What does legalising cannabis do to a city?

A cool person in Colorado doing something cool. Image: Getty.

It’s 4/20, a.k.a. National Weed Day: the day that a heady smog rises above every capital city, and hard currencies are replaced by fistfuls of crushed Doritos. In anticipation of 4/20, states in Australia and the United States have blazed up early, by announcing their plans to (partially) legalise cannabis.

Such decisions are made at national and state level. But, while advocates used to cite data collected from cannabis convivial countries like the Netherlands and Uruguay, a move towards legalisation in many U.S. states has lead to a spate of research at city level.

So, with this in mind, what impact does legalising cannabis have on a city and its infrastructure?

Economic benefits and drawbacks

Established weed welcomers have been long been aware of the economic benefits of legalisation: in the Netherlands, tax on coffee shops alone nets the government over €400m per annum. This is despite efforts by city councils to curtail the number of people who can buy and smoke cannabis.

Since Colorado legalised cannabis in November 2012, the state capital Denver has seen a “gold rush” of tourists, investment and new residents. A recent report from the Drug Policy Alliance found that the opening of just two dispensaries in Denver created 280 jobs and an economic output of $30m in the first half of 2014. There has also been an impact on the city’s housing market, with rent prices increasing by 9.6 per cent in 2014 and real estate prices rose by 10 per cent.

That said, these numbers are only impressive if a city actually wants drugs tourists and half its workforce priced out of the housing market.

And even though the sale of cannabis has benefited the Dutch economy, in October 2011 the border-city of Maastricht started banning foreigners from buying and smoking it. City authorities declared that drugs tourism was causing major traffic problems and disrupting residents’ ability to use the city. More recently Amsterdam, has started closing coffee shops in an attempt to make its central tourist district a bit more classy (elitist) and less sketchy (fun).


Less petty crime, more serious crime

Colorado legalised cannabis in 2012. Two years later, arrests for possession were down by 95 per cent in comparison to 2010. (You can still be arrested for carrying more than one ounce at a time.)

In theory, fewer arrests means less police time spent harassing teenagers suffering from pink eye. That in turn means fewer tax dollars spent on processing (in New York City the average possession charge costs $1000-$2000); fewer non-violent, first time offenders in prison; and an economy that benefits from not having a large proportion of its potential work force behind bars.

This theory holds true for cities that have legalised cannabis in the last five years. But! There has been a slight increase in serious crime. Not enough for residents to retreat into gated communities and start hoarding Fray Bentos pies; just enough for anti-legalisation advocates to start getting twitchy.

In 2015 burglaries at Denver cannabis businesses made up 2.5 per cent of attempted robberies in the city. And local police report that the number of “marijuana related crimes” are on the up – although there’s a gaping chasm of information about how these crimes were “related” to cannabis).

It is(n’t) easy being green

By now, it’s hopefully clear to everyone that people who illegally grow cannabis are basically the Hufflepuffs of crime. But, apparently, smoking something grown in weird Barry’s asbestos-ridden attic isn’t always 100 per cent safe. Legalisation means regulation – and while there’s something rather endearing about the idea of furtive farmers taking over an old Debenhams building, the potential for large electrical fires isn’t quite as cute.

In built up areas there is a real danger that herb happy Hufflepuffs might accidentally endanger hundreds of residents. But even if a city does decide to eliminate this risk, the issue of energy consumption remains. Cannabis cultivation uses a massive amount of water and energy, something that Californian residents are starting to notice is taking a toll.

Water use by cannabis farms is already impacting some city residents’ water supply. Increased consumption will place greater pressure on politicians to consider the environmental impact of legalisation, too.

 
 
 
 

Just like teenagers, self-driving cars need practice to really learn to drive

A self-driving car, of unknown level of education. Image: Grendelkhan/Flickr/creative commons.

What do self-driving cars and teenage drivers have in common?

Experience. Or, more accurately, a lack of experience.

Teenage drivers – novice drivers of any age, actually – begin with little knowledge of how to actually operate a car’s controls, and how to handle various quirks of the rules of the road. In North America, their first step in learning typically consists of fundamental instruction conveyed by a teacher. With classroom education, novice drivers are, in effect, programmed with knowledge of traffic laws and other basics. They then learn to operate a motor vehicle by applying that programming and progressively encountering a vast range of possibilities on actual roadways. Along the way, feedback they receive – from others in the vehicle as well as the actual experience of driving – helps them determine how best to react and function safely.

