Classic experiments in evidence-based policing

So far, it’s been a fun semester teaching evidence-based policing for the first time. We have covered everything from evidence-based medicine to research design and the Maryland Scientific Methods Scale, and even some basic stats so that we can understand confidence intervals. It’s been particularly rewarding to see students who have spent years in policing exploring and learning about the world of research evidence that supports and helps their world, a world of which many have been until now unaware.

What I am also learning is that those of us in the police education field have done a lousy job of explaining what we do and why it is important to advancing policing and the practice of law enforcement. There is a range of classic studies that are not well known, and an absence of knowledge around these – and other important works – fuels the never-ending cycle of operational decisions that fly in the face of all we know about what works, and what doesn’t. Police still support strategies and crime reduction tactics that are known to not work.

In light of this, I started putting together a list of experiments of which that I thought my students should be aware. The original studies are described in a range of works from academic journal articles to long-winded reports. All pretty impenetrable for most folk, especially busy cops. So I have copied and pasted the key pieces of information into a single page per study, copied from the original sources directly. I cite them at the bottom of each page so you know the source.

This isn’t an exhaustive list, and I intend for it to grow, but for now the list comprises:

  1. The Kansas City Preventive Patrol Experiment
  2. The Newark Foot Patrol Experiment
  3. The Philadelphia Foot Patrol Experiment
  4. The Minneapolis Domestic Violence Experiment
  5. The Minneapolis Hot Spots Policing Experiment
  6. The Philadelphia Policing Tactics Experiment
  7. The Sacramento Hot Spots Policing Experiment
  8. The Queensland Procedural Justice Experiment

I will add to these over time, but for now if you want a copy, download a pdf of the one page summaries.

Note: If you are using these summaries to write a college paper, you should refer to the original study and cite it appropriately. All I have done is edit a copy-and-paste, but I’m 1) not writing a term paper and 2) not passing this off as my own work. If you do, that’s plagiarism. 

The Modifiable Areal Unit Problem

The Modifiable Areal Unit Problem (MAUP) is a potential source of error that can affect spatial studies which utilize aggregate data sources (Unwin, 1996). Geographical data are often aggregated in order to present the results of a study in a more useful context, and spatial objects such as census tracts or police beat boundaries are examples of the type of aggregating zones used to show results of some spatial phenomena. These zones are often arbitrary in nature and different areal units can be just as meaningful in displaying the same base level data. For example, it could be argued that census tracts containing comparable numbers of houses are better sources of aggregation than police beats (which are often based on ancient parish boundaries in the UK) when displaying burglary rates.Preview

Large amounts of source data require a careful choice of aggregating zones to display the spatial variation of the data in a comprehensible manner. It is this variation in acceptable areal solution that generates the term ‘modifiable’. Only recently (well, the last 30 years) has this problem been addressed in the area of spatial crime analysis, where ‘the areal units (zonal objects) used in many geographical studies are arbitrary, modifiable, and subject to the whims and fancies of whoever is doing, or did, the aggregating.’ (Openshaw, 1984 p.3).

As the study area for crime incident locations has effectively infinite resolution, there exists a potentially infinite number of different options for aggregating the data. Numerous administrative boundaries exists, such as enumeration districts, wards, counties, health authority areas, etc. Within modern GIS, it is an elementary task to automatically generate a huge variety of non-overlapping boundaries. Regular, often square, grids are common, though polygons have been used in other studies of crime distribution (Hirschfield et al., 1997). The number of different combinations of areal unit available to aggregate data is staggering. Openshaw (1984) calculated that if one was to attempt to aggregate 1,000 objects into 20 groups, you would be faced with 101,260 different solution combinations. Although there are a large number of different spatial objects and ways in which a large geographical area can be sub-divided, the choices of areal units tend to be dominated by what is available rather than what is best. Police crime data is often mapped to police beats, even when the information is passed to outside agencies such as neighborhood watches or local councils who might benefit from more relevant boundary structures.

The MAUP consists of both a scale and an aggregation problem, and the concept of the ecological fallacy should also be considered (Bailey and Gatrell, 1995). The scale problem is relatively well known. It is the variation which can occur when data from one scale of areal units is aggregated into more or less areal units. For example, much of the variation in census areas changes or is lost when the data are aggregated to the ward or county level.

The aggregation problem is less well known and becomes apparent when faced with the variety of different possible areal units for aggregation. Although geographical studies tend towards aggregating units which have a geographical boundary, it is possible to aggregate spatial units which are spatially distinct. Aggregating neighbors improves the problem to a small degree but does not get round the quantity of variation in possibilities which remains.

