And each of those pages links to a GitHub page where all the lab goodies are stored.

The seminar in research focuses on popular quasi-experimental designs in CJ, and has code in R/Stata/SPSS for the weekly lessons. (Will need to update with python, I may need to write my own python margins library though!)

Grad GIS is mostly old ArcGIS tutorials (I don’t think I will update ArcPro, will see when Eric Piza’s new book comes out and just suggest that probably). Even though the screenshots are perhaps old at this point though the ideas/workflow are not. (It also has some tutorials on other open source tools, such as CrimeStat, Jerry’s Near Repeat Calculator, GeoDa, spatial regression analysis in R, and Mallesons/Andresens SPPT tool are examples I remember offhand.)

Undergrad Crime Analysis is mostly focused on number crunching relevant to crime analysts in Excel, although has a few things in Access (making SQL queries), and making a BOLO in publisher.

So for folks self-learning of course use those resources however you want. My suggestion is to skim through the syllabus, see if you want to learn about any particular lesson, and then jump right to that one. No need to slog through the whole course if you are just interested in one specific thing.

They are also freely available to any instructors who want to adapt those materials for their own courses as well.

One of the things that has disappointed me about the teaching response to Covid is instead of institutions taking the opportunity to really invest in online teaching, people are just running around with their heads cut off and offering poor last minute hybrid courses. (This is both for the kiddos as well as higher education.)

If you have ever taken a Coursera course, they are a real production! And the ones I have tried have all been really well done; nice videos, interactive quizzes with immediate feedback, etc. A professor on their own though cannot accomplish that, we would need investment from the University in filming and in scripting the webpage. But once it is finished, it can be delivered to the masses.

So instead of running courses with a tiny number of students, I think it makes more sense for Universities to actually pony up resources to help professors make professional looking online courses. Not the nonsense with a bad recorded lecture and a discussion board. It is IMO better to give someone a semester sabbatical to develop a really nice online course than make people develop them at the last minute. Once the course is set up, you really only need to administer the course, which takes much less work.

Another interested party may be professional organizations. For example, the American Society of Criminology could make an ad-hoc committee to develop a model curriculum for an intro criminology course. You can see in my course pages I taught this at one point – there is no real reason why every criminology teacher needs to strike out on their own. This is both more work for the individual teacher, as well as introduces quite a bit of variation in the content that crim/cj students receive.

Even if ASC started smaller, say promoting individual lessons, that would be lovely. Part of the difficulty in teaching a broad course like Intro to Criminology is that I am not an expert on all of criminology. So for example if someone made a lesson plan/video for bio-social criminology, I would be more apt to use that. Think instead of a single textbook, leveraging multi-media.

It is a bit ironic, but one of the reasons I was hired at HMS was to internally deliver data science training. So even though I am in the private sector I am still teaching!

Like I said previously, you are on your own for developing teaching content at the University. There is very little oversight. I imagine many professors will cringe at my description, but one of the things I like at HMS is the collaboration in developing materials. So I initially sat down with my supervisor and project manager to develop the overall curricula. Then for individual lessons I submit my slides/lab portion to my supervisor to get feedback, and also do a dry run in front of one of my peers on our data science team to get feedback. Then in the end I do a recorded lecture – we limit to something like 30 people on WebEx so it is not lagging, but ultimately everyone in the org can access the video recording at a later date.

So again I think this is a better approach. It takes more time, and I only do one lecture at a time (so take a month or two to develop one lecture). But I think that in the end this will be a better long term investment than the typical way Uni’s deliver courses.

]]>Here is a brief example to show how my R code works. You can source it directly from my dropbox page. Then I generated 10k simulated rows of Poisson data with a mean of 0.2. So I see many people in CJ make the mistake that, OK my data has 85% zeroes, I need to use some sort of zero-inflated model. If you are working with very small spatial/temporal units of analysis and/or rare crimes, it may be the mean of the distribution is quite low, and so the Poisson distribution is actually quite close.

```
# My check Poisson function
source('https://dl.dropboxusercontent.com/s/yj7yc07s5fgkirz/CheckPoisson.R?dl=0')
# Example with simulated data
set.seed(10)
lambda <- 0.2
x <- rpois(10000,lambda)
CheckPoisson(x,0,max(x),mean(x))
```

Here you can see in the generated table from my `CheckPoisson`

function, that with a mean of 0.2, we expect around 81.2% zeroes in the data. And since we simulated the data according to the Poisson distribution, that is what we get. The table shows that out of the 10k simulation rows, 8121 were 0’s, 1692 rows were 1’s etc.

In real life data never exactly conform to hypothetical distributions. But we often want to see how close they are to the hypothetical before building predictive models. A real life example as close to Poisson distributed data as I have ever seen is the Washington Post Fatal Use of Force data. Every year WaPo has been collating the data, the total number of Fatal uses of Police Force in the US have been very close to 1000 events per year. And even in all the turmoil this past year, that is still the case.

```
# Washington Post Officer Involved Shooting Deaths Data
oid <- read.csv('https://raw.githubusercontent.com/washingtonpost/data-police-shootings/master/fatal-police-shootings-data.csv',
stringsAsFactors = F)
# Year Stats
oid$year <- as.integer(substr(oid$date,1,4))
year_stats <- table(oid$year)[1:6]
year_stats
mean(year_stats)
var(year_stats)
```

One way to check the Poison distribution is that the mean and the variance should be close, and here at the yearly level the data have some evidence of underdispersion according to the Poisson distribution (most crime data is overdispersed – the variance is much greater than the mean). If the actual mean is around 990, you would expect typical variations of say around plus/minus 60 per year (`~ 2*sqrt(990)`

). But that only gives us a few observations to check (6 years). We can dis-aggregate the data to smaller intervals and check the Poisson assumption. Here I aggregate to days (note that this includes zero days in the table levels calculation). Then we again check the fit of the Poisson distribution.

```
#Now aggregating to count per day
oid$date_val <- as.Date(oid$date)
date_range <- paste0(seq(as.Date('2015-01-01'),max(oid$date_val),by='days'))
day_counts <- as.data.frame(table(factor(oid$date,levels=date_range)))
head(day_counts)
pfit <- CheckPoisson(day_counts$Freq, 0, 10, mean(day_counts$Freq))
pfit
```

According to the mean and the variance, it appears the distribution is a very close fit to the Poisson. We can see in this data we expected to have around 147 days with 0 fatal encounters, and in reality there were 160. I like seeing the overall counts, but another way is via the proportions in the final three columns of the table. You can see for all of the integers, we are less than 2 percentage points off for any particular integer count. E.g. we expect the distribution to have 3 fatal uses of force on about 22% of the days, but in the observed distribution days with 3 events only happened around 21% of the days (or `20.6378132`

without rounding). So overall these fatal use of force data of course are not exactly Poisson distributed, but they are quite close.