The same is true for autonomous vehicles. They are first programmed with basic knowledge. Red means stop; green means go, and so on. Then, through a form of artificial intelligence known as machine learning, self-driving autos draw from both accumulated experiences and continual feedback to detect patterns, adapt to circumstances, make decisions and improve performance.

For both humans and machines, more driving will ideally lead to better driving. And in each case, establishing mastery takes a long time. Especially as each learns to address the unique situations that are hard to anticipate without experience – a falling tree, a flash flood, a ball bouncing into the street, or some other sudden event. Testing, in both controlled and actual environments, is critical to building know-how. The more miles that driverless cars travel, the more quickly their safety improves. And improved safety performance will influence public acceptance of self-driving car deployment – an area in which I specialise.

Starting with basic skills

Experience, of course, must be built upon a foundation of rudimentary abilities – starting with vision. Meeting that essential requirement is straightforward for most humans, even those who may require the aid of glasses or contact lenses. For driverless cars, however, the ability to see is an immensely complex process involving multiple sensors and other technological elements:

  • radar, which uses radio waves to measure distances between the car and obstacles around it;
  • LIDAR, which uses laser sensors to build a 360-degree image of the car’s surroundings;
  • cameras, to detect people, lights, signs and other objects;
  • satellites, to enable GPS, global positioning systems that can pinpoint locations;
  • digital maps, which help to determine and modify routes the car will take;
  • a computer, which processes all the information, recognising objects, analysing the driving situation and determining actions based on what the car sees.

How a driverless car ‘sees’ the road.

All of these elements work together to help the car know where it is at all times, and where everything else is in relation to it. Despite the precision of these systems, however, they’re not perfect. The computer can know which pictures and sensory inputs deserve its attention, and how to correctly respond, but experience only comes from traveling a lot of miles.

The learning that is occurring by autonomous cars currently being tested on public roads feeds back into central systems that make all of a company’s cars better drivers. But even adding up all the on-road miles currently being driven by all autonomous vehicles in the U.S. doesn’t get close to the number of miles driven by humans every single day.

Dangerous after dark

Seeing at night is more challenging than during the daytime – for self-driving cars as well as for human drivers. Contrast is reduced in dark conditions, and objects – whether animate or inanimate – are more difficult to distinguish from their surroundings. In that regard, a human’s eyes and a driverless car’s cameras suffer the same impairment – unlike radar and LIDAR, which don’t need sunlight, streetlights or other lighting.

This was a factor in March in Arizona, when a pedestrian pushing her bicycle across the street at night was struck and killed by a self-driving Uber vehicle. Emergency braking, disabled at the time of the crash, was one issue. The car’s sensors were another issue, having identified the pedestrian as a vehicle first, and then as a bicycle. That’s an important distinction, because a self-driving car’s judgments and actions rely upon accurate identifications. For instance, it would expect another vehicle to move more quickly out of its path than a person walking.


Try and try again

To become better drivers, self-driving cars need not only more and better technological tools, but also something far more fundamental: practice. Just like human drivers, robot drivers won’t get better at dealing with darkness, fog and slippery road conditions without experience.

Testing on controlled roads is a first step to broad deployment of driverless vehicles on public streets. The Texas Automated Vehicle Proving Grounds Partnership, involving the Texas A&M Transportation Institute, University of Texas at Austin, and Southwest Research Institute in San Antonio, Texas, operates a group of closed-course test sites.

Self-driving cars also need to experience real-world conditions, so the Partnership includes seven urban regions in Texas where equipment can be tested on public roads. And, in a separate venture in July, self-driving startup Drive.ai began testing its own vehicles on limited routes in Frisco, north of Dallas.

These testing efforts are essential to ensuring that self-driving technologies are as foolproof as possible before their widespread introduction on public roadways. In other words, the technology needs time to learn. Think of it as driver education for driverless cars.

People learn by doing, and they learn best by doing repeatedly. Whether the pursuit involves a musical instrument, an athletic activity or operating a motor vehicle, individuals build proficiency through practice.

The ConversationSelf-driving cars, as researchers are finding, are no different from teens who need to build up experience before becoming reliably safe drivers. But at least the cars won’t have to learn every single thing for themselves – instead, they’ll talk to each other and share a pool of experience.

Johanna Zmud, Senior Research Scientist, Texas A&M Transportation Institute, Texas A&M University .

This article was originally published on The Conversation. Read the original article.