Featured on the international art festival SACO 6
This project explored the possibility of carthographying a digital place using for this purpose a dating app for the gay community called Grindr, because its users tend to create virtual communities based on geographical location. invisible landscapes is a project carried out for the semana del arte contemporáneo de antofagasta, chile. consists of 6 sublimation-printed lines on which a radial diagram is expressed, the diagrams will be accompanied by digital information accessed by scanning a QR code.
“Every night, on a certain corner of an unknown busy street a constellation of men sat. As we approached them the car we traveling in accelerated, leaving us only with a fading glimpse of their gaze; someone asked: what’s going on there? Another one muttered: It’s a gay space.
How do we think about a place through sexuality? Is it possible to construct historiographies from demographic analysis?
In its most basic geographic sense, space is understood as a the process of localizing events, places, people, and phenomena using cartographic tools (Gregory, 2009) Nigel Thrift argues that “space is under continual construction as the result of the interactions of things (…) ” (2009:86) space is nothing more than a flux of identities in constant mutation, by mapping it we are constructing it (Massey, 2005) and by constructing it we are recognizing it.
Technological advances such as the GPS, available in practically all mobile devices, allows any individual to map and navigate any space, first digitally and then organically. This democratization in the use of measurement instruments (GPS) allows the popularization of apps like GRINDR where users share their geographic location to communicate with other members based on their proximity. Users can scan through different geographical locations to determine spaces of higher engagement, creating with it digital cartography of their community and negotiating a common space.
A previous experience has taken place in Caracas/ Venezuela, where we scanned and mapped five aleatory zones using GRINDR to determine where there was the highest number of users under 100 m. The data was used to created radial graphs and maps that accounted for the particular characteristics of each zone.