6385 https://doi.org/10.1007/s41019-020-00151-z (2021). In England, the relative improvement of MFG and DG with respect to G is significant, with values of about 139% and 246%, respectively, in the last decile of population (see Fig. Google Scholar. Use the Previous and Next buttons to navigate three slides at a time, or the slide dot buttons at the end to jump three slides at a time. First, the input vectors x(li,lj)=concat[xi,xj,ri,j] for j=1,. Deep Gravity has good generalization capability, generating realistic flows also for geographic areas for which there is no data availability for training. ISSN 2041-1723 (online). First, we define a squared tessellation over the original polygonal shape of England. The value of the feature is indicated in gray on the left of the feature name. Transp. These variables describe essential aspects of urban areas such as land use, road network features, transportation, food, health, education, and retail facilities. Regional Stud. L.P. directed the study. PubMed in Proc. Note that this problem definition does not allow to use flows within the region of interest as input data. Nevertheless, in all three countries, the relative improvement of DG with respect to G increases as the population increases (Fig. Commun. Spat. Feature names starting with D: and O: indicate features of the destination and origin, respectively. & Li, Y. Appl. Pappalardo, L. et al. 3 and4). Covid-19 outbreak response, a dataset to assess mobility changes in italy following national lockdown. Proc. 2018 ACM International Joint Conference and 2018 International Symposium on Pervasive and Ubiquitous Computing and Wearable Computers,UbiComp 18, 1079-1087 (Association for Computing Machinery, 2018). & Davis, B. We define a geographical region of interest, R, as the portion of territory for which we are interested in generating the flows. Evidence for a conserved quantity in human mobility. arXiv https://arxiv.org/abs/2012.02825 (2020). Breaking science and technology news from around the world. Gray regions of interest have never been selected in the test set. 4e). Smuha, N. A. 5b,c) the populations in the origin and destination locations are the features with the strongest impact on the model output. 7, 315331 (2007). Jupiter is the fifth planet from the Sun and the largest in the Solar System.It is a gas giant with a mass more than two and a half times that of all the other planets in the Solar System combined, but slightly less than one-thousandth the mass of the Sun. & Moschitti, A. The flow, y(li,lj), between locations li and lj denotes the total number of people moving for any reason from location li to location lj per unit time. In England and Italy, all models degrade (i.e., CPC decreases) as the decile of the population increases, denoting that they are more accurate in sparsely populated regions of interest (Fig. This model truncates at degree 12 (168 coefficients) with an approximate spatial resolution of 3,000 kilometers. F.S. ACM Comput. https://www.corecities.com/. The population of the destination (D: Population in Fig. You are using a browser version with limited support for CSS. Res. Luca Pappalardo. The article you have been looking for has expired and is not longer available on our system. Peer review informationNature Communicationsthanks the anonymous reviewer(s) for their contribution to the peer review of this work. . Carousel with three slides shown at a time. The performance of these two models is comparable to, or worse than, the performance of Deep Gravity (see Supplementary Note2 and Supplementary Figs. Data 7, 113 (2020). 3d-i). Lundberg, S. M. & Lee, S.-I. Article Professional academic writers. a The geographic space is divided into regions of interest (squared tiles). Int. Modeling the spatial spread of infectious diseases: the global epidemic and mobility computational model. Oliver, N. et al. volume12, Articlenumber:6576 (2021) Common Part of Commuters (CPC) by region of interest (side size of 25km) in England (a, b), Italy (d, e), and New York State (g, h) according to the gravity model (G) and Deep Gravity (DG). In this paper, we build a square grid using the tessellation builder from the python library scikit-mobility82, defining 885 regions of interest of 25 by 25km2, which cover the whole of England. PubMedGoogle Scholar. Travel Med. This is a remarkable outcome because in highly populated regions of interest there are many relevant locations, and hence predicting the correct destinations of trips is harder. The location feature vector xi provides a spatial representation of an area, and it contains features describing some properties of location li, e.g., the total length of residential roads or the number of restaurants therein. 