For a paper that discusses the MAUP and possible solutions, see:
Ratcliffe, J. H. and McCullagh, M. J. 1999 ‘Hotbeds of crime and the search for spatial accuracy’, Geographical Systems 1(4): 385-398. Paper available here.

Also see the Ecological Fallacy.

References:

Bailey, T. C. and Gatrell, A. C. 1995 Interactive Spatial Data Analysis, Second Edition: Longman.

Hirschfield, A., Yarwood, D. and Bowers, K. 1997 ‘Crime Pattern Analysis, Spatial Targeting and GIS: The development of new approaches for use in evaluating Community Safety initiatives.’, in N. Evans-Mudie (ed) Crime and health data analysis using GIS, Sheffield: SCGISA.

Openshaw, S. 1984 ‘The modifiable areal unit problem’, Concepts and Techniques in Modern Geography 38: 41.

Unwin, D. J. 1996 GIS, spatial analysis and spatial statistics’, Progress in Human Geography 20(4): 540-441.

The ecological fallacy

The ecological fallacy is a situation that can occur when a researcher or analyst makes an inference about an individual based on aggregate data for a group. For example, a researcher might examine the aggregate data on income for a neighborhood of a city, and discover that the average household income for the residents of that area is $30,000.

To state that the average income for residents of that area is $30,000 is true and accurate. No problem there. The ecological fallacy can occur when the researcher then states, based on this data, that people living in the area earn about $30,000. This may not be true at all, and may be an ecological fallacy.

ecological fallacyCloser examination of the neighborhood might discover that the community is actually composed of two housing estates, one of a lower socio-economic group of residents, and one of a higher socio-economic group. In the poorer part of town, residents earn on average $10,000 while the more affluent citizens can average $50,000. When the researcher stated that individuals who live in the area earn $30,000 (the mean rate) this did not account for the fact that the average in this example is constructed of two disparate groups, and it is likely that not one person earns around $30,000.

Assumptions made about individuals based on aggregate data are vulnerable to the ecological fallacy.

This does not mean that identifying associations between aggregate figures is necessarily defective, and it doesn’t necessarily mean that any inferences drawn about associations between the characteristics of an aggregate population and the characteristics of sub-units within the population are absolutely wrong either. What it does say is that the process of aggregating or disaggregating data may conceal the variations that are not visible at the larger aggregate level, and researchers, analysts and crime mappers should be careful.

 

Harm-focused policing

On 28th January, 2015 I gave the Police Foundation‘s Ideas in American Policing lecture on the topic of harm-focused policing. This brief blog provides some background details to the talk. Please note that for a number of reasons (including photograph copyright) I am not distributing copies of the PowerPoint slides.

Harm-focused policing weighs the social harms of criminality and disorder with data from beyond crime and antisocial behavior, in order to focus police priorities and resources in furtherance of both crime and harm reduction.

Example information and data sources could include drug overdose information that could help triage drug markets for interdiction, traffic fatality data to guide police patrol responses, and community impact assessments to prioritize violent street gangs. For a summary of the core of the presentation and a grey-scale version of some of the graphics, please see:

Ratcliffe, J. H. (2015). Towards an index for harm-focused policing. Policing: A Journal of Policy and Practice, 9(2), 164-182.

You can visit the journal site and access the paper here (or here) and watch my annotated video of the lecture below (you might want to make it full screen so you can read the slides).

During the presentation, I had a couple of quotes. Here are the quotes and their sources.

“to establish priorities for strategic criminal intelligence gathering and subsequent analysis based on notions of the social harm caused by different sorts of criminal activity”. The source for this is page 262 of Ratcliffe, J. H. & Sheptycki, J. (2009) Setting the strategic agenda. In J. H. Ratcliffe (ed.) Strategic Thinking in Criminal Intelligence (2nd edition) Sydney: Federation Press.

“Weighting crimes on the basis of sentencing guidelines can be justified on good democratic grounds as reflecting the will of the people. …… it remains far closer to the will of the people than any theoretical or even empirical system of weighting that academics might develop.” The source for this is Sherman, L. W. (2013). Targeting, testing and tracking police services: The rise of evidence-based policing, 1975-2025. In M. Tonry (Ed.), Crime and Justice in America, 1975-2025. Chicago: University of Chicago Press. Page 47.

 

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Why we shouldn’t fixate on homicide numbers

There are some certainties in life. Death, taxes, the Eagles snatching defeat from the jaws of victory. And the annual January media fixation with homicide rates as the barometer of everything from a city’s moral compass to the effectiveness of the police chief.

I spent a couple of days speaking to various reporters about the homicide numbers in Philadelphia, and how they were significantly down on a few years ago, but had remained largely unchanged since last year. ‘What could we gather from this?’ ‘What were the implications?’ ‘Were police department strategies starting to falter?’ ‘What does it mean for the mayor and police commissioner?’