So the Poisson distribution is motivated via a process in which the inter-arrival dates of events being counted are independent. Or in more simple terms one event does not cause a future event to come faster or slower. So offhand if you had a hypothesis that publicizing officer fatalities made future officers more hesitant to use deadly force, this is not supported in this data. Given that this is officer involved fatal encounters in the entire US, it is consistent with the data generating process that a fatal encounter in one jurisdiction has little to do with fatal encounters in other jurisdictions.

(Crime data we are often interested in the opposite self-exciting hypothesis, that one event causes another to happen in the near future. Self-excitation would cause an increase in the variance, so the opposite process would result in a reduced variance of the counts. E.g. if you have something that occurs at a regular monthly interval, the counts of that event will be underdispersed according to a Poisson process.)

So the above examples just checked a univariate data source for whether the Poisson distribution was a decent fit. Oftentimes academics are interested in whether the conditional distribution is a good fit post some regression model. So even if the marginal distribution is not Poisson, it may be you can still use a Poisson GLM, generate good predictions, and the conditional model is a good fit for the Poisson distribution. (That being said, you model has to do more work the further away it is from the hypothetical distribution, so if the marginal is very clearly off from Poisson a Poisson GLM probably won’t fit very well.)

My `CheckPoisson`

function allows you to check the fit of a Poisson GLM by piping in varying predicted values over the sample instead of just one. Here is an example where I use a Poisson GLM to generate estimates conditional on the day of the week (just for illustration, I don’t have any obvious reason fatal encounters would occur more or less often during particular days of the week).

```
#Do example for the day of the week
day_counts$wd <- weekdays(as.Date(day_counts$Var1))
mod <- glm(Freq ~ as.factor(wd) - 1, family="poisson", data=day_counts)
#summary(mod), Tue/Wed/Thu a bit higher
lin_pred <- exp(predict(mod))
pfit_wd <- CheckPoisson(day_counts$Freq, 0, 10, lin_pred)
pfit_wd
```

You can see that the fit is almost exactly the same as before with the univariate data, so the differences in days of the week does not explain most of the divergence from the hypothetical Poisson distribution, but again this data is already quite close to a Poisson distribution.

So it is common for people to do tests for goodness-of-fit using these tables. I don’t really recommend it – just look at the table and see if it is close. Departures from hypothetical can inform modeling decisions, e.g. if you do have more zeroes than expected than you may need a negative binomial model or a zero-inflated model. If the departures are not dramatic, variance estimates from the Poisson assumption are not likely to be dramatically off-the-mark.

But if you must, here is an example of generating a Chi-Square goodness-of-fit test with the example Poisson fit table.

```
# If you really want to do a test of fit
chi_stat <- sum((pfit$Freq - pfit$PoisF)^2/pfit$PoisF)
df <- length(pfit$Freq) - 2
dchisq(chi_stat, df)
```

So you can see in this example the p-value is just under 0.06.

I really don’t recommend this though for two reasons. One is that with null hypothesis significance testing you are really put in a position that large data samples always reject the null, even if the departures are trivial in terms of the assumptions you are making for whatever subsequent model. The flipside of this is that with small samples the test is underpowered, so there are never many good scenarios where it is useful in practice. Two, you can generate superfluous categories (or collapse particular categories) in the Chi-Square test to increase the degrees of freedom and change the p-value.

One of the things though that this is useful for is checking the opposite, people fudging data. If you have data too close to the hypothetical distribution (so very high p-values here), it can be evidence that someone manipulated the data (because real data is never that close to hypothetical distributions). A famous example of this type of test is whether Mendel manipulated his data.

I intentionally chose the WaPo data as it is one of the few that out of the box really appears to be close to Poisson distributed in the wild. One of my next tasks though is to do some similar code for negative binomial fits. Like Paul Allison, for crime count data I rarely see much need for zero-inflated models. But while I was working on that I noticed that the parameters in NB fits with even samples of 1,000 to 10,000 observations were not very good. So I will need to dig into that more as well.

]]>```
Pre Post
Treated 80 20
Control 100 50
```

So then our difference-in-difference Poisson estimate of the treatment effect would be:

`(20 - 80) - (50 - 100) = -10`

What the parallel trends assumption means here is that since you saw a decrease in 50 crimes in the control area, you would expect a decrease of 50 crimes in the treated area as well. The variance of this estimate is then `20 + 80 + 50 + 100 = 250`

, and so the standard error is `sqrt(250) ~ 15.8`

. So this is not a statistically significant effect.

It is hard to interpret this effect size though, since it is not a standard unit of time comparison. Also the variance of the estimate will be larger if you have a longer pre time period, which is the opposite of what you want. We can actually amend the statistic though to be a per-unit-time comparison, which will reduce the variance of the estimate. It ends up being similar to my prior post on adding Harm Weights to the WDD, you can’t just pipe in the per unit time estimates in the spreadsheet I shared, but I will show here how to incorporate them into the estimator (and share some python code to show the estimator behaves as expected in simulations).

So again with a pre-time period of 2 years, and post of 1 year, we could do the prior table as per year estimates.

```
Pre Post
Treated 40 20
Control 50 50
```

And here our estimate of the crime reduction effect is different:

`(20 - 40) - (50 - 50) = -20`

So with a Poisson variable with a mean of 100, the variance of that variable is also 100. So here we are dividing that 100 by a constant 2 – this changes the variance to 100/(2^2). (`Var(X*a) = a^2*Var(X)`

where `X`

is a random variable and `a`

is a constant.) The post variables are simply divided by one, so does not change their variance. So to carry this forward to our standard error estimate, we would calculate:

`20/1 + 40/4 + 50/1 + 50/4 = 92.5`

So you can see that our variance estimate here is much smaller, and that the standard error is `sqrt(92.5) ~ 9.6`

. So here the reduction is right on the border of a statistically significant reduction in crimes. A 95% confidence interval would be `-20 +/- 2*9.6 ~ [-1, -39]`

. Here the WDD estimate is easier to interpret as well, and that confidence interval corresponds to a per year estimate reduction of somewhere between 1 and 39 crimes.

Below I share some python code to conduct simulations similar to the original WDD paper. This code will then establish the estimator has the null distribution as expected (when there are no changes it really is a standard normal distribution) and that the confidence intervals have coverage like you would expect.

For set up, I import the libraries I need (stat distributions, numpy and pandas). I am not going to go into detail into the functions, but it allows you to generate simulated distributions in various ways to conduct analysis of the properties of my time weighted estimator I have specified above.