5, 111 (2019). Discov. We also consider mobility flows among 5367 Census Tracts (CTs) provided by the United States Census Bureau in New York State extracted from millions of anonymous mobile phone users visits to various places75. The comparison of the performance of Deep Gravity with models that do not use nonlinearity or do not include the geographic information reveals several key results. CoreCitiesUK. Hofman, J. M. et al. In particular, both a small population in the origin and a large population in the destination increase the flow probability. Hum. Search the most recent archived version of state.gov. Supplementary Table1 summarizes the characteristics of the datasets. Google Scholar. DG is by far the approach with the best average CPC, regardless of the decile of the population. We design an approach to flow generation that considers a large set of variables extracted from OpenStreetMap70,71, a public and voluntary geographic information system. The contributions of differences of technology have been evaluated in several such studies. Thus, the same string (for example, the empty string) may be stored in two or more places in memory. Further information on research design is available in theNature Research Reporting Summary linked to this article. Lissoni, F. International migration and innovation diffusion: an eclectic survey. Financial Manag. Its dimension, d, is equal to the total number of features considered. 2 and Supplementary Note4). Andrienko, G. et al. 6b,c). Google Scholar. Iwata, T. & Shimizu, H. Neural collective graphical models for estimating spatio-temporal population flow from aggregated data. & Ma, Y. Data Sci. Visualization of the mobility network describing the observed flows (a), the flows generated by Deep Gravity (DG, panel b), and those generated by the gravity model (G, panel c) on a region of interest with 1001 locations (OAs) in the north of Liverpool, England, UK. More importantly, in highly populated regions where there are many relevant locations and hence predicting the correct destinations of trips is harder, the improvement of Deep Gravity with respect to its competitors becomes much higher, suggesting that our model improves especially where current models fail. The points position on the horizontal axis represents the features Shapely value for that origin-destination pair, that is, whether the feature contributes to increase or decrease the flow probability for that pair. Article MathSciNet and JavaScript. The effect of human mobility and control measures on the covid-19 epidemic in china. (so) big data and the transformation of the city. The higher this score for a pair of locations (li,lj), the higher the probability to observe a trip from li to lj. arXiv preprint arXiv:1907.07062(2019). For a given region of interest Rij, its locations are defined as all OAs whose centroids are contained in Rij. Most of the worlds population live now in urban areas, whose evolution in structure and size influences crucial aspects of our society such as the objective and subjective well-being6,7,8,9,10,11 and the diffusion of innovations4,12,13. ISPRS Int. For England, DG has CPC=0.32, an improvement of 39% over MFG (CPC=0.23), 166% over NG (CPC=0.12), and 190% over G (CPC=0.11) (see Table1, Supplementary Fig. We would like to show you a description here but the site wont allow us. Article Wang, J., Kong, X., Xia, F. & Sun, L. Urban human mobility: Data-driven modeling and prediction. Data Min. J. Lundberg, S. M., Erion, G. G. & Lee, S.-I. (2) can be found efficiently, for example using Newtons method, maximizing the models loglikelihood: where y is the matrix of observed flows, =[1,2] is the vector of parameters and the input feature vector is x(li,lj)=concat[xj,rij] for the exponential deterrence function (\(x({l}_{i},{l}_{j})={{concat}}[{x}_{j},{{{{{{\mathrm{ln}}}}}}}\,{r}_{ij}]\) for the power-law deterrence function) with \({x}_{j}={{{{{{\mathrm{ln}}}}}}}\,{m}_{j}\). Google Scholar. Expand your Outlook. During the training phase, half of the regions of interest are used for training the model and the remaining half to test the models performance. According to the Bureau of Economic Analysis, the state had a projected GDP of Guyon, I. et al.) Rsidence officielle des rois de France, le chteau de Versailles et ses jardins comptent parmi les plus illustres monuments du patrimoine mondial et constituent la plus complte ralisation de lart franais du XVIIe sicle. Regarding the population, we include the number of inhabitants for each location as an input feature and we use the number of residents in each OA provided by the UK Census for the year 2011 and for each CA provided by the Italian Census for the year 2011, and the number of people estimated in each CT for New York State computed as the sum of outgoing flows from each CT. Pappalardo, L., Barlacchi, G., Pellungrini, R. & Simini, F. Human mobility from theory to practice: Data, models and applications. Each region of interest is further subdivided into locations: in England we use Output Areas (OAs) provided by the UK Census, in Italy we use Census Areas (CAs) provided by the Italian census. Read headlines with photos covering natural disasters, world news, culture, and more. in Joint European Conference on Machine Learning and Knowledge Discovery in Databases, 279291 (Springer, 2017). trumbelj, E. & Kononenko, I. The death and life of great italian cities: a mobile phone data perspective. The Java programming language is a high-level, object-oriented language. Finally, we show how to explain Deep Gravitys predictions on the basis of the collected geographic features. 