Taking more time than I really had, given I am trying to update ‘Intelligence-Led Policing’ for a second edition, I tried to explain that the homicide figures are a really bad choice of metric. For just about anything. For example, a not insubstantial number of homicides occur between people who know each other, and often take place indoors. How are the police department supposed to anticipate and prevent those homicides? Even if they develop a ‘Minority Report’ predictive capacity, we have a reactive legal and criminal justice system: it isn’t keen on letting the police just wander into your house and lock you up for pondering murder. And for the homicides that take place on the street? Sitting in on numerous Philadelphia Police Department crime briefings and listening to the homicide reports, it is clear that many are the result of minor disputes that flared up with little-to-no warning or are the result of disputes between participants in gangs or drug organizations who conceal their business and would never seek the intervention of the police.

The difference between a homicide and an aggravated assault is also largely outside of police control. Could be the shooter has lousy aim or is firing gangster style, there is a delay in getting the victim to the hospital, or simply medical mismanagement. Once a person decides to shoot someone else, they are easily able to in the US because we allow them the opportunities to do so. Our legislators seem unwilling to help the police with this, so again, little chance for police influence here.

I examined a summary of every incident recorded by the Philadelphia Police for the last available full year (2013) to estimate how much police patrol energy is expended on responding to homicide incidents. In Philadelphia, the city receives millions of calls for service, and from these – as well as police-generated activity – an INCT database is created. This database contains every incident where a police officer was required to act, and ranges from dog bites and graffiti to shootings and homicides, and from assistance to city agencies and delivering messages, to removing debris from the interstate or arresting a drunk driver. In 2013 there were in excess of 1.65 million incidents. What percentage of these related to homicide? 0.021%. Less than one quarter of one tenth of one percent.

I explained to the reporters that aggravated assaults and robberies were also down, and due to their greater number generally, this was a much better way to indicate the crime health of the city. They said they got it, but their hands were tied: “the public interest is in homicides”.  So we still got story after story about the homicide rate. Not a major grumble: reporters have to make a call and write what they think is the story. But I wonder if the fascination with homicides is really driven by public demand, or by the media? I can’t believe there was massive public outcry that drove the claim that “Philly’s Murder Rate Is Skyrocketing Again in 2014”… especially only two weeks into the year (you had to go back and checked the post date didn’t you?).

Traditionally, homicides have been used because they are easily comparable between cities, because police departments have recorded other incidents in different ways, or because in the past (sometimes not so distant) the police have distorted the crime figures. But homicides comprise so little of the work of a police agency, and the chances of most people being a victim of homicide are so low, that they tell us little about the experienced crime rate or the quality of life for city residents.

We need to start moving to more holistic measures if oversight and strategy are to be more data driven and evidence based. Harm-focused policing that examines and weighs all incidents, and includes other harms to communities, such as traffic accidents or the potentially deleterious impact of unrestrained pedestrian investigations, is increasingly possible with the big data sets that public agencies generate. We need to evolve beyond our fixation with homicide if we are to move the discussion about safety and harm forward.

But in the meantime, Philadelphia, be glad that shootings and robberies are also down.

(This post was updated shortly after posting to correct the homicide incident rate)

PFPE map

Schrödinger’s crime hotspot

Attendance at the recent 2014 American Society of Criminology conference brought a chance to catch up with friends and observe a couple of splendid presentations (and quite a few awful ones). A couple of sessions in particular reaffirmed to me the gulf between some academic criminology and public policy. I watched as speakers attempted to parse in ever-increasing detail the boundaries of crime hotspots. Discussions continued around the efficacy of street blocks as potentially more accurate units of analysis compared to census block groups, and I could see the accuracy issue being a hot topic in predictive policing workshops. Hotspot boundary definition appeared to be the end in itself.

As my colleague Ralph B. Taylor has argued, “hot spots exist in the data world but not the real world” (Taylor, 2009) ¹. In this they are unlike land use parcels, behavior settings or street blocks – places that exist in the data and real worlds. He goes on to contend that “hot spots are amalgams of different types of locations” and we therefore have a construct validity problem. Simply because we see a cluster of events, does not mean we have a new entity (a crime hotspot) but rather a collection of events that exist as a cohesive entity only in the abstract world. To think otherwise is to commit a reification fallacy (Gould, 1981). When we move to the real world, and think about operationalizing a strategy to address our hotspots, things can unravel as it becomes clear that this collection of points exists for different reasons, each of which need addressing.

