Characterizing geographical preferences of international tourists and the local influential factors in China using geo-tagged photos on social media

Understanding the geographical preferences of international tourists is critical for the tourism planning and marketing. However, it is not an easy endeavor to gather the corresponding information, given the absence of city-level tourism statistical data and high costs of participant survey. This paper characterizes the geographical preferences of international tourists using geo-tagged photos on social media (the Flickr in particular). Data are harvested for 333 prefecture-level cities in China from 2008 to 2013, and the intensity of photo sharing (IPS: the ratio between total number of uploaded photos and total area of the city/region) is used an indicator of tourist geographical preferences. IPS visualization shows the geography that tourism hotpots generally concentrate in regional capital cities and economically developed megaregions. More specifically, the East Asia and Oceania tourists exhibit more preferences towards the eastern coastal cities, while the Europe and North America visitors show increasing interest in exploring the western and northern places. Spatial regression is employed to quantify the local influential factors of tourists’ geographical preferences. It is found that international tourists usually consider the local economy, accessibility, infrastructure and cultural attractions when they choose their destinations in China. East Asia and Oceania visitors particularly appreciate the local economy and cultural attractions. Europe and North America tourists especially value the cultural attractions and local openness. The demonstrated methodological framework is not restricted to Flickr data, and it can be applicable to social media offer geo-tagging service. This paper is therefore believed to advance the applications of social media into geographical research.     

Spatial crime distribution and prediction for sporting events using social media

ABSTRACT

Sporting events attract high volumes of people, which in turn leads to increased use of social media. In addition, research shows that sporting events may trigger violent behavior that can lead to crime. This study analyses the spatial relationships between crime occurrences, demographic, socio-economic and environmental variables, together with geo-located Twitter messages and their ‘violent’ subsets. The analysis compares basketball and hockey game days and non-game days. Moreover, this research aims to analyze crime prediction models using historical crime data as a basis and then introducing tweets and additional variables in their role as covariates of crime. First, this study investigates the spatial distribution of and correlation between crime and tweets during the same temporal periods. Feature selection models are applied in order to identify the best explanatory variables. Then, we apply localized kernel density estimation model for crime prediction during basketball and hockey games, and on non-game days. Findings from this study show that Twitter data, and a subset of violent tweets, are useful in building prediction models for the seven investigated crime types for home and away sporting events, and non-game days, with different levels of improvement.     

Finding spatial outliers in collective mobility patterns coupled with social ties

ABSTRACT

Currently the increase in the variety and volume of data sources is demanding new data analytical workflows for exploring them concurrently, especially if the goal is to detect spatial outliers. In this paper, we propose a data analytical workflow for exploring Call Detail Records in conjunction with geotagged tweets. The aim was to investigate how massive data point observations can be analyzed to detect spatial outliers in collective mobility patterns that are coupled with social ties. This workflow consists of analytical tasks that are developed based on the a-priori assumption of two isometric spaces where Natural Language Processing techniques are used to find spatial clusters from geotagged tweets in a Social Space which are later used to aggregate the Call Detail Records generated by antennas located in the Mobility Space. The dynamic weighted centroids that are given by the mean location of the number of calls per hour of all antennas that belong to a particular cluster are used to compute Standard Deviation Ellipses. The longer the period of time a weighted centroid stays outside of the 99.7% probability region of an ellipse, the highest the likelihood that they are spatial outliers. The workflow was implemented for the city of Dakar in Senegal. The results indicate that the further the hourly weighted centroids are skewed from the normal mean of an ellipse, the stronger the influence of a cluster is in finding spatial outliers. Furthermore, the longer the period of time the outliers stays outside of the 99.7% probability region of an ellipse, the highest the likelihood that the outliers are genuine and can be associated to extraordinary events such as natural disasters and national holidays.     

Remote sensing and social sensing for socioeconomic systems: A comparison study between nighttime lights and location-based social media at the 500 m spatial resolution

With the advent of “social sensing” in the Big Data era, location-based social media (LBSM) data are increasingly used to explore anthropogenic activities and their impacts on the environment. This study converts a typical kind of LBSM data, geo-tagged tweets, into raster images at the 500 m spatial resolution and compares them with the new generation nighttime lights (NTL) image products, the Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) monthly image composites. The results show that the monthly tweet images are significantly correlated with the VIIRS-DNB images at the pixel level. The tweet images have nearly the same ability on estimating electric power consumption and better performance on assessing personal incomes and population than the NTL images. Tweeted areas (i.e. the pixels with at least one posted tweet) are closer to satellite-derived built-up/urban areas than lit areas in NTL imagery, making tweet images an alternative to delimit extents of human activities. Moreover, the monthly tweet images do not show apparent seasonal changes, and the values of tweet images are more stable across different months than VIIRS-DNB monthly image composites. This study explores the potential of LBSM data at relatively fine spatiotemporal resolutions to estimate or map socioeconomic factors as an alternative to NTL images in the United States.