Relaxing in Central Park - dogs are part of the scene
By Tarah Hodgkinson
One of the most common complaints I hear when working with neighbourhood residents around community safety is not what you might think. Generally, we work in neighbourhoods with some of the highest crime rates. One might assume that when asking residents of these neighbourhoods about their biggest concerns they would raise issues like gun violence, youth gangs, or drug trafficking.
Nope. They talk to us about dog poop.
Surprising right? Not for us. The inability of a neighbourhood to deal with the droppings of their four-legged friends highlights many of the same issues that contribute to crime and violence. These issues include a lack of territorial control and maintenance, an unwillingness to intervene, and a failure to work together to support the needs and values of the community.
A proliferation of pet feces is often indicative of other issues.
WHY IS DOG POOP IMPORTANT?
First, there aren’t supports in these neighbourhoods for pet ownership. Supports include green space, especially dog parks, locations with extra bags in case residents need them, and ample waste baskets. These might seem like minor changes, but they impact how residents clean up after their pets.
Second, people don’t care about their neighbourhood because they don’t feel a part of their neighbourhood. They aren’t connected to other people (what we term in 2nd Generation CPTED - socially cohesive) and so they are less likely to be concerned about leaving animal waste. When people are disconnected from their neighbours, informal controls are weaker, for example, worrying what your neighbours might think and behaving better.
Third, there is no ownership and maintenance of the spaces. People are more likely to dispose of their dog’s droppings appropriately when it is clear that a place is cared for and maintained. This is even more effective if residents know the owner of the space.
Simple message for dog owners - show respect!
PET OWNERSHIP
Pet ownership increased dramatically during the COVID-19 pandemic as many people had the additional time and space to care for a new friend. However, places for these furry friends are being reduced as green spaces are being fenced off or removed.
Creating connected communities, with the support necessary for pet ownership will make our neighbourhoods healthier and more liveable.
But to do that, we need to recognize that neighbourhoods aren’t just for people. And if we want to support a pet-friendly city, we need to ensure that our neighbourhoods are built for everyone – including our furry friends.
California wildfire researchers work with NASA's scientific data
by Gregory Saville
Yesterday I watched NASA’s meteorologists mapping California’s wildfires with LiDAR and climate sensors to help reduce risks of damage and death in the future. It is remarkable what scientists can do when they use obvious, decent data. As a university undergrad, I studied physical geography and meteorology. That's why the NASA documentary resonated. In university, and in the video, I learned that it is high-quality data that makes the difference.
Geographers study things like weather patterns, how epidemics spread, and how water controls our lives. Geography was the first science in the early environmental movement with climate studies on global warming long before it was rejected by extremist Climate Deniers.
Like the NASA video, it is all amazing stuff. At the core of it is real-life, measurable data with observable patterns.
Imagine my shock when I transferred to a degree program in urban studies, city development, and urban planning. Social geography data was so very different than science data! Social data is not always obvious, well-known, or observable. It uses surveys, tax, census, land use, and economic and demographic data. Social data can be difficult to collect and much of it is incomplete. Useful theories exist only after decades of research.
The very best applied and theoretical science at NASA's Johnson Space Center's Mission Control Center - author NASA
CRIME PATTERNS
In criminology, the situation is worse. Crime studies usually emerge from police data - in other words, citizen reports to the police. Sometimes this shows basic patterns and sometimes not. If citizens don't trust the police, they may not call the police for anything. How can we know if unreported incidents distort the real story?
Criminologists conduct self-report surveys and victim surveys to close the gap. However, the dirty secret of crime patterns is that crime data is horrid. Since data is the basis on which scientific theory proceeds, unreliable data cannot produce good science. If you thought weather prediction was a fuzzy science, at least it uses decent data.
Crime pattern studies, sadly, almost exclusively rely on police reports to analyze crime. In SafeGrowth we use a method called the risk assessment matrix to collect different forms of physical, social, and cultural data along with police stats. It's not perfect, but it is not dependent on police stats.
Now imagine crime prediction! Researchers serve us terminologies like time decay factors, graph signal processing, and the deep generative adversarial network. They slice and dice data into sophisticated algorithms and impressive 3-D graphs. All of this emerges, of course, from the worst imaginable data (at least in scientific terms). My geography classes seldom mentioned crime data. Now I know why.
Fortune-telling attempts prediction without data. This Japanese fortune-telling service had no luck predicting the demise of its own advertising tower - Author hanonimas, Creative Commons
Homicide research is the type I trust most (it's difficult to ignore a murder victim and most get reported). The homicide prediction study concludes:
“Our results are important because, from a policy perspective, optimal deployment of scarce police resources should be guided by high resolution spatial strategies”
Hm…
Well, first, police resources should be guided by evidence-based policy driven by reliable data! That is a long way off.
Second, predictions from mathematical algorithms and graph signal processing should be based on real-life context. For example, will deploying police resources to the right places cut homicides? If alleyway bar fights or outdoor robberies cause the majority of homicides, then police patrols and arrests might work. Policing gang behavior to cut outdoor gang shootings will definitely help.
Unfortunately, studies reveal that, in the US and most other Western countries, most murder victims know their attackers, many offenses occur inside the domestic household, or the victim and attacker live in the same neighborhood. It’s hard to imagine how police deployment will make much difference.
NASA Sunspot predictions for Solar Cycle 23 and 24 from solar observation data - Creative Commons
“Responses [to homicide] must place more responsibility on communities for identifying disputes at an early stage in their development, before they turn fatal.”
Since most homicides happen in the same troubled neighborhoods over and over, strategies like SafeGrowth build capacity in troubled neighborhoods to help residents, with their local police, to transform violence-breeding neighborhoods into livable, crime-resistant places.
Not much prediction is needed there…just municipal services, competent training, and resources to get started. I believe strongly in research and evidence to create viable prevention programs. But let’s not get ahead of ourselves with delusions of scientific prediction when the data is so poor and well-known solutions are already at hand.
Let's admit the truth of crime data and the limit to what it says.