I thought of Ralph Taylor’s comments as I sat in the audience, and pondered the analogy between crime hotspots and Schrödinger’s cat. Erwin Schrödinger’s feline thought experiment was designed to explain why the Copenhagen interpretation of quantum superposition was flawed, because the cat exists in a paradoxical state of being neither alive nor dead. The hypothetical animal is placed in a steel box with a Geiger counter, a vial of poison, a hammer, and a small amount of radioactive substance, small enough to have only a 50/50 chance of being detected over the course of an hour. If the radioactive substance decay is detected by the counter, the hammer is triggered to smash the vial, release the poison and kill the cat. Only by looking in the box can the observer determine whether the cat is alive or dead. Prior to opening the box, the cat’s health is unknown and could be considered simultaneously alive and dead. The observer opens the box with the express intent of confirming the wellbeing of the cat. Before opening the box, the cat’s condition is unresolved and abstract. In the same way, crime hotspots are in a largely abstract state until we look at them from a particular viewpoint.

This brings me to two points that appeared rather lost on some of the conference speakers. First, crime mapping and the application of GIS to crime problems is not the end of the analysis – it is the start of it. Digital cartography is a necessary abstraction of the real world, and to think otherwise is to be oblivious to the classification, simplification and symbolization that takes place. It is through these processes that unrelated events can often be made apparently similar. The nighttime beating and robbery of a drug dealer will often be classified in the same manner as the punch a school child receives as they are relieved of their smart phone by a classmate. In a map of crime for the year, these events will likely be cartographically identical, but unlikely to be prevented in the future with the same response. Just because two crimes share geographic proximity, doesn’t mean they necessarily share a common cause (a point I’ve made elsewhere).

This brings me to a related second point. Crime hotspots (in the abstract world) are only made real when they are mapped for a purpose. When a police captain asks for a map of robbery hotspots, the captain is bringing a purpose to the analysis. He or she wants to deploy a surveillance team, task a crime prevention officer, or know where to assign more foot patrol officers. An academic, meanwhile, might want to seek underlying causal factors and understand why crime concentrates in certain areas. With the knowledge of the eventual purpose, a proficient analyst can create tailored hotspots that map to the parameters of the user’s needs. The maps would be different, but no less useful. Our captain brings a lens through which he or she interrogates the hotspot, and by looking at a map of crime hotspots they are made real and are understood. It is the captain, not the analyst, who opens the box.²

Both the captain and the academic bring to the analysis a predetermined purpose, and although different, the crime hotspots that each uncovers are equally valid. In opening the box, and staring at the map through the lens of a proposed application, crime hotspots are made real and understood. At this point, they can serve a purpose. But until then, they remain in an abstract state and their accuracy, or even their state as being alive or dead as viable entities, remains unknown. Like Schrödinger’s cat.

Works cited

Taylor, R. B. (2009). Hot spots do not exist, and other fundamental concerns about hot spots policing. In N. Frost, J. Freilich & T. Clear (Eds.) Contemporary Issues in Criminal Justice Policy: Policy Proposals from the American Society of Criminology Conference (pp. 271-278). Belmont, CA: Cengage/Wadsworth.

Gould, S. J. (1981). The Mismeasure of Man. New York: Norton.

Note

¹ And continues to discuss in greater detail in his forthcoming book, Taylor, R. B. (2015) Community Criminology. New York: New York University Press.

² In discussing this with John Eck he added the suggestion of a Schrödinger’s policy, where two policies sit in a box and are both in a state of existence, until someone looks at the data. Then only one policy becomes viable and exists.

Jerry’s top ten crime mapping tips

Some tips for crime mappers…

Tip 1: Include a scale bar. A map is all about geography, and what is the point if a map reader cannot tell how far one place is from another? Use sensible numbers such as 0 – 5 – 10 miles, and adjust your scale bar accordingly. Be careful when using automatic scale bars; they are rarely spot on first time. If you are presenting to an international audience, they will appreciate a map showing miles and kilometers. 5 miles is close enough to 8 kilometers for presentation purposes.

Tip 2: Include a North arrow. It may not seem much, but it takes up very little room, is easy to do and does help a few viewers. Some people say there is a limit to the value of North arrows. For example, it is probably not necessary to show a North arrow on a map of the of the US; but if in doubt, include a North arrow.

Tip 3: Simple and clear titles. Don’t forget a title for your map, and use a simple one that means something to a range of people. You never know who will use your map later on and may misinterpret what they are seeing (alas, I speak from experience). When deciding on a title, use the KISS principle (Keep It Simple Stupid!). Often the type of crime, the place, and the date range – is enough, but sometimes a more provocative title garners attention. An explanatory sub-title can be helpful.

Tip 4: Use color carefully. Color is a marvel – that should be used sparingly. Think about how appropriate your color use is, and use color for those things that you want to emphasize. Strong colored backgrounds tend to destroy any hope of seeing symbols on top of them. Try and use paler (more insipid) backgrounds to shade regions (if they have to be shaded at all) with dark or bold bright symbols over them. Don’t be afraid to experiment (and make improvements), but remember the maxim “less is more”. You might also want to view the companion web pages on color and presentations.

