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Drone technology opens the door to major changes and opportunities in our society. But this technology, like many others, needs to be administered and regulated to prevent potential harm to the public. Therefore, national and local governments around the world established regulations for operating drones, which bans drone use from specific locations or limits their operation to qualified drone pilots only. This study reviews the types of restrictions on drone use that are specified in federal drone regulations for the US, the UK, and France, and in state regulations for the US. The study also maps restricted areas and assesses compliance with these regulations by analyzing the spatial contribution patterns to three crowd-sourced drone portals, namely SkyPixel, Flickr, and DroneSpot, relative to restricted areas. The analysis is performed both at the national level and at the state/regional level within each of the three countries, where statistical tests are conducted to compare compliance rates between the three drone portals. This study provides new insight into drone users' awareness of and compliance with drone regulations. This can help governments to tailor information campaigns for increased awareness of drone regulations among drone users and to determine where increased control and enforcement of drone regulations is necessary. [ABSTRACT FROM AUTHOR]

This document is an editorial from the journal "Geographies" titled "Perspectives on Advanced Technologies in Spatial Data Collection and Analysis." The editorial discusses the rapid changes that have occurred in the field of geographical information science and systems in recent decades. It highlights various advanced technologies that have contributed to these changes, such as volunteered geographic information (VGI), wearable devices, social media and networking apps, GIS cloud computing, mobile networks and communication techniques, GeoAI, and blockchain. The editorial also mentions the collection of new types of spatial and spatiotemporal data and invites contributions on topics including geospatial open-source software, big data analysis, GeoAI, and geovisual analytics. The authors acknowledge the authors of the papers in the special issue, the anonymous referees, and the editorial staff for their contributions. [Extracted from the article]

Comparison of routes recommended by online trip planners, such as Google Maps and MapQuest, with routes traveled by cyclists allows estimation of whether the routing criteria used in such trip planners are the same as those used by cyclists. This study uses GPS tracking data from the Endomondo fitness tracker app from trips in Miami-Dade (Florida) and North Holland and compares their characteristics to cycling routes suggested by Google Maps, MapQuest and the shortest path. Results highlight which trip attributes differ between Endomondo and its alternative trips, and how consistent those differences are across the two study areas. For example, Endomondo cyclists in both regions go through fewer traffic signals, and use more cycleways, footways, or bike lanes than suggested routes from Google and MapQuest. A multinomial logit model on observed Endomondo trips provides insight into the routing preferences of Endomondo commute and sport cyclists in both study regions. [ABSTRACT FROM AUTHOR]

Data from GPS-based fitness tracker apps have become a prominent source for studying cycling behavior. This type of crowd-sourced data provides larger datasets than were previously attainable by travel surveys and cyclist counts, which allows for the comparison of trip characteristics between geographic regions and the study of temporal trends in bicycle ridership. Researchers acknowledge that different types of biases come with tracking data from fitness tracker apps, such as self-selection bias, which is partially based on different target audiences among fitness tracker apps. This begs the question as to whether the behavior of cyclists, and thus the characteristics of routes traveled, varies among the apps. To provide a first insight into this question, this research analyzes trips reported on three fitness tracker apps, Bikemap, Endomondo, and MapMyFitness, for South Florida (Miami-Dade, Broward, and Palm Beach counties) and North Holland. Comparison of trip characteristics is made among the three apps and across both study regions. Results show that cycling behavior observed in the three apps is similar relative to a set of control trips in each region (e.g. fewer primary roads than reference trips observed), but that there are some pronounced differences in trips recorded with the different apps between both regions. This suggests that geographic region plays a role in how trip characteristics recorded on different apps compare to each other, demonstrating the presence of an additional aspect of geographic bias in crowd-sourced cycling data. [ABSTRACT FROM AUTHOR]