A pet owner has invented a tracking device for animals — after trying to discover where his own wandering moggy kept going.

Puss in routes ... Yollo and gizmo

SWNS

Clever Dave Evans, 41, fitted his ultra-light GPS gadget to Yollo’s collar when the tabby mysteriously put on weight after regularly sneaking out.

Dave uploaded the gizmo’s information when the cat returned and plotted his every move on Google Maps — discovering that the puss was chasing chickens.

The dad of two said: “My cat was getting fat even though I was feeding him less and I needed to know what was going on.

“Now I know he travels a couple of miles a day, where he goes at night and who’s feeding him. I know he also enjoys chasing chickens at 6am every morning and can see his favourite hang-outs.”

Dave, of Marlborough, Wilts — who has a background in satnav marketing — has called his 15g waterproof catnav gadget “G-Paws”.

He is now putting others on sale at £50 each — to be used on both cats and dogs.

The inventor is also developing a social networking site for pet owners to accompany G-Paws, which will be available in the summer. 

Cat who got the scene ... Dave Evans

SWNS

He added: “It will enable users to see where their pets have been as well as share photos, videos and other information.

“For too long we have been kept in the dark about the secret lives of our pets. At last we will know what they get up to.”

Yang Xu, Shih-Lung Shaw, Jiaoli Chen, Qingquan Li, Zhixiang Fang, and Yuguang Li

“Global Positioning System (GPS) based vehicle tracking data have been used to derive useful traffic data such as computing travel speed or congestion level (e.g., Herrera et al., 2010; Mohan, 2008) or measuring urban dynamics (e.g., Calabrese et al., 2011; Reades et al., 2007). Vehicle tracking data also have been used to analyze travel activities (e.g., Li et al., 2011; Liu et al., 2010). This study, on the other hand, focuses on identifying repeated spatio-temporal patterns embedded in a GPS-based taxi tracking dataset collected in Wuhan, China. Although taxi trajectories may appear to be chaotic at first glance, there could be important repeated spatio-temporal patterns embedded in taxi tracking data. For example, certain taxi drivers may have preferences of waiting for passengers at particular locations such as airports, train stations, etc. In many developing countries, it also is common to have two work shifts of drivers for one taxi. Identifying repeated spatio-temporal patterns embedded in vehicle tracking data thus can shed light on important travel and activity behavioral patterns.

Spatial distribution of repeatedly visited road segments by frequency.

“This study uses a taxi tracking dataset collected in Wuhan, China as a case study to identify repeated spatio-temporal behavioral patterns among the taxi drivers. The main objective of this study is to develop a systematic method that can facilitate uncovering repeated behavioral patterns in a large tracking dataset. This method can be adapted for studies using other types of tracking data such as cell phone tracking data of individual trajectories or online tracking data of individual web browsing histories.”

• Read the paper [PDF]