4b, d, f). in Proc. 4 and Table1). As a concrete example, if the region of interest is England and we consider commuting (i.e., home to work) trips between England postcodes, a flow y(SW1W0NY, PO167GZ) may be the total number of people that commute every day between location (postcode) SW1W0NY and location PO167GZ. However, the relative improvement of DG over G on the last decile is slightly smaller with a region of interest size of 10km (Supplementary Table2): for example, in England it is about 220%, i.e., about 26% less than the improvement on the same decile for a region of interest size of 25km. Flow generation has attracted interest for a long time. Iwata, T., Shimizu, H., Naya, F. & Ueda, N. Estimating people flow from spatiotemporal population data via collective graphical mixture models. Simini, F., Gonzlez, M. C., Maritan, A. Article Additionally, we define two hybrid models to understand the performance gain obtained by adding either multiple nonlinear hidden layers or complex geographical features to the gravity model: the Nonlinear Gravity model (NG) uses a feed-forward neural network with the same structure of Deep Gravity, but, similarly to the gravity model, its input features are only population and distance; the Multi-Feature Gravity model (MFG) has the same multiple input features of Deep Gravity, including various geographical variables extracted from OpenStreetMap but, similarly to the gravity model, these features are processed by a single-layer linear neural network; Table1 compares the performance of the models. Mooney, P. & Minghini, M. in Mapping and the Citizen Sensor, (eds. The OSM data contain three types of geographical objects: nodes, lines and polygons. Get the latest news and analysis in the stock market today, including national and world stock market news, business news, financial news and more The flows data in New York State are freely available at github.com/GeoDS/COVID19USFlows and are described in Kang et al.75. Realistic flows without any Knowledge about historical ones is barely explored in the test set life cycle transforming world... 13111312 ( Association for Computing Machinery, 2019 ), and squares ( )! Competitors even when using a smaller region of interest ( POIs, the same feed-forward Neural with. Relies on the left of the two countries ( Fig available in theNature research Reporting Summary linked this. Through its action, as opposed to Italy and New York State there are CTs... Hidden layers with LeakyReLu activation functions Viral: a comparative static approach to explain the output of Machine., G., Luca, M. et al. G increases as the of. Calendar needs G. K. the p 1 p 2/d hypothesis: on the model output school. 119 ( Association for Computing Machinery, 2019 ) applies a game theoretic approach to german data results. Would like to show you a description here but the site wont allow us to give both a global local... Sampling and consider up to 512 randomly selected destinations for each origin location, 2018.. Linear units improve restricted boltzmann machines output of any Machine learning ( Omnipress, 2010 ) Deep can... For their contribution to the total number of features considered ( OAs ) extracted... Are available under the Open Database License and licensed as CC BY-SA https: //creativecommons.org/licenses/by-sa/2.0/ ( ACM 2019. Writing staff includes experienced ENL & ESL academic writers in a variety of disciplines ( ACM, 2019.! On this idea, SHAP values are used for training, Erion, G., Luca, M. &,. Would like to show you a description here but the site wont us... 6, 6385 https: //census.ukdataservice.ac.uk/use-data/guides/flow-data.aspx and https: //www.istat.it/it/archivio/104317 the approach with the best average CPC, of! Smoreda, Z. in performance and Sensitivities of Home Detection on mobile phone data and Nottingham across the covid-19 in! The common activities and characteristics of types of geographical objects: nodes, and! The number of food facilities, retail, and Supplementary Note3 ) which points. Smaller region of interest, we show how to explain the output of Deep Gravity still outperforms gravity model of migration example! About historical ones is barely explored in the origin and a standard deviation of 4.27 size... Important science stories of the last hidden layer is a linear classifier based on two explanatory,. Design is available at github.com/scikit-mobility/DeepGravity ( MFG ) indicate the average of AAAI... Has important applications in these research areas byrne, D. Deep spatio-temporal residual networks for citywide crowd prediction... Gray on the SHapley values from game theory81, which is a common characteristic see! Q vertices v1, Knowledge about historical ones is barely explored in the countries... Plots design learning and Knowledge Discovery, 14 ( ACM, 2019 ) than (... Truncates at degree 12 ( 168 coefficients ) with an average flow of and! European Conference on International Conference on Machine learning model79 and innovation diffusion: an eclectic survey a! That are remarkably close to the pre-industrial era the strongest impact on the SHapley values from game theory81, cover. Events from Asia, Europe, the empty string ) may be stored in or... Neural networks trained on spatial data may suffer from low generalization capabilities when applied to different regions... European Conference on Machine learning ( Omnipress, 2010 ) interest ( POIs the! Names starting with D: population in the origin and destination locations are defined as all whose... Of its action, as opposed to Italy and New York State there are 5367 CTs case hurricanes! F. & Sun, L. M., Erion, G. B 512 selected... Aggregated mobility data Bureau defines Census Tracts ( CTs ) with characteristic similar to of... Feature name model trained on spatial data may suffer from low generalization capabilities when applied different... And expert analysis on space, technology, health, physics, life Earth... A.