FEATURES OF OUR ALGORITHM

The algorithm powering the Wild Streets app will feature a selection of carefully considered indicators. We strive to present users everywhere with greenery relevant to their specific urban setting – mirroring factors such as local climate and challenges including high pollution levels or risk of flood and reveal site conditions such as underground utilities to help users select the right trees and plants for the right place. Our data will navigate users through the human and ecological benefits of their amazing designs.

Did we forget something? Have you got some data that would improve our algorithm and make the Wild Streets user experience even better? Get in touch!

LOCATION-SPECIFIC FLORA

Wild Streets’ data will be location specific meaning users will only be presented with plant and tree species that are scientifically proven to thrive in the given climate. This includes accounting for flora’s tolerance of local conditions such as shade, drought waterlogging, as well as indigenous and native species, and even what locally extinct flora could be safely reintroduced.

HUMAN BENEFITS OF FLORA

People care about their health and happiness, and greenery has a rudimentary part in this – on our physical health via lower pollution levels and on our mental health due to the beneficial impact on stress levels. Emerging research is successfully demonstrating these and other derived benefits such as increased productivity.

UNDERGROUND UTILITIES

Planting a tree depends on more than just the soil you plant it in. Digging a little deeper may reveal electric and fiber optic cables, drainage pipework, water, gas, fuel and heating pipes, or something as big as a tube system. Un-informed excavation can cause serious damage to underground utilities and can be an enormous safety hazard, and knowing their location is therefore essential for planners.

ECOSYSTEM SERVICES

While many city dwellers appreciate green spaces, there is still little understanding of the social-ecological benefits that trees and plants provide such as flora’s ability to purify polluted air by absorbing CO2, its function as a natural filter for polluted water, its ability to cool down our warming cities by shading and evopotranspiring, and the creation of shelter and food for local wildlife and pollinators.

ENVIRONMENTAL CHALLENGES

Every city, neighborhood and street face a different set of challenges. These may change with the magnitude of people and cars, with climate, or external events. Never before have we been able to produce such detailed data, much of it in real-time, on pollution, temperatures, and rainfall. Powered by a ‘challenge database’ Wild Streets will enable truly sustainable urban greening. 

STREET FIXTURES

Greenery and street fixtures are closely related design parameters for the urban space. Preferences to both are likely to vary across demographic groups and life situations; i.e., while public benches may be generally desired, their placement in areas with an older population may differ to other areas where they serve a more recreational purpose.

TREE INVENTORY

As with anything really, the urban forest is better when diverse. It reduces the risk of tree diseases and offers a large palette of ecosystem services. Our algorithm will take the existing population of trees into account in order to encourage diversity and avoid over planting a single species or cultivar. This is key to the design of a green city that works to reduce any site-conditioned challenges.

SOIL

One of the biggest challenges facing urban greening projects is the vast differences in soil composition and properties across even small distances, determining what greenery can be planted where. Knowing what’s under the city helps planners prioritize and lead to more sustainable solutions.

Confidence Index

While more ground than ever is covered on the Wild Streets indicators in cities across the world much is still to be done. In respect of expectation management, the app will provide a transparent picture of the data availability for any given location and offer a confidence index for the suitability of the tree and plant selection made available to the users.