Tip 5: Really understand your data. Know what you are presenting, and understand the limitations of the data. This is especially true if you are presenting maps of data created by someone else. Having a map showing minute by minute burglary patterns is useless if your burglary data (like most) has start and end times hours or days apart. Repeat victimization is also a real consideration and most GIS will simply place one dot on top of another. Do something about this, or at least be ready to explain to your audience, either in a caveat or in person.

Tip 6: Use thematic mapping cautiously. Thematic mapping really simplifies what used to be a complex procedure, but the automatic settings used by most GIS still leave a lot to be desired. The automatic settings when making maps using ‘quantiling’ or ‘equal count’ for example, tend to end up with categories that use fractions that, while technically accurate, mean little to most viewers. Be prepared to customize them to more sensible values. GIS are also stupid in that they will let you make choropleth maps of things that should just not be mapped in that way. The automatic features should not be invoked without understanding your data and the thematic map processes. Graduated symbols such as circles should only be used to denote increasing values of something (e.g. value of goods stolen, or number of burglaries at a location), while area shading is preferable for showing proportional rates (assaults per 100,000 people) than raw rates (because the map can be skewed by different sized areas and therefore populations).

Tip 7: Legends are generally essential. A legend is essential if you have any type of shading or symbology. It will also help you remember what the map portrays months later. Use sensible numbers. 1 to less than 5 means something to most people. 1.000325 to 2.4352 is in the realm of nonsense, unless you have a very technical audience. In fact, I know a few technical people and they get offended by this. If you have complex numbers, you could always change the scale from ‘low criminal activity’ to ‘high criminal activity’ (or similar) and lose the numbers. The audience will appreciate it.

Tip 8: Caveats mean you are not lying. To make a map with a title saying; “Melbourne burglaries, 2014” implies that you are mapping all of the recorded burglaries. However it is still an unfortunate reality that geocoding rates are rarely 100% and you should tell the reader of the real rate, and any other caveats. This is especially the case if the geocoding hit rate is less than 85%. It saves embarrassing questions later, when someone points to an area of known burglaries that is featureless due to geocoding problems. You should append the caveat to the map itself, as maps and text can often be separated by others, either by accident or nefarious design.

Tip 9: Limit the information you show. As map complexity increases, a limit is reached beyond which map comprehension in the reader actually decreases. Sometimes it might be better to produce two or more maps instead of one monster that loses all meaning. A person can differentiate about 5 different types of symbol at a glance, and any more needs to be constantly checked back to the legend. Why put them through that? Also consider the function of additional features such as national parks, public toilets & railway lines. Are railway lines relevant and therefore necessary to your map? If you suspect that burglars are using them to gain access to properties then perhaps yes, but they are hardly relevant for a map of drug sale locations.

Tip 10: Check the map appearance in grayscale. If you map is a real success then it will be copied and disseminated – this is the real mark of success. Unfortunately until color copiers become standard you should run your map under a photocopier to see what comes out of the other end. This will give you an idea of what becomes indistinguishable or illegible after reproduction. Small, italicized text is particularly vulnerable, as are similar shades of color.

Ten ways to make your crime maps more ‘interesting’

PFPE mapAt various conferences and visits to police stations I have seen quite a few maps, and some have been great – really well presented, laid out and prepared. Alas many are awful, so I’ve put this page together as a brief guide to those perhaps less versed in the cartographic ways. You do not have to adhere to the guidelines here, but they might improve the readability and quality of your maps. If you do not follow these suggestions, ArcGIS will not self-destruct in a fit of cartographic rage – but this is part of the overall problem. The software does not understand your data and will let you do just about anything you want – even if it is wrong.

Note: If you are a Computer Science major and confused by the concept of sarcasm, feel free to click over to the vanilla version for you earnest types.

Tip 1: Do not include a scale bar. This will make it much more interesting as your map readers have to guess the distance between objects. One of the main aims of mapping crime is to compare areas and examine the proximity of objects, so why make it easy for the uninitiated to understand you map? Without a scale bar nobody will have a clue how far things are apart and this gives you the opportunity to have impromptu quizzes or make things up as you are presenting. If you accidentally include a scale bar use a scale that goes “0 —– 6.75 ——13.25 kilometers” instead of the usual “0 —5 —10” or similar. Big complex numbers really impress audiences.

Tip 2: Do not include a North arrow. Hundreds of years of cartographic tradition have no place in the new millennium – we are in the digital age and therefore all maps automatically have North at the top: even if we have to rotate the map to get it to fit on the page. Anyway, if you have visitors from outside your suburb, city, or country, why should they want to know in which direction is North so they can orientate themselves? They probably are not interested anyway.