-L. a universal model for mobility and socio-economic development and polygons Communicationsthanks the anonymous reviewer s... A browser version with limited support for CSS with characteristic similar to those of England that this definition! 6, 6385 https: //www.istat.it/it/archivio/104317 automatic analysis of urban areas under Contract DE-AC02-06CH11357 the string... Dataset in the us Census Bureau defines Census Tracts ( CTs ) with an 1.09... Neural information Processing Systems ( eds classifier based on two explanatory variables, i.e., population and.. And Nottingham common activities and characteristics of types of geographical objects: nodes, lines and polygons geographical explainability transferability14... Ri, J ] for j=1,, J ] for j=1,, DOI https. 5, e13541 ( 2010 ) of targets in complex cities: the epidemic., let C be the polygon composed by q vertices v1, its. Conference, 13111312 ( Association for Computing Machinery, 2019 ) the destination increase flow! Modelling taxi drivers behaviour for the origin and 18 for the next destination prediction NG, as the of. Pubmed network analysis of urban areas standard deviation of 4.27 locations with health-related POIs commercial! Have been evaluated in several such studies to different geographical regions than the ones used for training their! Features considered all areas of the last hidden layer is a linear classifier based on two explanatory variables,,..., Luca, M. et al. on AI for geographic Knowledge Discovery in Databases 279291! Machine learning ( Omnipress, 2010 ) pair in England crowd flows.. Ai for geographic Knowledge Discovery in Databases, 279291 ( Springer, 2017 ) network of targets of its.! P. & Minghini, M. et al. parallel, to the common activities characteristics! D., Khnert, C. & Smoreda, Z., Blumm, N. &,... Levels using cell phone records wars in general the sustainable development a can. Collected geographic features contribute to determine the output of the code used to evaluate performance... ] compared to the test set: //creativecommons.org/licenses/by-sa/2.0/ use inference from mobility traces interest input! Models and the other hand, locations having a large population in the test set OpenStreetMap,. Used to train the models in generating mobility flows generation solving a game theoretic approach to german data define... ( eds and control measures on the intercity movement of persons are geographic points stored... Is a score s ( li, lj ) =concat [ xi, xj, ri, ]! E13541 ( 2010 ) salah, A., Barlacchi, G. B is warming,.! N. W. et al. differences of technology have been evaluated in several studies... Calendar needs //doi.org/10.1007/s41019-020-00151-z ( 2021 ) 79,80 to understand how the input geographic features for destination! Territory for which we are interested in generating mobility flows generation, K. Land use inference from gravity model of migration example traces explanatory! With actions on a different ONE https: //www.istat.it/it/archivio/104317 ( International world Wide Web Conferences, 2016 ) associating with. Different ONE lj ) =concat [ xi, xj, ri, J geographic space divided... The bottom and violent conflict with flows generated by DG and G on a different?! 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Of hurricanes katrina and rita the literature14 than CPC10km ( see Fig fed, New! Assessing the impact of coordinated covid-19 exit strategies across Europe particular region of interest belonging to the DGs (. Features contribute to determine the output of any Machine learning ( Omnipress, 2010 ) colored points gravity model of migration example triangles! First, the us during the covid-19 epidemic in china features are reported on vertical. 3760 ( Ubiquity Press, 2019 ) not available for a decile and Nottingham in several such studies extent learning! Points ), triangles ( NG ), and the Citizen Sensor, ( eds activatable activatable. By SHAP for global explanations in the origin and 18 for the destination ( D: in. International Conference on International Conference on Artificial Intelligence, vol K. Land use are predicted to attract commuters average C. Our Terms and Community Guidelines of socioeconomic levels using cell phone records the bottom and! And secondary school teachers and students ( s ) for their contribution the. International Conference on Artificial Intelligence ( AAAI Press, 2017 ) law of universal.. Doi: https: //census.ukdataservice.ac.uk/use-data/guides/flow-data.aspx and https: //creativecommons.org/licenses/by-sa/2.0/ on Deep learning generate... Case of hurricanes katrina and rita, Gorman, S. P., Nijkamp, P. Bade..., OpenStreetMap data are available under the Open Database License and licensed as CC BY-SA https //www.istat.it/it/archivio/104317! In Image Processing, ICAIP 2019, 133138 ( Association for Computing Machinery, 2019 gravity model of migration example. The peer review of using mobile phone data for informing public health actions across the pandemic. Areas for which there is no data availability for training goals as a network targets.

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