```
'''
WDD Simulation with differing time periods
Andy Wheeler
'''
import pandas as pd
import numpy as np
from scipy.stats import norm
from scipy.stats import poisson
from scipy.stats import uniform
#This works for the scipy functions
np.random.seed(seed=10)
# A function to generate the WDD estimate for simulated data
def wdd_sim(treat0,treat1,cont0,cont1,pre,post):
tr_cr_0 = poisson.rvs(mu = treat0, size=int(pre)).sum()
co_cr_0 = poisson.rvs(mu = cont0, size=int(pre)).sum()
tr_cr_1 = poisson.rvs(mu = treat1, size=int(post)).sum()
co_cr_1 = poisson.rvs(mu = cont1, size=int(post)).sum()
est = ( tr_cr_1/post - tr_cr_0/pre ) - ( co_cr_1/post - co_cr_0/pre )
post2 = (1/post)**2
pre2 = (1/pre)**2
var_est = tr_cr_0*pre2 + tr_cr_1*post2 + co_cr_0*pre2 + co_cr_1*post2
true_val = ( treat1 - treat0 ) - ( cont1 - cont0 )
z_score = est / np.sqrt(var_est)
return (est, var_est, true_val, z_score)
def make_data(n, treat0, treat1, cont0, cont1, pre, post):
base = pd.DataFrame( range(n), columns=['index'])
base['treat0'] = treat0
if treat1 is not None:
base['treat1'] = treat1
else:
base['treat1'] = base['treat0']
if cont0 is not None:
base['cont0'] = cont0
else:
base['cont0'] = base['treat0']
if cont1 is not None:
base['cont1'] = cont1
else:
base['cont1'] = base['cont0']
base.drop(columns='index',inplace=True)
base['pre'] = pre
base['post'] = post
sim_vals = base.apply(lambda x: wdd_sim(**x), axis=1, result_type='expand')
sim_vals.columns = ['est','var_est','true_val','z_score']
return pd.concat([base,sim_vals], axis=1)
```

So for a first example, this code generates treatment/control areas with a Poisson mean of 5 in both the pre/post time periods. But, the pre time period is 4 units of time, and the post time period is only 1 unit. So this means there is no change, and the Z score estimator should on average have a 0 estimate and a standard deviation of 1. I do 10,000 simulations to keep it going a bit faster, but you can up that if you want.

```
# No change, with baseline of 5 crimes per unit time
sim_dat = make_data(10000, 5, 5, 5, 5, 4, 1)
sim_dat['z_score'].describe()
```

So here we can see these 10k simulated Poisson data have a mean z-score of 0 and a standard deviation of 1, right like we expected.

So I haven’t extensively tested, but if you have average crime counts well under 5, I would be a bit hesitant to use this estimator. (So you either need larger area aggregations or larger time aggregations.) Although you could do simulations on your own to see how it holds up.

The way I wrote the functions you can also pass in random variables as well, so here is an example with again no change, but the baseline varies uniformily from 5 to 100. And here also the pre time periods are 6, and the post time period is again just 1.

```
# Can pass in random functions instead of constant values
sim_n = 10000
tf = uniform.rvs(loc=5, scale=100, size=sim_n)
sim_dat2 = make_data(sim_n, tf, None, None, None, 6, 1)
sim_dat2.head()
sim_dat2['z_score'].describe()
```

So you can see the base simulated dataset pre/post always has the same means, but instead of being a set of constant 5’s, it changes for each row (simulation) in the dataset. And again the null distribution is right on the money with a mean of 0 and standard deviation of 1.

So those are examples of the null distribution of no changes in the time weighted estimator. This establishes that the false positive alpha rates are as you would expect. E.g. if you use the usual p-value < 0.05, if the differences are really 0 you only have a false positive reject the null 5 times out of 100.

But we also want to establish that when there is a difference, the estimator is not biased and that the variance estimates are correct. For the later part looking at the coverage rates of the confidence intervals is one way to do that. So here I show that with my hypothetical example in the intro part of this blog, the 95% and 90% confidence interval coverage rates are exactly as they should be. And the z-score estimate is right about where it should be as well.

```
# Lets look at the coverage rate for a decline from 40 to 20
def cover(data, ci=0.95):
mult = (1 - ci)/2
nv = norm.ppf(1 - mult)
dif = nv*np.sqrt( data['var_est'] )
low = data['est'] - dif
high = data['est'] + dif
cover = ( data['true_val'] > low) & ( data['true_val'] < high )
return cover
sim_dat3 = make_data(sim_n, 40, 20, 50, 50, 2, 1)
sim_dat3.head()
# This should be centered on 2
sim_dat3['z_score'].describe()
# Should be ~ 0.9
co_90 = cover(sim_dat3, ci=0.9)
co_90.mean()
# Should be ~ 0.95
co_95 = cover(sim_dat3, ci=0.95)
co_95.mean()
```

So you can see the coverage is right on the money. The estimator is slightly biased downward in this simulation (should get a z-score on average around -2, but here the mean is -1.85). But it is good enough IMO to not worry about much in this situation.

Again, the original estimator without weighted for time is fine, if we do the same motions without doing weighting for different time periods, the coverage is still all fine and dandy.

```
# Note you can do the same coverage estimate without time weighted
sim_dat4 = make_data(sim_n, 80, 20, 100, 50, 1, 1)
sim_dat4.head()
# This should be around -0.6
sim_dat4['z_score'].describe()
co_90w = cover(sim_dat4, ci=0.9)
co_90w.mean()
co_95w = cover(sim_dat4, ci=0.95)
co_95w.mean()
```

So you can see again coverage is right on the money, and the z-score estimator actually has less bias than the time weighted one, it is right on the money as expected.

So why would you prefer the time weighted estimator if it shows more bias? It is because it has a lower variance, this code shows the length of the confidence intervals in the simulations.