Tip 3: Use jargon and special codes in the title of the map. Including special codes and police service jargon in the title of your map will make it, and you, look more professional. A title such as “B-type crimes for sectors GF and YTU for shifts R4 and R5” really impresses audiences. Make sure you also use dates in a mixture of European and American format at international conferences (without telling the audience which you are using). After all, 10/10/00 is the same either way, and what can the rest of the world teach us? Better still, don’t have a title at all (or have one that warbles on for three or more complete lines), and never put your name on the map – that way there is nobody to blame.

Tip 4: Find the color palette, and use every one. Color is what maps are all about. Use as many colors as you can find. There really are no rules about inappropriate choices of color, so bright cheerful pinks are fine for displaying child murder sites. If you have interesting symbols at particular places (such as body dump locations) try to de-emphasis them by making the background color glaring and bright. This will detract from your murder sites and make the viewer only see the underlying light industrial land use – much more important. Other features such as roads and railways and national parks are probably not very relevant, but they fill up the map nicely so give them a bold, bright color. This will further detract from your important points and distract the viewer – making them think there is less crime.

Tip 5: Don’t worry too much about understanding your data. The important thing is the presentation and the display. Don’t worry about showing maps with dots all over the place, often obscuring other dots. The audience will get the general picture and do not need complicated things like graduated circles to show how many crimes have occurred at the same place. This is just pedantic mapping for airy-fairy academics. And certainly do not worry about it when showing maps of repeat victimization. Also, if you can only geocode to the level of a zip code, still show the viewers the very best detail you can, right down to the street corner. Let “spurious accuracy” be your guide.

Tip 6: Make the most of thematic mapping. Those automatic thematic map menu items are there to be used as much as possible, and negate the need to really understand what they do. After all, it always looks great so it must be right! If you have a numerical date variable, use the graduated circle. I especially like those maps that show the time of day of an offence as a graduated circle. The bigger the circle – the later in the day. Stellar cartography right there. Another one is to use the bar graph function when comparing big numbers and little numbers. You can never see the little bars unless your nose is against the screen – how that one makes us all laugh in my office.

Tip 7: Legends have had their day. In bygone years there was a time for legends, but that age has most definitely passed. In modern cartography – especially for presentations, you will be there to explain the symbols and the values associated with different colors. And if you forget, don’t worry – the audience will understand. Hey, we’ve all done it. If you have to be passé and include a legend, then for color shaded areas use impressive looking numbers such as “3.01453 to less than 6.03215”, instead of “3 to less than 6”. This will impress the audience no end as you obviously have a grasp of quantum arithmetic.

Tip 8: Caveats look weak. You are out there to impress with your map. Having a caveat, especially for the geocoding rate, looks weak and as if you have not put enough effort in. To suggest that you have not been able to map every point will make you look bad next to all the other crime mappers who must obviously be better at it than you. After all, how can you make a good impression if you have data error? Being misleading is just helpful.

Tip 9: Get as much information onto a map as possible. Maps take time to produce so it is important to squeeze in as much information as possible. This is especially true for maps with symbols. Try and use more than 5 different types of symbol on a map, and ideally make them roughly the same size and color. It would be unfair to give added weight to one, so make them as indistinguishable as possible. If you can make them illegible from the back of a room on a PowerPoint presentation then that also helps because it makes people have to concentrate and come closer.

You could also try to disguise unhappy symbols like the locations of assaults and murders with unrelated symbols such as public lavatories and libraries. This can of course work both ways, it might suggest that there have been a lot of robberies, but most of your viewers will be fooled into thinking your area is well stocked for public utilities.

Tip 10: Never let anyone photocopy your map. A map is a work of art and should never be disseminated – ever. Stick it on the wall in the office, and use it in presentations but never let it out of your sight. Someone might take it down from the wall and photocopy it – ruining the point of the thing. Worse, they might actually use it to make a good public safety decision. The best way to teach them not to use your work is to make symbols and background colors roughly the same shade. A mid-blue and a mid-red blend nicely in color, producing a pleasing effect on the eye, but are indistinguishable when photocopied into grayscale. That will teach them to steal your maps!

Jerry’s top ten PowerPoint tips

After all the work that some people put into their research and analysis, it sometimes defies belief that they make a complete hash of their moment to impress – their moment to shine as an intellectual. After months of slaving over a hot keyboard many people use the opportunity presented by a briefing or conference to confuse the audience, contradict themselves and generally disappoint the whole room.

