Analysis of Flickr, Snapchat, and Twitter use for the modeling of visitor activity in Florida State Parks

Spatio-temporal information attached to social media posts allows analysts to study human activity and travel behavior. This study analyzes contribution patterns to the Flickr, Snapchat, and Twitter platforms in over 100 state parks in Central and Northern Florida. The first part of the study correlates monthly visitor count data with the number of Flickr images, snaps, or tweets, contributed within the park areas. It provides insight into the suitability of these different social media platforms to be used as a proxy for the prediction of visitor numbers in state parks. The second part of the study analyzes the spatial distribution of social media contributions within state parks relative to different types of points of interest that are present in a state park. It examines and compares the location preferences between users from the three different platforms and therefore can draw a picture about the topical focus of each platform

Comparing the Spatial and Temporal Activity Patterns between Snapchat, Twitter and Flickr in Florida

Social media services generate enormous amounts of spatiotemporal data that can be used to characterize and analyse user activities and social behaviour. Although crowdsourced data have the advantage of comprehensive spatial and temporal coverage compared to data collected in more traditional ways, the various social media platforms target different user groups, which leads to user selection bias. Since data from social media platforms are used for a variety of geospatial applications, understanding such differences and their implications for analysis results is important for geoscientists. Therefore, this research analyses differences in spatial and temporal contribution patterns to three online platforms, namely Flickr, Twitter and Snapchat, over a six-week period in Florida. For the comparison of spatial contribution patterns, a set of negative binomial regression models are estimated to identify which socio-economic factors and characteristics of the built and natural environments are associated with contribution activities. The contribution differences observed are discussed in light of the targeted user groups and different purposes of the three platforms

Spatial and Temporal Analysis of Location and Usage of Public Electric Vehicle Charging Infrastructure in the United States

Switching to electric vehicles (EVs) has increased rapidly over recent years. This paradigm change provides an important pillar in the United States transport sector to reach sustainability goals. EVs rely on a network of charging locations to operate. This study analyses the spatial distribution, accessibility and usage patterns of the public EV infrastructure in the US. First, using a negative binomial regression model, the influence of socio-economic and other factors on the abundance of EV charging locations in a state is investigated. Second, analysis of the network’s use and of service areas generated around charging locations provides insight into the accessibility of these stations to populations living in urban and rural areas. Third, the study compares publicly available datasets on the EV charging infrastructure provided by different companies in the Miami urbanized area, and lastly, it analyses real-time data from the SemaConnect charging network. Results indicate increased access of residents to the EV charging infrastructure over the years. Economic activity, highway density and political preference were statistically associated with the number of charging stations. Charging behaviour was found to follow the patterns of a regular workday, indicating that EV owners rely primarily on the public infrastructure as opposed to charging their vehicles only at home

User Interface Design for Semantic Query Expansion in Geodata Repositories

Semantic query expansion is the process of supplementing a user query with additional terms that interpret and extend the user\u27s information needs. This work presents the results of an empirical study that investigates user preferences for different designs of user interfaces that provide semantic query expansion for data search from geo-data repositories. The study assesses further whether it is possible to map qualitative gradations of semantic relatedness between geographic key terms to ranges of numerical similarity values

Web Based Bicycle Trip Planning for Broward County, Florida

To promote the use of bicycle transportation mode in times of increasing urban traffic congestion, Broward County Metropolitan Planning Organization funded the development of a Web-based trip planner for cyclists. This presentation demonstrates the integration of the ArcGIS Server 9.3 environment with the ArcGIS JavaScript Extension for Google Maps API and the Google Local Search Control for Maps API. This allows the use of Google mashup GIS functionality, i.e., Google local search for selection of trip start, trip destination, and intermediate waypoints, and the integration of Google Maps base layers. The ArcGIS Network Analyst extension is used for the route search, where algorithms for fastest, safest, simplest, most scenic, and shortest routes are imbedded. This presentation also describes how attributes of the underlying network sources have been combined to facilitate the search for optimized routes

Identification of Transit Service Gaps through Accessibility and Social Vulnerability Mapping in Miami-Dade County

