Lux in Tenebris The Spatial Analysis of the Multivalent Factors that Influence Crime

Problem - Urban Regulatory Policy and Infrastructure decisions are made without adequately measuring their effects? :

The last 2 centuries have seen an almost uninterrupted global push towards urbanization. More and more people leave traditional rural communities behind and move to cities in search of different and often better opportunities for growth for themselves and their families. This has led to ever bigger and denser cities facing ever increasing and novel problems. As the demographic growth of our urban centers continues to increase at an exponential rate, the ability of authorities, urban planners, decision makers and everyone else involved in policy and regulatory reform has been surpassed. Our authorities are in a race just to keep up with the increasing pressures from and ever-changing reality and this forces them to often come up with new and untested ideas in the hope that they will be able to at least solve today the problems identified yesterday, only to find out tomorrow that the measures where already obsolete at the time they where being implemented. It almost seems hopeless to simply function let alone try to measure the effects of policy on communities that are themselves changing in real time. All of this leads to even more ambiguity on the actual effect of policy and it in turn leads to more chaos and the implementation of well-intentioned but often catastrophic policies that not only fail to address the underlying problems but end up doing more harm than good.

On the other hand, we have infrastructure, the fact that our cities are growing in real time, the city of Tijuana Mexico, for example, grows at an average of 6 hectares per week, this means that our infrastructure has to grow at the same pace or as close to it as possible just to keep things functioning. As with policy, our decision makers must make sure that this infrastructure is both functional, efficient and safe. Again, they are forced to create new infrastructure before they have even measured if the infrastructure is functioning as intended. The pace of growth makes it imposible to give order to this chaos, traffic has gone from being a nuisance to a concern as it starts to stunt our growth. If food or perishables, or workers, or students, can’t make it to their intended destinies on time, if an over taxing of powerlines, or excessive demand on water resources, or sanitary systems cause them to fail, then the same growth that originated the problem becomes its victim. Infrastructure can not be allowed to fail. So this makes infrastructure a priority, however, again the same as with policy, we need to understand how infrastructure affects the community, because if well-functioning infrastructure causes the community more harm than good, then the whole process ends in failure.

The fact remains that regardless of why, Urban Regulatory Policy and Infrastructure decisions are being made without scientific rigor and without measuring their actual effects.

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Objectives:

Determine if Urban Regulatory Policies and Infrastructure decisions play a role in crime distribution and if so, what is this role

Measure the effect of Urban Regulatory Policies and Infrastructure decisions on Crime Distribution. For this,

Create Protocols to measure in real time the effect of Regulatory Policies and Infrastructure Decision on Crime Distribution and any other observable effects

Identity areas of opportunity to guide Policy and Regulatory Practices, Standards and Codes to prioritize the reduction of Crime and curtailment of Criminal behavior.

Establish new science based operational guidelines to prevent future errors in decision making.

Methodology: G.I.S. Analysis

Starting with Georeferenced Crime Data to identify patterns in distribution

Rather than analysing every single policy decision taken in any given time period, we will start with Georeferenced Crime Data, essentially the distribution of crimes that led to arrests in the city of Tijuana during a time span of 5 years. With this information on hand, we will try to identify geograghic patterns in the distribution that would lead us to believe that their might be specific measurable variables that influence said distribution.

The logic behind this methodology is as follows, both Urban Regulatory Policy Decisions and Decisions that affect Infrastructure in any urban setting, have a geographic area of influence, in example, the decision to allow the operation of specific business ventures, or the placement of streetlights, these decisions have a geographic physical aspect. So, if there where a cause and effect relation between any of these Decisions and Crime Distribution, then the latter would have to display some geographic pattern such that we should find some visual geographic correlation between the decision (cause) and crime distribution (effect). If, on the other hand, we find that crime distribution where completely random, then their probably is no correlation between it and policy and infrastructure decisions and our hypothesis would be null.

