September 2014: Local Coffee Houses in Chicago

Cynthia VanDeMark’s map, created in GEO 441 (GIS for Community Development), uses data collected from Yelp.com to show locations of 115 “local” coffee locations and 123 Dunkin’ Donut locations. Local coffee houses are defined as those that are unique to their area and not corporate chains such as Starbucks or Dunkin’ Donuts. The kernel density map on the left shows that local coffee houses are spatially concentrated in the north, near west, and downtown areas of Chicago. The second map shows proposed sites for new local coffee house locations defined as tracts that have high population density (10k per square mile) in proximity (a mile) to a CTA rail train but don’t have existing local coffee houses. A recent report from the US Bureau of Labor (2014) predicts that service and management, arts, science, and business jobs are going to increase in the next 10 years while production jobs are going to decrease. Communities might consider how local coffee houses help create community, as local coffee houses may serve as a “third place” (first is home and second is work) more often than fast food locations such as Dunkin’ Donuts, that could facilitate advancements in social networking and career changes residents are aiming to achieve.https://via.library.depaul.edu/mom/1015/thumbnail.jp

Segmenting human trajectory data by movement states while addressing signal loss and signal noise

This paper considers the problem of partitioning an individual GPStrajectory data into homogeneous, meaningful segments such asstops and trips. Signal loss and signal noise are highly prevalent inhuman trajectory data, and it is challenging to deal with uncertaintiesin segmentation algorithms. We propose a new trajectorysegmentation algorithm that detects stop segments in a noiserobustmanner from GPS data with time gaps. The algorithm consistsof three steps that impute time gaps, split data into basesegments and estimate states over a base segment. The statedependentpath interpolation was proposed as a framework forgap imputation to deal with locational and temporal uncertaintiesassociated with signal loss. A spatiotemporal clustering-based trajectorysegmentation was proposed to detect spatiotemporal clustersof any shape regardless of density to cut a trajectory intointernally similar base segments. Fuzzy inference was employed todeal with borderline cases in determining states over base segmentsbased on input features. The proposed algorithm wasapplied to detect stop/move episodes from raw GPS trajectoriesthat were collected from 20 urban and 19 suburban participants.Sensitivity analysis was conducted to guide the choice of parameterssuch as the temporal and spatial definitions of a stop.Experimentation results show that the proposed method correctlyidentified 92% of stop/move episodes, and correctly estimated 98%of episode duration. This study indicates that a sequence of statedependentgap imputation, clustering-based data segmentationand fuzzy-set-based state estimation can satisfactorily deal withuncertainty in processing human GPS trajectory data