```
# Does it make a difference?
def len_ci(data, ci=0.95):
mult = (1 - ci)/2
nv = norm.ppf(1 - mult)
dif = nv*np.sqrt( data['var_est'] )
low = data['est'] - dif
high = data['est'] + dif
return high - low
len4 = len_ci(sim_dat4)
len4.describe()
len3 = len_ci(sim_dat3)
len3.describe()
```

So you can see here that the non-time weighted estimator tends to have a confidence interval with a length of 62, whereas the time weighted estimator has a confidence interval on average of 42.

So above establishes that the time weighted estimator behaves as you would expect. You can also use this code to conduct some potential power analyses. So for the time weighted estimator we show, even though the reduction is around 50% in the treated area (going from 40 to 20), the power is not great, around 60%.

```
# Example power analyses, ONE TAILED
def reject_rate(data, alpha=0.05):
p_vals = norm.cdf(data['z_score'])
return p_vals < alpha
r3 = reject_rate(sim_dat3)
r3.mean()
```

So this means if you did this experiment in real life and it was that effective, you would still fail to reject the null of no differences 2/5 times.

But what if we say we will get more historical data? So 4 years back instead of just 2? How does that impact our power estimates?

```
# How about with more historical data
sim_dat5 = make_data(sim_n, 40, 20, 50, 50, 4, 1)
r5 = reject_rate(sim_dat5)
r5.mean()
```

The power goes up by alittle, to 0.67. The same is true if we up the post period to 4 time periods instead of 1:

```
# How about with more post data
sim_dat6 = make_data(sim_n, 40, 20, 50, 50, 4, 4)
r6 = reject_rate(sim_dat6)
r6.mean()
```

So now in this example you have an over 90% power to detect a crime reduction, going from 40 to 20 per time period (where the control has an average of 50 crimes per time period), if you have 4 pre time periods and 4 post time periods.

So a few caveats with this. For one, you may think that since dividing per time period reduces the variance, why not divide by smaller time slivers. So instead of one year, why not divide by 365 days?

I have not studied extensively this property of the estimator. So I cannot say how it behaves with more/less time aggregation into smaller Poisson estimates. You will need to take that on yourself if you want to examine very fine time units and very small Poisson counts per unit time. Again I think a baseline rule of thumb that they should not be lower the 5 counts per unit time is the best advice I can give without doing simulations for your exact circumstances.

A second part is that with longer time periods comes the risk that the control areas are not as good. This is a problem intrinsic to synthetic control analysis as well (that I don’t believe anyone has a particular answer to). And I don’t have an answer either.

For the pre-time period, you can check the parallel trends assumption by simply plotting the two time series, they should be close to in step with one another. So that is not a big deal. But with the post time period, I think if you monitor long enough they will eventually depart from one another.

So I think it is best to set up a time period at the start you have committed to doing the experiment. And you can use the power analysis simulations like I showed to help you figure out that period. But it may be possible to extend this WDD estimate to continuously monitor an intervention (see here for example).

]]>A few things occurred to prompt me to look into this. First, Chicago increased a big spike of homicides in 2016 and 2017. Here is a graph breaking them down between domestic related homicides and all other homicides. You can see all of the volatility is related to non-domestic homicides.

So this (at least to me) begs the question of whether those spiked homicides show similar characteristics compared to historical homicides. Here we focus on long term spatial patterns and micro place grid cells in the city, 150 by 150 meter cells. Dick & Carolyn Block had collated data, including the address where the body was discovered, using detective case notes starting in 1965 (ending in 2000). The data from 2000 through 2017 is the public incident report data released by Chicago PD online. Although Dick and Carolyn’s public dataset is likely well known at this point, Dick has more detailed data than is released publicly on ICPSR and a few more years (through 2000). Here is a map showing those homicide patterns aggregated over the entire long time period.

So we really have two different broad exploratory analyses we employed in the work. One was to examine homicide clustering, and the other was to examine temporal patterns in homicides. For clustering, we go through a ton of different metrics common in the field, and I introduce even one more, Theil’s decomposition for within/between neighborhood clustering. This shows Theil’s clustering metric *within* neighborhoods in Chicago (based on the entire time period).

So areas around the loop showed more clustering in homicides, but here it appears it is somewhat confounded with neighborhood size – smaller neighborhoods appear to have more clustering. This is sort of par for the course for these clustering metrics (we go through several different Gini variants as well), in that they are pretty fickle. You do a different temporal slice of data or treat empty grid cells differently the clustering metrics can change quite a bit.

So I personally prefer to focus on long term temporal patterns. Here I estimated group based trajectory models using zero-inflated Poisson models. And here are the predicted outputs for those grid cells over the city. You can see unlike prior work David Weisburd (Seattle), myself (Albany), or Martin Andresen (Vancouver) has done, they are much more wavy patterns. This may be due to looking over a much longer horizon than any of those prior works though have.

The big wave, Group 9, ends up being clearly tied to former large public housing projects, which their demolitions corresponds to the downturn.

I have an interactive map to explore the other trajectory groups here. Unfortunately the others don’t show as clear of patterns as Group 9, so it is difficult to answer any hard questions about the uptick in 2016/2017, you could find evidence of homicides dispersing vs homicides being in the same places but at a higher intensity if you slice the data different ways.

Unfortunately the analysis is never ending. Chicago homicides have again spiked this year, so maybe we will need to redo some analysis to see if the more current trends still hold. I think I will migrate away from the clustering metrics though (Gini and Theil), they appear to be too volatile to say much of anything over short term patterns. I think there may be other point pattern analysis that are more diagnostic to really understand emerging/changing spatial patterns.

The coffee next to the cover image is Chris Herrmann’s beans, so go get yourself some as well at Fellowship Coffee!

]]>Given the typical lags in the peer review process, if you look at my CV I will appear active in terms of publishing in 2020 (6 papers) and 2021 (4 papers and a book). But I have not worked on any peer review paper in earnest since I started working at HMS in December 2019, only copy-editing things I had already produced. (Which still takes a bit of work, for example my Cost of Crime hot spots paper took around 40 hours to respond to reviewers.)

At this point I am not sure if I will pursue any more peer reviewed publications directly in criminology/criminal justice. (Maybe as part of a team in giving support, but not as the lead.) Also we have discussed at my workplace pursuing publications, but that will be in healthcare related projects, not in Crim/CJ.

Part of the reason is that the time it takes to do a peer review publication is quite a bit relative to publishing a simple blog post. Take for instance my recent post on incorporating harm weights into the WDD test. I received the email question for this on Wednesday 11/18, thought about how to tackle the problem overnight, and wrote the blog post that following Thursday morning before my CrimCon presentation, (I took off work to attend the panel with no distractions). So took me around 3 hours in total. Many of my blog posts take somewhat longer, but I definitely do not take any more than 10-20 hours on an individual one (that includes the coding part, the writing part is mostly trivial).

I have attempted to guess as to the relative time it takes to do a peer reviewed publication based on my past work. I averaged around 5 publications per year, worked on average 50 hours a week while I was an academic, and spent something like I am guessing 60% to 80% (or more) of my time on peer review publications. Say I work 51 weeks a year (I definitely did not take any long vacations!, and definitely still put in my regular 50 hours over the summertime), that is `51*50=2550`

hours. So that means around `(2550*0.6)/5 ~ 300`

or `(2550*0.8)/5 ~ 400`

so an estimate of 300 to 400 hours devoted to an individual peer review publication over my career. This will be high (as it absorbs things like grants I did not get), but is in the ballpark of what I would guess (I would have guessed 200+).

So this is an average. If I had recorded the time, I may have had a paper only take around 100 hours (I don’t think I could squeeze any out in less than that). I have definitely had some take over 400 hours! (My Mapping RTM using Machine Learning I easily spent over 200 hours just writing computer code, not to brag, it was mostly me being inefficient and chasing a few dead ends. But that is a normal part of the research process.)

So it is hard for me to say, OK here is a good blog post that took me 3 hours. Now I should go and spend another 300 to write a peer review publication. Some of that effort to publish in peer review journals is totally legitimate. For me to turn those blog posts into a peer review article I would need a more substantive real-life application (if not multiple real-life applications), and perhaps detailed simulations and comparisons to other techniques for the methods blog posts. But a bunch is just busy work – the front end lit review and answering petty questions from peer reviewers is a very big chunk of that 300 hours (and has very little value added).

My blog posts typically get many more views than my peer review papers do, so I have very little motivation to get the stamp of approval for peer review. So my blog posts take far less time, are more wide read, and likely more accessible than peer reviewed papers. Since I am not on the tenure track and do not get evaluated by peer reviewed publications anymore, there is not much motivation to continue them.

I do have additional ideas I would like to pursue. Fairness and efficiency in siting CCTV cameras is a big one on my mind. (I know how to do it, I just need to put in the work to do the analysis and write it up.) But again, it will likely take 300+ hours for me to finish that project. And I do not think anyone will even end up using it in the end – peer reviewed papers have very little impact on policy. So my time is probably better spent writing a few blog posts and playing video games with all the extra time.

If you are an editor reading this, I still do quite a few peer reviews (so feel free to send me those). I actually have more time to do those promptly since I am not hustling writing papers! I have actually debated on whether it is worth it to start my own peer reviewed journal, or maybe contribute to editing an already existing journals (just joined the JQC editorial board). Or maybe start writing my own crime analysis or methods text books. I think that would be a better use of my time at this point than pursuing individual publications.

]]>To be clear what this model is, instead of the many time series examples floating around about changepoints (like the one in the Stan guide), we have a model with a particular continuous independent variable x, and we are predicting the probability of something based on that x variable. It is not that different, but many of those time series examples the universe to check for changepoints is obvious, only the observed time series locations. But here we have a continuous input (distance a crime event is from a CCTV camera), but we can only check a finite number of locations. It ends up being closer in spirit to this recent post by Keith Goldfield.

So in some quick and dirty text math, here `c`

is the changepoint location and `l`

is the logit function:

```
l(Prob[y]) = intercept + b1*x; if x <= c
l(Prob[y]) = intercept + b1*x + b2*(x - c); if x > c
```

This model can be expanded however you want – such as other covariates that do not change with the changepoint. But for this simple simulation I am just looking at the one running variable x and the binary outcome y.

So first, I load up the libraries I will be using, then I simulate some data. Here the changepoint is located at 0.42 for the x variable, and in the `ylogit`

line you can see the underlying logistic regression equation.

```
#################################
# Libraries I am using
import pystan
import numpy as np
import pandas as pd
import statsmodels.api as sm
#################################
#################################
# Creating simulated data
np.random.seed(10)
total_cases = 1000 #30000
x = np.random.uniform(size=total_cases) #[total_cases,1]
change = 0.42
xdif = (x - change)*(x > change)
ylogit = 1.1 + -4.3*x + 2.4*xdif
yprob = 1/(1 + np.exp(-ylogit))
ybin = np.random.binomial(1,yprob)
#################################
```

When testing out these models, one mistake I made was thinking offhand that 1,000 observations should be plenty. (Easier to run more draws with a smaller dataset.) When I had smaller effect sizes, the logistic coefficients could be pretty badly biased. So I started as a check estimating the logistic model inputting the correct changepoint location. Those biased estimates are pretty much the best case scenario you could hope for in the subsequent MCMC models. So here is an example fitting a logit model inputting the correct location for the changepoint.

```
#################################
#Statsmodel code to get
#The coefficient estimates
#And standard errors for the sims
con = [1]*len(x)
xcomb = pd.DataFrame(zip(con,list(x),list(xdif)),columns=['const','x','xdif'])
log_reg = sm.Logit(ybin, xcomb).fit()
print(log_reg.summary())
#################################
```

So you can see that my coefficient estimates and the frequentist standard errors are pretty large even with 1,000 samples. So I shouldn’t expect my later MCMC model to have any smaller credible intervals than above.

So here is the Stan model. I am using pystan here, but of course it would be the same text file if you wanted to fit the model using R. (Just compiles C++ code under the hood.) Of only real note is that I show how to use the softmax function to estimate the actual mean location of the changepoint. Note that that mean summary though only makes sense if you make your grid of changepoint locations regular and fairly fine. (So if you said a changepoint could be at 0.1, 0.36, and 0.87, taking a weighted mean of those three locations doesn’t make sense.)

```
#################################
#Stan model
changepoint_stan = """
data {
int<lower=1> N;
vector[N] x;
int<lower=0,upper=1> y[N];
int<lower=1> Samp_Points;
vector[Samp_Points] change;
}
transformed data {
real log_unif;
log_unif = -log(Samp_Points);
}
parameters {
real intercept;
real b_x;
real b_c;
}
transformed parameters {
vector[Samp_Points] lp;
real before;
real after;
lp = rep_vector(log_unif, Samp_Points);
for (c in 1:Samp_Points){
for (n in 1:N){
before = intercept + b_x*x[n];
after = before + b_c*(x[n] - change[c]);
lp[c] = lp[c] + bernoulli_logit_lpmf(y[n] | x[n] < change[c] ? before : after );
}
}
}
model {
intercept ~ normal(0.0, 10.0);
b_x ~ normal(0.0, 10.0);
b_c ~ normal(0.0, 10.0);
target += log_sum_exp(lp);
}
generated quantities {
vector[Samp_Points] prob_point;
real change_loc;
prob_point = softmax(lp);
change_loc = sum( prob_point .* change );
}
"""
#################################
```

And finally I show how to prepare the data for pystan (as a dictionary), compile the model, and then draw a ton of samples. I generate a regular grid of 0.01 intervals from 0.03 to 0.97 (can’t have a changepoint outside of the x data locations, which I drew as a random uniform 0,l). Note the more typical default of 1000 tended to not converge, the effective number of samples is quite small for that many. So 5k to 10k samples in my experiments tended to converge. Note that this is not real fast either, took about 40 minutes on my machine (the Stan guesstimates for time were always pretty good ballpark figures).