If we find geographic patterns to crime distribution then we would proceed to identify what decisions share the geographic pattern. We would accomplish this by comparing datasets of the areas of influence of different decisions and the variables that these decisions affect such as street light placement and or maintenance, the location of abandoned houses and or buildings, living quarters density, etc, This would allow us ti identify possible correlations.

If we find any such correlations, we can proceed to identify procedures to measure the degree of the effect and in so doing so we can create protocols both for continued studies in this field and to allow authorities to measure the effect of their decision in real time and provide scientific rigor to their decision making going forward.

If we get to this point, we would have identified specific areas of interest where we have the opportunity to create policies and or standards and codes or modify existing codes to have a lasting beneficial effect on crime distribution.

If we can effectively create policy based on the expected results, or find ways to use infrastructure to diminish crime and or at least prevent it from fostering crime, then we can create a science based set of basic guidelines for decisions makers to follow going forward.

The information for this can be located in public access Esri Shape databases such as INEGI (Instituto Nacional de Estadística Geograghía e Información), the State and Municipal Secretaries of Public Safety as well as the Municipal Secretary of Public Works specifically DOIUM (Department of…) say for streetlight placement.

INEGI publishes several datasets and keeps them updated in some cases on a bimonthly basis. The DENUE (Directorio Nacional de Unidades Económicas) is an open access public database that contains every single business in Mexico. Businesses work based on operation permits; these are Regulatory Policy Decisions. The SCINCE is the National Census and provides information at the Urban Block level on all statistics from said census social, demographic, etc. All these datasets, except for the police datasets on arrests are completely public and can be readily downloaded. For the last one, arrests, a detailed request has to be made to the city police department based on current public information access law, however, even the public data on the secretary′s web page would suffice.

Impact and Intervention

We Identified a Strong Correlation between Urban Social Factors and Violent Crime Distribution, specifically violence towards women. (SCINCE).

Abandoned houses, over crowdedness, squalor, and factors that contribute to the generational poverty trap: lack of opportunity, education, stagnant social mobility. Communities that exhibited these factors showed more than double violent crime specifically against women.

We Identified a Strong Correlation between High Street Illumination and low night time street crime. (DOIUM).

We managed to identify a threshold of minimum required illumination to prevent most night crime: 10 streetlights per hectare is this threshold. Areas that have poor lighting, such that the perp cannot be easily identified but that lighting is enough for the perp to be confident that the victim is vulnerable, show severely increased street crime, however, areas of total darkness where even the criminals cannot ascertain their safety show nearly no street crime.

We managed to identify a strong correlation between both Pawn Shops and Clandestine Metal Recyclers and Crime. (DENUE).

We identified a strong correlation between both Pawn Shops and Clandestine and or irregular Metal Recyclers and Crime (theft). This would also point to regulatory policy decisions. In the 200 meters around a Pawn shop you are 4 times more likely to be the victim of theft.

We managed to identify a strong correlation between green areas, specifically long big parks or interconnected parks and a lower level of violence towards women. (INEGI).

Finally, we managed to prove the strong correlation between large long parks or interconnected parks and low violence towards women. Green seems to be better for the social health as well as for the environment.

We have the opportunity to both guide future code and authorities.

Not only can we and should we guide future codes to incorporate these uncovered realities to prevent crime, but we where able to modify the way the Police Department in Tijuana works, as they have incorporated GIS into their daily routine.

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Arnold Coronado is a Civil Engineer and Researcher, GIS Analyst focused on Risk Management and regulatory and code reform to reduce and manage risk. Most of this investigation was carried out as he worked in the Tijuana Metropolitan Institute of Planning, IMPLAN.

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Ana Garza is an Architect-Biotechnician focusing on green construction and bridging the gap between the need to develop and the obligation to develop without overtaxing the environment.

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Nestor Cruz is an accountant that worked in the Tijuana Police Department in the office of Strategic Planning from an Information Analyst all the way up to Director.?