They do this by having a lousy PowerPoint. Now I’m well aware that some people reading this will have seen my presentations, so I’m not claiming to be a great presenter. But I would like to think that my audience at least can see and understand my slides, have time to read the words, and understand what I’m on about (most of the time). Having spent days or even weeks of my life staring at innumerable incomprehensible PowerPoint presentation, here are ten tips that I think help avoid some basic traps.

A PowerPoint presentation in the briefing room at Philadelphia Police Headquarters

A PowerPoint presentation in the briefing room at Philadelphia Police Headquarters

Tip 1: Strike a sensible contrast between text and background. Right at the beginning, try to get the presentation off to a good start. Back a few years ago when projectors were in their infancy, PowerPoint presentations looked best with a dark background and light text as these were the easiest to read and most gentle on the eye. That rule is still true, and by using a dark background you can make better use of a range of colors, colors that often look faded or indistinguishable against a light background.

The one caveat with dark backgrounds is that in brightly lit rooms, they can look washed out. So an option is to switch to a light background with dark text. Modern projectors are now able to project this combination better than before. But be warned that in a dark room with a bright PowerPoint presentation, the people near the front, stuck immediately in front of a huge, bright, glaring screen will feel like they are being interrogated. Be gentle on the audience and they will appreciate your message all the more.

Tip 2: Use simple titles and points. Long titles that go on for more than one line is a no-no. Keep titles and bullet points short and relevant. You are the message, not your slides, so don’t overcomplicate them. If you are a more accomplished speaker and use the slides as a prompt (always better than reading a script) try a few quirky bullet points. They will act as a more memorable prompt for you and will intrigue the audience.

Tip 3: Get the font size and type right. Absolute minimum font size 16, but bear in mind that with some fonts (such as Garamond) 16 is smaller than in another font (like Arial or Calibri). Flowery, airy-fairy fonts do not work well as they are difficult to read. Simple San Serif-type fonts such as Arial are simplest to read, but always go to the back of the room before the presentation to check the legibility. If you can not fit your text on the screen using a font size of 16 or more, don’t reduce the font size – reduce the number of words.

If you are taking your presentation somewhere else and using another machine, do not use some unique font you downloaded from the net, such as Ratcliffe’s Bizarro Bold Font (not a real font, I hope). The host presentation machine is unlikely to have the font. Unless you are skilled enough to have embedded the font in the presentation, PowerPoint will default to a standard font and this will probably disrupt your layout. Stick with the basic fonts unless you know what you are doing with embedding.

Tip 4: Limit the number of bullet points. Never more than 7 per slide and about 5 is best (think 4-6 as a good rule of thumb). A presentation should be an illuminating summary of your work, not the whole damn thing – so summarize. If you really want to put in more bullet points then break your list into a number of slides with other stuff in between. Listening to a presenter reading a list that can be read faster on the screen is many people’s idea of hell. Instead of boring people with a long list, give them a handout. Better to tell them a few things well, than lots of things badly.

Tip 5: Avoid the trap of fancy builds and dimming. ‘Builds’ is the term given to those fancy swirly ways to introduce text or other items to the screen. I’m sure you’ve seen them: text flies in from the left, then one letter is added at a time from the right, etc. If you need to use fancy builds to impress your audience then you really are struggling… Whizzy builds tend to annoy audiences, especially the slow builds accompanied by applause sounds or camera clicks. Regular conference attendees have seen them all and are not impressed. They also distract from what you are saying and your message. Slow builds can also be a presenter’s nightmare because if you have to hurry through your last slides, they hold you up. Avoid slow or complicated builds and transitions between slides. Use only the simple stuff.

Dimming is the term given to the dulling of, or worse, disappearing text or objects once the next item is visible. Legislation should be in place to prevent presenters showing you a bullet point and then hiding that point when the next point arrives. Most conference attendees have a modest attention span (and I’m being generous here). It is therefore annoying for them to look up after a few seconds mental time-out (i.e. checking their iPhone) and find they have missed the first points. Never dim segments of a chart as chart segments are only valuable if seen in proportion to the other elements.

Tip 6: Don’t rely on the spel chequer. This should be an obvious one, but I’ve seen one academic talk to a room full of 600 law enforcement personnel with slides that said pubic order instead of public order. Form instead of from, policy instead of police – there are lots of common mistakes. The importance of maintaining pubic order is still a favorite of mine though… Remember to plan your presentation prep well in advance: the more you are in a rush the more you will make mistakes here.

Tip 7: How to bore – include technical detail. There is nothing more soporific than large equations on a PowerPoint presentation (unless your whole presentation is to a room full of statisticians about a new algorithm you have just discovered). Complex, illegible flow diagrams with too little time spent explaining them can also do it. The presentation should be a short, catchy and impressive summary of your work. Don’t impress them with how much you have done, just impress them with what you have done. If you must include technical detail, give them a handout afterwards, or better still, point them to your most recent book/article/publication.