```
#################################
# Prepping data and fitting the model
stan_dat = {'N': ybin.shape[0]}
stan_dat['change'] = np.linspace(0.03,0.97,95) #[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]
stan_dat['Samp_Points'] = len(stan_dat['change'])
stan_dat['x'] = x
stan_dat['y'] = ybin
sm = pystan.StanModel(model_code=changepoint_stan)
#My examples needed more like 10,000 iterations
#effective sample size very low, took about 40 minutes on my machine
fit = sm.sampling(data=stan_dat, iter=5000,
warmup=500, chains=4, verbose=True)
#Prints some results at the terminal!
print(fit.stansummary(pars=['change_loc','intercept','b_x','b_c']))
#################################
```

So you can see the results – the credible intervals for the intercept and regression coefficient before the change point are not bad, just slightly larger than the logistic model. The credible interval for the changepoint location and the changepoint effect different are quite uncertain though. The changepoint location covers almost the whole interval I examined. It may be better to plot the individual probabilities, like Goldfield did in his post, as opposed to summarized a mean location for the distribution (which is discrete in the end based on your grid of locations you look at).

So that at least gives a partial warning that you need quite big data samples to effectively identify the changepoint location, at least for this Stan model as I have shown. I haven’t run it on my 26k actual data sample, as it will end up taking my computer around 30 hours to crunch out 10k draws with 4 chains. Next up I rather see if I can get a similar model working in pyro, as my GPU on my personal machine I think will be faster than the C++ code here. (There are probably smarter ways to vectorize the Stan model as well.)

]]>So I had a few questions about applying splines in generalized linear models and including control variables in my prior post (on a macro to estimate the spline terms). These include can you use them in different types of generalized linear models (yes), can you include other covariates into the model (yes). For either of those cases, interpreting the splines are more difficult though. I am going to show an example here of how to do that.

Additionally I have had some recent critiques of my paper on CCTV decay effects. One is that the locations of the knots we chose in that paper is arbitrary. So while that is true, one of the reasons I really like splines is that they are pretty robust – you can mis-specify the knot locations, and if you have enough of them they will tend to fit quite a few non-linear functions. (Also a note on posting pre-prints, despite being rejected twice and under review for around 1.5 years, it has over 2k downloads and a handful of citations. The preprint has more downloads than my typical published papers do.)

So here I am going to illustrate these points using some simulated data according to a particular logistic regression equation. So I know the true effect, and will show how mis-located spline knots still recovers the true effect quite closely. This example is in SPSS, and uses my macro on estimating spline basis.

So first in SPSS, I define the location where I am going to save my files. Then I import my Spline macro.

```
* Example of splines for generalized linear models
* and multiple variables.
DATASET CLOSE ALL.
OUTPUT CLOSE ALL.
* Spline Macro.
FILE HANDLE macroLoc /name = "C:\Users\andre\OneDrive\Desktop\Spline_SPSS_Example".
INSERT FILE = "macroLoc\MACRO_RCS.sps".
```

Second, I create a set of synthetic data, in which I have a linear changepoint effect at x = 0.42. Then I generate observations according to a particular logistic regression model, with not only the non-linear X effects, but also two covariates Z1 (a binary variable) and Z2 (a continuous variable).

```
*****************************************************.
* Synthetic data.
SET SEED = 10.
INPUT PROGRAM.
LOOP Id = 1 to 10000.
END CASE.
END LOOP.
END file.
END INPUT PROGRAM.
DATASET NAME Sim.
COMPUTE X = RV.UNIFORM(0,1).
COMPUTE #Change = 0.42.
DO IF X <= #Change.
COMPUTE XDif = 0.
ELSE.
COMPUTE XDif = X - #Change.
END IF.
COMPUTE Z1 = RV.BERNOULLI(0.5).
COMPUTE Z2 = RV.NORMAL(0,1).
DEFINE !INVLOGIT (!POSITIONAL !ENCLOSE("(",")") )
1/(1 + EXP(-!1))
!ENDDEFINE.
*This is a linear changepoint at 0.42, other variables are additive.
COMPUTE ylogit = 1.1 + -4.3*x + 2.4*xdif + -0.4*Z1 + 0.2*Z2.
COMPUTE yprob = !INVLOGIT(ylogit).
COMPUTE Y = RV.BERNOULLI(yprob).
*These are variables you won't have in practice.
ADD FILES FILE =* /DROP ylogit yprob XDif.
FORMATS Id (F9.0) Y Z1 (F1.0) X Z2 (F3.2).
EXECUTE.
*****************************************************.
```

Now like I said, the correct knot location is at `x = 0.42`

. Here I generate a set of regular knots over the x input (which varies from 0 to 1), at *not* the exact true value for the knot.

`!rcs x = X loc = [0.1 0.3 0.5 0.7 0.9].`

Now if you look at your dataset, there are 3 new `splinex?`

variables. (For restricted cubic splines, you get `# of knots - 2`

new variables, so with 5 knots you get 3 new variables here.)

We are then going to use those new variables in a logistic regression model. We are also going to save our model results to an xml file. This allows us to use that model to score a different dataset for predictions.

```
GENLIN Y (REFERENCE=0) WITH X splinex1 splinex2 splinex3 Z1 Z2
/MODEL X splinex1 splinex2 splinex3 Z1 Z2
INTERCEPT=YES DISTRIBUTION=BINOMIAL LINK=LOGIT
/OUTFILE MODEL='macroLoc\LogitModel.xml'.
```

And if we look at the coefficients, you will see that the coefficients look offhand very close to the true coefficients, minus splinex2 and splinex3. But we will show in a second that those effects should be of no real concern.

So you should do this in general with generalized linear models and/or non-linear effects, but to interpret spline effects you can’t really look at the coefficients and know what those mean. You need to make plots to understand what the non-linear effect looks like.

So here in SPSS, I create a new dataset, that has a set of regularly sampled locations along X, and then set the covariates `Z1=1`

and `Z2=0`

. These set values you may choose to be at some average, such as mean, median, or mode depending on the type of covariate. So here since Z1 can only take on values of 0 and 1, it probably doesn’t make sense to choose 0.5 as the set value. Then I recreate my spline basis functions using the exact sample macro call I did earlier.

