Studying Human Mobility through Photograph Geo-Tagging - Research Paper Example

Running head: STUDYING HUMAN MOBILITY THROUGH PHOTOGRAPH GEO-TAGGING Studying Human Mobility through Photograph Geo-Tagging Abstract Introduction Background Geolocation technologies have improved vastly and have developed so much that locating a person anywhere on earth has become a very simple job. The development of wireless networks and mobile technology aids to the development of these technologies considerably. Global Positioning system (GPS) is one of the important promoting systems of geotechnologies, both on the science and legal perspective. With 24 satellites and aided with two carrier signals, it can transmit signals accurately. However, to operate within buildings, it requires at least 4 of its satellites to have clear access to that specified area (Djuknic, & Richton, 2001). To overcome such a difficulty, assisted-GPS technology has come into limelight. It provides more accurate detail even from within buildings and also associated with it is a superior quality (Djuknic, & Richton, 2001). Georeferencing is one of the developing technologies, especially geotagging. An analysis on the research related to this field focuses on four major points: 1. Organization of georeferenced media sources 2. Data mining of organized knowledge 3. Identifying the world’s landmarks and 4. Identification of the photo location, geographically(Zheng et. al, 2011) The organized knowledge used for online data mining has two parts. 1. The knowledge stored in database is actually a collection of data provided by the society itself, obtained from several social websites. 2. The stored knowledge and corresponding mining provides multi-modal information about the stored data. (Zheng et. al, 2011) There are several methods that are used as sources for collecting information that is geotagged. Collective geotagging is one of the important methods where publicly available information uploaded by the users are geotagged and collected. Here the term, “geotagged” means that some kind of location metadata is added to the object, say a photo. Some of the important information stored in the metadata includes latitude information and place names. This process can be done either by hardware method or software method. Hardware geotagging is done through the installed cameras in mobile phones which on clicking of a photo add that information to the picture. In terms of software, which is the most prevalent method of geotagging, several photo sharing sites like Flickr and Picasso add such information through map tools. Although they are less in accuracy, the provide results with maximum relevance. (Zheng et. al, 2011) Sharing of images in online communities is an upward trend. Especially for people who post their travelogues in blogs and communities with lots of pictures of their trip. Hao. Et. al (2009) created a process which picks up the keywords from the description, identifies the pictures associated with those words and prepared a document with the results of those pictures to create an automated travelogue. Georeferencing has improved on the above process by one step and provides a more coherent result. With many people uploading photos in public and social sites, travel and tracking human mobility has been given a fresh look. Human mobility Georeferencing is considered to be the best thing to have happened for people involved in human mobility studies and for tourists. (Girardian & Blat, 2008) conducted a research to indicate that the pictures and other such information presented by the people voluntarily can be used to identify human mobility, especially that of tourists. The first step in understanding the mobility of humans by the authors was to make use of the large amount of data available in the online community. After conducting detailed studies in several tourist parts like Rome, Barcelona and Italy, they were able to collect detailed information about urban tourism. (Girardian & Blat, 2008) The figure shown below shows heat maps which indicate the presence of tourists in Italy along with their georeferenced photos. Image source: Girardian & Blat, 2008 The below given table shows the number of photos taken over a period of 2 years from 2005 to2007 Region Number of photos Number of photographers Barcelona 154,106 5818 Province of Florence 81,017 4280 Rome 144,501 6018 Table source: Girardian & Blat, 2008 Based on the above collected data, peoples’ movement and location details were garnered. In addition, tourism relevant statistics were also obtained. It allowed for identification of new information for the officials of the tourism department of the respective countries. It also allows for an opportunity to create image sequences which just allow people to identify paths they have taken through their trip. (Girardian & Blat, 2008) Applications of image sequence geo-location Option to tag images allowing for an opportunity to do a virtual travel Ability to auto-tag images Making use of geotags available wherever possible Increase in research related to mobility of human movement Increase in availability of information about places for tourist planners (Kalogerakis et. al., 2009). Footprinting Digital footprints are available in mammoth quantities and hence their cost is also invariably low. With the advent of new tools and technologies that use these foot prints, there has been a concise development in identifying people who come as tourists to a place as well as to study about people movement and travel. The digital footprints, in general can be classified into two categories. They are, Active tracks: These are footprints that are provided by the user itself through activities like taking pictures and sharing them. Passive tracks: These are footprints that are generated in log of devices like mobile phones whenever people use them (Girardin et al., 2008). To analyze more on usage of footprints, two types of data sources are considered. 