Methodological Development for Identifying Foraging Behaviors from GPS Data Among Artisanal Fishers in the Caribbean

This project addresses questions about human foraging behavior in the ethnographic context of small-scale fishing-foraging in the Commonwealth of Dominica, an island in the Eastern Caribbean. The first goal of this project is to further develop a method of inferring foraging behaviors from GPS data by testing a recent partial sum approach—the CUMSUM method. The principle underpinning this method of research is that remotely gathered movement data can be accurately translated into meaningful data on foraging activities. GPS data produces movement tracks that are used to parse out changes in behavior, but segmentation of GPS tracks into different bouts of foraging activities is not straightforward. Previous research demonstrates that the CUMSUM method has benefits for detecting behavioral shifts and identifying patches of resources behaviorally, but it has seen limited testing across different foraging contexts. Developing this method has broad application across a range of disciplines, and one relevant utility is using CUMSUM segments to test foraging models. A second goal of this project is to demonstrate by testing a prediction of the marginal value theorem. The MVT explores generalized decision-making rules on patch residence time and was primarily developed in experimental settings with non-human animals. There are few tests of the MVT among human populations in naturalistic settings. Research activities took place across three field sessions in the rural village of Desa Ikan, Dominica, among artisanal fisher-foragers. I tested the CUMSUM method with fishing data and found the method correctly identifies about 90% of patches with relatively small error rates. The strength of this approach is using both directly observed behavioral data to ground-truth simultaneously collected GPS data. I tested an aspect of the MVT using patch data from both observational data and CUMSUM segment data. Observational data supports the theoretical prediction that fishers spend more time in patches with higher travel costs, while support from CUMSUM model-generated data is equivocal

Extracting Semantics of Individual Places from Movement Data by Analyzing Temporal Patterns of Visits

Data reflecting movements of people, such as GPS or GSM tracks, can be a source of information about mobility behaviors and activities of people. Such information is required for various kinds of spatial planning in the public and business sectors. Movement data by themselves are semantically poor. Meaningful information can be derived by means of interactive visual analysis performed by a human expert; however, this is only possible for data about a small number of people. We suggest an approach that allows scaling to large datasets reflecting movements of numerous people. It includes extracting stops, clustering them for identifying personal places of interest (POIs), and creating temporal signatures of the POIs characterizing the temporal distribution of the stops with respect to the daily and weekly time cycles and the time line. The analyst can give meanings to selected POIs based on their temporal signatures (i.e., classify them as home, work, etc.), and then POIs with similar signatures can be classified automatically. We demonstrate the possibilities for interactive visual semantic analysis by example of GSM, GPS, and Twitter data. GPS data allow inferring richer semantic information, but temporal signatures alone may be insufficient for interpreting short stops. Twitter data are similar to GSM data but additionally contain message texts, which can help in place interpretation. We plan to develop an intelligent system that learns how to classify personal places and trips while a human analyst visually analyzes and semantically annotates selected subsets of movement data

Locational wireless and social media-based surveillance

The number of smartphones and tablets as well as the volume of traffic generated by these devices has been growing constantly over the past decade and this growth is predicted to continue at an increasing rate over the next five years. Numerous native features built into contemporary smart devices enable highly accurate digital fingerprinting techniques. Furthermore, software developers have been taking advantage of locational capabilities of these devices by building applications and social media services that enable convenient sharing of information tied to geographical locations. Mass online sharing resulted in a large volume of locational and personal data being publicly available for extraction. A number of researchers have used this opportunity to design and build tools for a variety of uses – both respectable and nefarious. Furthermore, due to the peculiarities of the IEEE 802.11 specification, wireless-enabled smart devices disclose a number of attributes, which can be observed via passive monitoring. These attributes coupled with the information that can be extracted using social media APIs present an opportunity for research into locational surveillance, device fingerprinting and device user identification techniques. This paper presents an in-progress research study and details the findings to date

PinMe: Tracking a Smartphone User around the World

With the pervasive use of smartphones that sense, collect, and process valuable information about the environment, ensuring location privacy has become one of the most important concerns in the modern age. A few recent research studies discuss the feasibility of processing data gathered by a smartphone to locate the phone's owner, even when the user does not intend to share his location information, e.g., when the Global Positioning System (GPS) is off. Previous research efforts rely on at least one of the two following fundamental requirements, which significantly limit the ability of the adversary: (i) the attacker must accurately know either the user's initial location or the set of routes through which the user travels and/or (ii) the attacker must measure a set of features, e.g., the device's acceleration, for potential routes in advance and construct a training dataset. In this paper, we demonstrate that neither of the above-mentioned requirements is essential for compromising the user's location privacy. We describe PinMe, a novel user-location mechanism that exploits non-sensory/sensory data stored on the smartphone, e.g., the environment's air pressure, along with publicly-available auxiliary information, e.g., elevation maps, to estimate the user's location when all location services, e.g., GPS, are turned off.Comment: This is the preprint version: the paper has been published in IEEE Trans. Multi-Scale Computing Systems, DOI: 0.1109/TMSCS.2017.275146

