Abstract
Information concerning the home and workplace of residents is the basis of analyzing the urban job-housing spatial relationship. Traditional methods conduct time-consuming user surveys to obtain personal job and housing location information. Some new methods define rules to detect personal places based on human mobility data. However, because the travel patterns of residents are variable, simple rule-based methods are unable to generalize highly changing and complex travel modes. In this paper, we propose a visual analysis approach to assist the analyzer in inferring personal job and housing locations interactively based on public bicycle data. All users are first clustered to find potential commuting users. Then, several visual views are designed to find the key candidate stations for a specific user, and the visited temporal pattern of stations and the user’s hire behavior are analyzed, which helps with the inference of station semantic meanings. Finally, a number of users’ job and housing locations are detected by the analyzer and visualized. Our approach can manage the complex and diverse cycling habits of users. The effectiveness of the approach is shown through case studies based on a real-world public bicycle dataset.
Highlights
With the widespread availability of location-aware technologies, the acquisition of massive spatio-temporal trajectory data over a long time period has become possible
Traditional methods use questionnaire surveys [4,5] to obtain personal job and housing data; these surveys are time-consuming, and inaccurate when people move to a new location or change jobs
We present a visual analysis approach to explore the personal job and housing Ilnoctahtiiosnps abpaeser,dwoen pPrBeSsednataa
Summary
With the widespread availability of location-aware technologies, the acquisition of massive spatio-temporal trajectory data over a long time period has become possible. TThheuPsB, Sthwe aPsBwSiddealtya uisseudsebfyul for analycziitnizgenins dinivwidourkalajnodb laifned: ahpopurosxinimg aloteclayt3io0n%s.oEf fufsoerrtssihnacovrepboeraetneddePvBoStiendtotodauinlydceormstamnudtiinngg, tahneddaily routintPheBesSmodfoactsaittfiiszreeuqnsuesefaunntllfdyorucisatenydadlsyytzanitniaogmnisnicdwsievbriadesucealdol sjooesbnt aPtonBdeSihtdhoaeurtsaihn[og1m4lo–ec1(a74t]0i,o%nb)su.otErtfhwfoeortrusknh(da4ev0r%elyb) ie[n1egn3].sdtTeavhtouiotsen,d-thrteoelated semaunntidcemrsetaanndiinnggstrheemdaaiinlyurnoudteinteersmofinceitdiz.ens and city dynamics based on PBS data [14,15,16,17], but the Tuhnedeprrloyibnlgemstaotifofinn-rdeilnatgedjosbemanadnthicomuseiannginlogscarteimonasinbuanseddetoernmPinBeSdd. ata is interesting and challenging. It cannot bTheesoplrvoebdleamlgoofritfihnmdiincgalljoybwainthdouhtouhsuinmganlocwatiisodnosmb.asFeidrsto,nusPeBrSs cdaantaboisrrionwterbesiktiensg fraonmd any statiocnhaltleanngyintgim. (1) A new kind of trajectory data: PBS data is studied to infer personal job and housing locations, and a visual analysis approach is presented to process such data.
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