1. Public Website Flickr which has photos uploaded by the users. 2. Information from logs generated by the usage of mobile phones with the help of Telecom Italia System (TIM). (Girardin et al., 2008). The first one comes under the category of ‘explicit sharing’ because people share pictures intentionally. The second one is ‘implicit sharing’ information where the information by default gets stored in the mobile phone usage log (Girardin et al., 2008). An example of it could be identification of the country of the user based on the location details present in the number they use. One method of using these footprints is spatial distribution where each cell consists of information like number of calls, photos etc. One implied usage of digital footprints is determining the desire lines (Girardin et al., 2008) of a person by examining the photos taken by him/her, aggregating them and arranging them through time/space values and determining the paths he had taken. The data collected by the above mentioned methods and stored in the special matrix allow for a detailed analysis of people mobility. There are no proven results yet but the technology provides a lot of scope for fine tuning it. Some of the problems associated with this data collection are the possibilities of wrong entry of information. For example, there is no chance for determining whether the user had set correct time in mobile when taking the photo because there is equal possibility that he may have set it wrong. Also, there are ethical issues associated with this too. But, the generated systems have some methods associated with them that completely erase any individual information present in the log of the mobile phones (Girardin et al., 2008). Geotagging Use Mapping One of the primary uses of geotagging is that it allows for generation of maps based on the photos. According to Crandall et.al (2009), a structure is designed based on the photos taken by people and based on that structure the associated content is analyzed to predict the possible sequencing of the photos by arranging them in the order of time and location and thus resulting in the generation of a map of the trip taken by the people. Software Using Geotagging PhotoCompas The current existing approaches are more or less not capable of satisfying the current demands of an ever changing world. The current techniques however, are classified into three categories. They are: 1. Availability of tools that help in annotation. However, the demands and complexities associated with performing annotation has made this process as one of the most rarely used ones among people. 2. Presence of tools that help in manipulating images at a fast pace. Some of the operations that are possible through this type of scanning are zooming and panning of images. 3. Final category is considered to be a future prospect as no tools are yet to achieve the desired results here. This category contains tools that work based on content but they are yet to produce a practical usability with respect to managing pictures. (Naaman et. al, 2004). One of the tools that have been devised for analyzing geo-reference photographs is a browser called as PhotoCompas. The specialty of this tool is that it allows for clustering of photos based on the two important criterions – time and location. (Naaman et. al, 2004). The below given diagram present a list of Categories for metadata of image collection. It is actually a screen shot of the opening screen of PhotoCompas (Naaman et. al, 2004). Image source: Naaman et. al, 2004 These given metadata categories can be used to retrieve images by the method of annotation. For example, geographical based news reports as well as time based news reports can be retrieved from any kind of data repository which contains global information through this method and tool. Photomap Photomap is another such tool that allows for obtaining the location of the user through their collections of photo. Based on the photo taken, the software provides a map based view of where the photo was taken and based on which one can easily identify the routines that were followed by the owner of the photo during that trip. (Viana et al., 2007) Cybercasing Information that are available online and that come under the category of geo-tagging can be used for hacking into the privacy of several entities in the net. This procedure is termed as cybercasing. When an individual goes onto share his or her location with people in net, they are completely unaware of the implication that sharing may cause for their data. With advent of techniques and tools that help in identifying geo-tagged information, the factor of complexity associated with it has been completely eradicated. (Friedland & Sommer, 2010) Sites like Facebook, YouTube etc. allow for information to be available to anyone instantly, irrespective of their security features. With advent of other social networking sites like twitter and LinkedIn, there is every possibility for a person to get hold of the required information about another person, thanks to the above said absence of thinking from the information provider. So, it has come down to again to the hands of technology to develop geo-location in such a way that such kind of easy access to information is prevented. (Friedland & Sommer, 2010) As mentioned above, development in geo-tagging has been stepped up at an extraordinary level with several internet giants