```
INPUT PROGRAM.
LOOP #xloc = 0 TO 300.
COMPUTE X = #xloc/300.
END CASE.
END LOOP.
END FILE.
END INPUT PROGRAM.
DATASET NAME Fixed.
COMPUTE Z1 = 1.
COMPUTE Z2 = 0.
EXECUTE.
DATASET ACTIVATE Fixed.
*Redoing spline variables.
!rcs x = X loc = [0.1 0.3 0.5 0.7 0.9].
```

Now in SPSS, we score this dataset using our prior model xml file we saved. Here this generates the predicted probability from our logistic model.

```
MODEL HANDLE NAME=LogitModel FILE='macroLoc\LogitModel.xml'.
COMPUTE PredPr = APPLYMODEL(LogitModel, 'PROBABILITY', 1).
EXECUTE.
MODEL CLOSE NAME=LogitModel.
```

And to illustrate how close our model is, I generate what the true predicted probability should be based on our simulated data.

```
*Lets also do a line for the true effect to show how well it fits.
COMPUTE #change = 0.42.
DO IF X <= #change.
COMPUTE xdif = 0.
ELSE.
COMPUTE xdif = (X - #change).
END IF.
EXECUTE.
COMPUTE ylogit = 1.1 + -4.3*x + 2.4*xdif + -0.4*Z1 + 0.2*Z2.
COMPUTE TruePr = !INVLOGIT(ylogit).
FORMATS TruePr PredPr X (F2.1).
EXECUTE.
```

And now we can put these all into one graph.

```
DATASET ACTIVATE Fixed.
GGRAPH
/GRAPHDATASET NAME="graphdataset" VARIABLES=X PredPr TruePr
/FRAME INNER=YES
/GRAPHSPEC SOURCE=INLINE.
BEGIN GPL
SOURCE: s=userSource(id("graphdataset"))
DATA: X=col(source(s), name("X"))
DATA: PredPr=col(source(s), name("PredPr"))
DATA: TruePr=col(source(s), name("TruePr"))
GUIDE: axis(dim(1), label("X"))
GUIDE: axis(dim(2), label("Prob"))
SCALE: cat(aesthetic(aesthetic.shape), map(("PredPr",shape.solid),("TruePr",shape.dash)))
ELEMENT: line(position(X*PredPr), shape("PredPr"))
ELEMENT: line(position(X*TruePr), shape("TruePr"))
END GPL.
```

So you can see that even though I did not choose the correct knot location, my predictions are nearly spot on with what the true probability should be.

So in practice you can do more complicated models with these splines, such as allowing them to vary over different categories (e.g. interactions with other covariates). Or you may simply want to generate predicted plots such as above, but have a varying set of inputs. Here is an example of doing that; for Z1 we only have two options, but for Z2, since it is a continuous covariate we sample it at values of -2, -1, 0, 1, 2, and generate lines for each of those predictions.