Using accelerometer, high sample rate GPS and magnetometer data to develop a cattle movement and behaviour model

The study described in this paper developed a model of animal movement, which explicitly recognised each individual as the central unit of measure. The model was developed by learning from a real dataset that measured and calculated, for individual cows in a herd, their linear and angular positions and directional and angular speeds. Two learning algorithms were implemented: a Hidden Markov model (HMM) and a long-term prediction algorithm. It is shown that a HMM can be used to describe the animal's movement and state transition behaviour within several “stay” areas where cows remained for long periods. Model parameters were estimated for hidden behaviour states such as relocating, foraging and bedding. For cows’ movement between the “stay” areas a long-term prediction algorithm was implemented. By combining these two algorithms it was possible to develop a successful model, which achieved similar results to the animal behaviour data collected. This modelling methodology could easily be applied to interactions of other animal specie

Methodological Development for Identifying Foraging Behaviors from GPS Data Among Artisanal Fishers in the Caribbean

This project addresses questions about human foraging behavior in the ethnographic context of small-scale fishing-foraging in the Commonwealth of Dominica, an island in the Eastern Caribbean. The first goal of this project is to further develop a method of inferring foraging behaviors from GPS data by testing a recent partial sum approach—the CUMSUM method. The principle underpinning this method of research is that remotely gathered movement data can be accurately translated into meaningful data on foraging activities. GPS data produces movement tracks that are used to parse out changes in behavior, but segmentation of GPS tracks into different bouts of foraging activities is not straightforward. Previous research demonstrates that the CUMSUM method has benefits for detecting behavioral shifts and identifying patches of resources behaviorally, but it has seen limited testing across different foraging contexts. Developing this method has broad application across a range of disciplines, and one relevant utility is using CUMSUM segments to test foraging models. A second goal of this project is to demonstrate by testing a prediction of the marginal value theorem. The MVT explores generalized decision-making rules on patch residence time and was primarily developed in experimental settings with non-human animals. There are few tests of the MVT among human populations in naturalistic settings. Research activities took place across three field sessions in the rural village of Desa Ikan, Dominica, among artisanal fisher-foragers. I tested the CUMSUM method with fishing data and found the method correctly identifies about 90% of patches with relatively small error rates. The strength of this approach is using both directly observed behavioral data to ground-truth simultaneously collected GPS data. I tested an aspect of the MVT using patch data from both observational data and CUMSUM segment data. Observational data supports the theoretical prediction that fishers spend more time in patches with higher travel costs, while support from CUMSUM model-generated data is equivocal

A Smartphone-Based System for Outdoor Data Gathering Using a Wireless Beacon Network and GPS Data: From Cyber Spaces to Senseable Spaces

Information and Communication Technologies (ICTs) and mobile devices are deeply influencing all facets of life, directly affecting the way people experience space and time. ICTs are also tools for supporting urban development, and they have also been adopted as equipment for furnishing public spaces. Hence, ICTs have created a new paradigm of hybrid space that can be defined as Senseable Spaces. Even if there are relevant cases where the adoption of ICT has made the use of public open spaces more “smart”, the interrelation and the recognition of added value need to be further developed. This is one of the motivations for the research presented in this paper. The main goal of the work reported here is the deployment of a system composed of three different connected elements (a real-world infrastructure, a data gathering system, and a data processing and analysis platform) for analysis of human behavior in the open space of Cardeto Park, in Ancona, Italy. For this purpose, and because of the complexity of this task, several actions have been carried out: the deployment of a complete real-world infrastructure in Cardeto Park, the implementation of an ad-hoc smartphone application for the gathering of participants’ data, and the development of a data pre-processing and analysis system for dealing with all the gathered data. A detailed description of these three aspects and the way in which they are connected to create a unique system is the main focus of this paper.This work has been supported by the Cost Action TU1306, called CYBERPARKS: Fostering knowledge about the relationship between Information and Communication Technologies and Public Spaces supported by strategies to improve their use and attractiveness, the Spanish Ministry of Economy and Competitiveness under the ESPHIA project (ref. TIN2014-56042-JIN) and the TARSIUS project (ref. TIN2015-71564-C4-4-R), and the Basque Country Department of Education under the BLUE project (ref. PI-2016-0010). The authors would also like to thank the staff of UbiSive s.r.l. for the support in developing the application