Inadequate provision of public transportation services can lead to mobility-related social exclusion for disadvantaged population groups (e.g., lower-income families, the elderly), and limited accessibility to jobs, healthy food, and recreational as well as social activities. The aim of this study is to identify areas in Miami-Dade County, Florida, where disadvantaged populations lack transit-based access to these opportunities, and where transit service improvement could benefit these groups especially. This involves developing a transit-based accessibility index which uses timetable data from three public transit agencies. It also entails devising a vulnerability index based on a combination of socioeconomic variables to identify disadvantaged population groups with regards to mobility. Both indices can be combined into a service provision score which quantifies the presence of populations in need of transit service improvements. Results show that the combination of the different index maps and the application of Hotspot analysis can help to identify areas requiring transit service improvement in order to achieve accessibility equity. The analysis and interpretation of accessibility maps and selected demographic layers, such as percentage of households without vehicle, facilitates the identification of areas with above-average rates of users who rely on public transportation

ChatGPT as a mapping assistant: A novel method to enrich maps with generative AI and content derived from street-level photographs

This paper explores the concept of leveraging generative AI as a mapping assistant for enhancing the efficiency of collaborative mapping. We present results of an experiment that combines multiple sources of volunteered geographic information (VGI) and large language models (LLMs). Three analysts described the content of crowdsourced Mapillary street-level photographs taken along roads in a small test area in Miami, Florida. GPT-3.5-turbo was instructed to suggest the most appropriate tagging for each road in OpenStreetMap (OSM). The study also explores the utilization of BLIP-2, a state-of-the-art multimodal pre-training method as an artificial analyst of street-level photographs in addition to human analysts. Results demonstrate two ways to effectively increase the accuracy of mapping suggestions without modifying the underlying AI models: by (1) providing a more detailed description of source photographs, and (2) combining prompt engineering with additional context (e.g. location and objects detected along a road). The first approach increases the suggestion accuracy by up to 29%, and the second one by up to 20%.Comment: Submitted to The Fourth Spatial Data Science Symposiu

Cartographic Vandalism in the Era of Location-Based Games—The Case of OpenStreetMap and Pokémon GO

User-generated map data is increasingly used by the technology industry for background mapping, navigation and beyond. An example is the integration of OpenStreetMap (OSM) data in widely-used smartphone and web applications, such as Pokémon GO (PGO), a popular augmented reality smartphone game. As a result of OSM’s increased popularity, the worldwide audience that uses OSM through external applications is directly exposed to malicious edits which represent cartographic vandalism. Multiple reports of obscene and anti-semitic vandalism in OSM have surfaced in popular media over the years. These negative news related to cartographic vandalism undermine the credibility of collaboratively generated maps. Similarly, commercial map providers (e.g., Google Maps and Waze) are also prone to carto-vandalism through their crowdsourcing mechanism that they may use to keep their map products up-to-date. Using PGO as an example, this research analyzes harmful edits in OSM that originate from PGO players. More specifically, this paper analyzes the spatial, temporal and semantic characteristics of PGO carto-vandalism and discusses how the mapping community handles it. Our findings indicate that most harmful edits are quickly discovered and that the community becomes faster at detecting and fixing these harmful edits over time. Gaming related carto-vandalism in OSM was found to be a short-term, sporadic activity by individuals, whereas the task of fixing vandalism is persistently pursued by a dedicated user group within the OSM community. The characteristics of carto-vandalism identified in this research can be used to improve vandalism detection systems in the future

Relative occurrence of the family Kalotermitidae (Isoptera) under different termite sampling methods

The termite family Kalotermitidae constitutes a wood-nesting termite family that accounts for about 15% of all extant termite species. In recent decades, field studies have been carried out to assess termite diversity in various wooded habitats and geographic locations. Three sampling methods have been favored expert, transect, and alate light-trap surveys. Expert collecting is not spatially quantifiable but relies on field personnel to recognize and sample termite niches. The transect method aims to standardize and quantify termite abundance and diversity. Light trapping is a passive method for sampling nocturnal alate flights. We compared our expert survey results and results of published sampling methods for their proportional yields of kalotermitid versus non-kalotermitid encounters. Using an odds ratio statistic, we found that worldwide, there is about a 50.6-fold greater likelihood of encountering a kalotermitid sample versus a non-kalotermitid using the expert survey method and a 15.3-fold greater likelihood using alate trapping than using the transect method. There is about a 3.3 -fold greater likelihood of collecting a kalotermitid specimen versus a non-kalotermitid sample using the expert survey method than using the alate trap method. Transect studies in which only termite species diversity was reported gave similar low Kalotermitidae yields. We propose that multiple biases in sampling methodology include tools, time constraints, habitat type, geographical location, topographical conditions, and human traits account for the divergent outcomes in sampling the abundance and diversity of Kalotermitidae compared to other termite families