Tip 8: Maps and graphs speak volumes. To help with maps look at my top ten crime mapping tips and I suggest you look at this for charts as well. Maps are great for PowerPoint presentations as a picture really does say a thousand words. Never assume that your audience will know where you are talking about (for example, entertainment can be had asking Americans to name the capital of Australia or Canada). Charts and graphs should be simple and never have more than about 5 pieces or components. Line and bars are good for showing time periods, pies must show parts of a whole (i.e. 100%) and surface charts can show general trends. Don’t cram too much in, and don’t dim any part of a chart (see tip 5).

Tip 9: Continuity across slides is essential. If you really want to annoy your audience, change the builds, fonts, colors and styles regularly throughout your presentation. That will really hack them off, and it looks really amateur. Good presentations are slick, professional and keep up a consistent style throughout. Your organization may already have a corporate style, but don’t feel you have to throw away the tips here if they do. Decide on a color scheme, set of one or two fonts (One for titles, one for text) and stick to them.

Tip 10: Finish on your title slide or a black screen. You just finished your presentation, it has gone superbly well, and then you give away all the magic and let the audience see the trap door underneath the stage (i.e. the slide sorter page). When you finish talking show your title slide again with your contact details or end on a meaningful quote so that they can concentrate on your message and how good you are. Don’t let them see the slide layout view at the end, especially if you have unused or hidden slides. Doesn’t look good and will distract the audience just when you had won them over.

What we have learned from Philadelphia foot patrols

With the recent publication of our comparison of foot patrol versus car patrol, it ‘s worth a quick review of all that we learned from the Philadelphia Foot Patrol Experiment. Especially as the paper Liz Groff took the lead on is available for free from the publishers until the end of May.

The original foot patrol experiment paper described our randomized controlled field experiment which saw the Philadelphia Police Department place 240 officers on 60 violent crime hotspots (randomly selected from a list of 120) for the long hot summer of 2009. And it was hot walking the streets of Philadelphia in a ballistic vest – we all remember the fieldwork and empathizing with the officers who did it all summer!

At the end of the experiment, 90 violent crimes had been prevented, resulting in a net reduction of 53 violent offenses after some displacement. This was a 23 percent reduction in violent crime as a result of foot patrol in carefully-targeted areas – a unique finding for policing.

How was this achieved? We found that pedestrian stops increased by 64 percent in the foot patrol areas, probably increasing the likelihood that offenders would be stopped, and subsequently reducing their enthusiasm for carrying a firearm. We learned some other things that summer:

  1. There was no community backlash within the foot patrol areas. To the contrary, members of the local community were really upset when their foot patrol officers were eventually removed, and they let the PPD know about it in no uncertain terms.
  2. The image of foot patrol as a punishment posting changed to a degree within the PPD. Good commanders became convinced that foot patrol was a practical tactic in high crime areas, and some patrols remained in place after the experiment.
  3. The fool patrol officers got a real feel for their foot patrol areas, developing community and criminal intelligence in the months they spent on foot.
  4. The foot patrol officers engaged in more pedestrian stops than their vehicle-bound colleagues, and they also dealt with many more disorder incidents – an activity that is always an issue in the summer in Philadelphia. They dealt with fewer serious crime incidents, yet were undoubtedly responsible for the decline in violent crime.
  5. Importantly, the foot patrol officers engaged is a different type of police work than their colleagues in cars. Less response-driven, they engaged in more order maintenance and community-related activities. They did not replace the activities of the cars, but rather work in a complementary fashion, being co-producers of community safety with their colleagues. Even if they sometimes wandered a little.

Unfortunately, we also learned – in a subsequent Criminology article headed up by two enterprising graduate students, Evan Sorg and Cory Haberman – that the gains achieved during the foot patrol experiment did not last. The effects dissipated as soon as the foot patrol officers were removed, and in fact some effects were starting to wear off as the foot patrol experiment continued into the late summer.

My colleague Jen Wood took the lead on the qualitative component so important to understanding the nuance of the foot patrol experiment. We learned that officers negotiated order based on geography, people and space, and varied their strategies and tactics based on their knowledge of the people and the environment.

The experiment was generously awarded with a research award from the IACP and from the American Society of Criminology’s Division of Experimental Criminology, but more importantly it helped people recognize the Philadelphia Police Department as an innovative department willing to try new things, take risks, and learn. And while it involved a lot of researchers, they nearly all volunteered their time on top of their normal duties. Temple University and the College of Liberal Arts generously helped out with some fieldwork costs, indicative of their desire and ongoing commitment to moving the city forward; But the experiment – which learned so much – did not cost the city taxpayers a single cent.