like Google and Yahoo investing an humongous amount of time and capital to offer services that allow for identification of location and time based on images. Even outside the world of internet, mobile makers and other modern device developers have started providing apps that determine the location of the device owner and send it across continuously. However with the rules of privacy and ethical concerns taking over, these organization have thrown the control to the user in determining whether they need location tracking and if required, to whom the information to be sent. One primary example that could exemplify the development of Geo-tagging is the development of Apple’s 3G devices which can locate the information as exactly as a postal address as soon as the camera is turned on. Of course, they have also made sure to provide options that could nullify storing such information in the clicked photos. So, it could be said that publishing of location information of personnel is no more a problem, in fact not even an issue. (Friedland & Sommer, 2010) But, prohibiting publication of such information would not eradicate the in-hand problem by any means. It is because there have been three parallel development, that have made this into an even bigger threat. They are 1. Globalization of information: The presence of public websites that allow for sharing of videos and pictures has brought in a major change where even a minute amount of information could allow a person to hack into the privacy of others. 2. The presence of location finders like GPS, Google Earth etc have allowed hackers to easily streamline to the data they require without much of a fuss. 3. Additional location services like Street view of Google. (Friedland & Sommer, 2010) Some other applications that provide location services include, Foursquare – a tool that allows people to check their current location and Yowza – an Iphone app that helps to identify product offers available for a person in his present location. Additionally, in order to showcase the extent to which geo-tagged information is available publicly, we can examine the Craiglist website. (Friedland & Sommer, 2010) One form of uploading images in Craiglist is through HTML link. Images uploaded by this would still have their geo information present in them. Since Craiglist being one of the biggest sites of human resource information, there is every possibility that everyone’s geo tagged information would be publicly available for taking. (Friedland & Sommer, 2010) Securing the privacy of Location To secure, it is important for application developers to put users in the driver’s seat. They should ultimately decide whether the image should contain location information or not. From the developer’s perspective, if the user is ignorant about location information, they should ensure that appropriate protection measures are given. The above mentioned Apple 3G device has taken the right step forward by installing a user priority interface but they still lack in two aspects. First, the pictures taken by the phone are not under this priority rule and secondly, it is not applicable to all applications. (Friedland & Sommer, 2010) Another step that can be taken to solve such location compromises is to impose a rule on all sites to indicate to the user that their uploaded pictures and videos have location information in them and also provide them with the option to remove it, if required. Conclusion With the above mentioned applications combined with the development of geo-tagging technology, it has become almost a simple process to do cybercasing. It is important that community realizes that this technology, in its initial stages of development, can still be formatted and directed in its approach towards creating a secure environment. An appropriate balance needs to be attained between its benefits and disadvantages in order to actualize its true potential. References Girardian, F. & Blat, J. (2008). Assessing pervasive user-generated content to describe Tourist dynamics Retrieved from: http://www.girardin.org/fabien/publications/girardin_trends_giscience_2008.pdf Naaman, M., Harada, S., Wang, Q., Garcia-Molina, H., & Paepcke, A. (2004). Context data in geo-referenced digital photo collections. Paper presented at the Proceedings of the 12th annual ACM international conference on Multimedia. Friedland, G., & Sommer, R. (2010). Cybercasing the joint: on the privacy implications of geotagging. Paper presented at the Proceedings of the 5th USENIX conference on Hot topics in security. Girardin, F. et al. (2008). “Digital Footprinting: Uncovering Tourists with User-Generated Content.” Pervasive Computing, IEEE 7.4. pp36-43. Kalogerakis, E. et. al. (2009). Image Sequence Geolocation with Human Travel Priors.ICCV 2009 Djuknic, G. M., & Richton, R. E. (2001). Geolocation and Assisted GPS. Computer, 34(2), 123-125. Zheng, Y.-T., Zha, Z.-J., & Chua, T.-S. (2011). Research and applications on georeferenced multimedia: a survey. Multimedia Tools and Applications, 51(1), 77-98. Crandall, D. J., Backstrom, L., Huttenlocher, D., & Kleinberg, J. (2009). Mapping the world's photos. Paper presented at the Proceedings of the 18th international conference on World wide web. Viana, W., et al. (2007). PhotoMap - automatic spatiotemporal annotation for mobile photos. Paper presented at the Proceedings of the 7th international conference on Web and wireless geographical information systems. Hao Q, Cai R, Wang X-J, Yang J-M, Pang Y, Zhang L (2009) Generating location overviews with images and tags by mining user-generated travelogues. In: Proceedings of the seventeen ACM international conference on multimedia. ACM, New York, pp 801–804 Read More