```
*****************************************************.
* Can do the same thing, but vary Z1/Z2.
DATASET ACTIVATE Sim.
DATASET CLOSE Fixed.
INPUT PROGRAM.
LOOP #xloc = 0 TO 300.
LOOP #z1 = 0 TO 1.
LOOP #z2 = -2 TO 2.
COMPUTE X = #xloc/300.
COMPUTE Z1 = #z1.
COMPUTE Z2 = #z2.
END CASE.
END LOOP.
END LOOP.
END LOOP.
END FILE.
END INPUT PROGRAM.
DATASET NAME Fixed.
EXECUTE.
DATASET ACTIVATE Fixed.
*Redoing spline variables.
!rcs x = X loc = [0.1 0.3 0.5 0.7 0.9].
MODEL HANDLE NAME=LogitModel FILE='macroLoc\LogitModel.xml'.
COMPUTE PredPr = APPLYMODEL(LogitModel, 'PROBABILITY', 1).
EXECUTE.
MODEL CLOSE NAME=LogitModel.
FORMATS Z1 Z2 (F2.0) PredPr X (F2.1).
VALUE LABELS Z1
0 'Z1 = 0'
1 'Z1 = 1'.
EXECUTE.
*Now creating a graph of the predicted probabilities over various combos.
*Of input variables.
DATASET ACTIVATE Fixed.
GGRAPH
/GRAPHDATASET NAME="graphdataset" VARIABLES=X PredPr Z1 Z2
/FRAME INNER=YES
/GRAPHSPEC SOURCE=INLINE.
BEGIN GPL
SOURCE: s=userSource(id("graphdataset"))
DATA: X=col(source(s), name("X"))
DATA: PredPr=col(source(s), name("PredPr"))
DATA: TruePr=col(source(s), name("TruePr"))
DATA: Z1=col(source(s), name("Z1"), unit.category())
DATA: Z2=col(source(s), name("Z2"), unit.category())
COORD: rect(dim(1,2), wrap())
GUIDE: axis(dim(1), label("X"))
GUIDE: axis(dim(2), label("Predicted Probability"))
GUIDE: axis(dim(3), opposite())
GUIDE: legend(aesthetic(aesthetic.color), label("Z2"))
SCALE: cat(aesthetic(aesthetic.color), map(("-2",color."8c510a"),("-1",color."d8b365"),
("0",color."f6e8c3"), ("1",color."80cdc1"), ("2",color."018571")))
ELEMENT: line(position(X*PredPr*Z1), color(Z2))
END GPL.
*****************************************************.
```

So between all of these covariates, the form of the line does not change much (as intended, I simulated the data according to an additive model).

If you are interested in drawing more lines for Z2, you may want to use a continuous color scale instead of a categorical one (see here for a similar example).

]]>For an example of where there can be potential ambiguity even with the probability surface, in the surface below we have three hot spots. So if we have four people to search this area, and they can only search a finite connected area (so no hop-scotching around), should we have them split between each of the hot spots, or should they cover one of the hot spots in more detail. (It is hard to tell in my graph, but the hot spot in the central western part of the graph has a higher hump, but is steeper, so top right has more mass but is more spread out.)

I’ve actually failed to be able to generate a decent algorithm to do this though. It is similar to this prior work of mine, but I actually discovered some errors in that work in trying to apply it to this situation (can have disconnected subtours that are complicated paths). So attempted several other variants and have yet to come up with a decent solution.

I tried out a greedy algorithm to solve the problem (pick the highest hump, march like an ant around until you have covered your max tour, and then start again). But this was not good either. But it generated some interesting accidental art. So here is my greedy approach to pick four tours in which they can traverse 300 grid cells, and here it says better to ignore the bottom hotspot and spread around your effort in the other two areas:

I know this is pretty sup-optimal though, as you can continue to generate more tours through this approach and eventually find better ones.

This is going to bug me forever now, but posting a blog to move on. So if you know of a solution please fill me in!

]]>The DT article I don’t think *per se* gives bad advice. Use an outline? Golly I suggest students do that too! Be comprehensive in your lit review about covering all relevant work? Well who can argue with that!

I think an important distinction to make in the advice DT give is the distinction between functional actions and symbolic actions. Functional in this context means an action that makes the article better accomplish some specific function. So for example, if I say you should translate complicated regression models to more intuitive marginal effects to make your results more interpretable for readers, that has a clear function (improved readability).

Symbolic actions are those that are merely intended to act as a signal to the reader. So if the advice is along the lines of, you should do this to pass peer review, that is on its face symbolic. DT’s article is nearly 100% about taking symbolic actions to make peer reviewers happy. Most of the advice doesn’t actually improve the content of the manuscript (or in the most charitable interpretation *how* it improves the manuscript is at best implicit). In DT’s section *Why is it important* this focus on symbolic actions becomes pretty clear. Here is the first paragraph of that section:

Literature reviews are important for a number of reasons. Primarily, literature reviews force a writer to educate him/herself on as much information as possible pertaining to the topic chosen. This will both assist in the learning process, and it will also help make the writing as strong as possible by knowing what has/has not been both studied and established as knowledge in prior research. Second, literature reviews demonstrate to readers that the author has a firm understanding of the topic. This provides credibility to the author and integrity to the work’s overall argument. And, by reviewing and reporting on all prior literature, weaknesses and shortcomings of prior literature will become more apparent. This will not only assist in finding or arguing for the need for a particular research question to explore, but will also help in better forming the argument for why further research is needed. In this way, the literature review of a research report “foreshadows the researcher’s own study” (Berg, 2009, p. 388).

So the first argument, a lit review forces a writer to educate themselves, may offhand seem like a functional objective. It doesn’t make sense though, as lit. reviews are almost always written ex post research project. The point of writing a paper is not to educate yourself, but educate other people on your research findings. The symbolic motivation for this viewpoint becomes clear in DT’s second point, you need to demonstrate credibility to your readers. In terms of integrity if the advice in DT was ‘consider creating a pre-analysis plan’ or ‘release data and code files to replicate your results’ that would be functional advice. But no, it is important to wordsmith how smart you are so reviewers *perceive* your work as more credible.

Then the last point in the paragraph, articulating the need for a particular piece of research, is again a symbolic action in DT’s essay. You are arguing to peer reviewers about the need for a particular research question. I understand the spirit of this, but think back to what function does this serve? It is merely a signal to reviewers to say, given finite space in a journal, please publish my paper over some other paper, because my topic is more important.

You actually don’t need a literature review to demonstrate a topic is important and/or needed – you can typically articulate that in a sentence or two. For a paper I reviewed not too long ago on crime reductions resulting from CCTV installations in a European city, I was struck by another reviewers critique saying that the authors “never really motivate the study relative to the literature”. I don’t know about you, but the importance of that study seems pretty obvious to me. But yeah sure, go ahead and pad that citation list with a bunch of other studies looking at the same thing to make some peer reviewers happy. God forbid you simply cite a meta-analysis on prior CCTV studies and move onto better things.

So again I don’t think DT give bad advice – mostly vapid but not obviously bad. DT focus on symbolic actions in lit reviews because as lit reviews are currently performed in CJ/Crim journals, they are almost 100% symbolic. They serve almost no functional purpose other than as a signal to reviewers that you are part of the club. So DT give about the best advice possible navigating a series of arbitrary critiques with no clear standard.

As an example for this position that lit reviews accomplish practically nothing, conduct this personal experiment. The next peer review article you pick up, do not read the literature review section. Only read the abstract, and then the results and conclusion. Without having read the literature review, does this change the validity of a papers findings? It for the most part does not. People get feelings hurt by not being cited (including myself), but even if someone fails to cite some of my work that is related it pretty much never impacts the validity of that persons findings.

So DT give advice about how peer review works now. No doubt those symbolic actions are important to getting your paper published, even if they do not improve the actual quality of the manuscript in any clear way. I rather address the question about what *I think* a lit review should look like – not what you should do to placate three random people and the editor. So again I think the best way to think about this is via articulating specific functions a lit review accomplishes in terms of improving the manuscript.

Broadening the scope abit to consider the necessity of citations, the majority of citations in articles are perfunctory, but I don’t think people should plagiarize. So when you pull a very specific piece of information from a source, I think it is important to cite that work. Say you are using a survey instrument developed by someone else, citing the work that establishes that instruments reliability and validity, as well as the original population those measures were established on, is certainly useful information to the reader. Sources of information/measures, a recent piece saying the properties of your statistical model are I think other good examples of things to cite in your work. Unfortunately I cannot give a bright line here, I don’t cite Gauss every time I use the normal distribution. But if I am using a code library someone else developed that is important, inasmuch as that if someone wants to do a similar project they could use the same library.

In terms of discussing relevant results in prior studies, again the issue is the boundary of what is relevant is very difficult to articulate. If there is a relevant meta-analysis on a topic, it seems sufficient to me to simply state the results of the meta-analysis. Why do I think that is important though? It helps inform your priors about the current study. So if you say a meta-analysis effect size is X, and the current study has an effect size much larger, it may give you pause. It is also relevant if you are generalizing from the results of the study, it is just another piece of evidence in addition to the meta-analysis, not an island all by itself.

I am not saying discussing prior specific results are not needed entirely, but they do not need to be extensive. So if studies Z, Y, X are similar to yours but all had null results, and you think it was because the sample sizes were too small, that is relevant and useful information. (Again it changes your priors.) But it does not need to be belabored on in detail. The current standard of articulating different theoretical aspects ad-nauseum in Crim/CJ journals does not improve the quality of manuscripts. If you do a hot spots policing experiment, you do not need to review all the different minutia of general deterrence theory. Simply saying this experiment is likely to only accomplish general deterrence, not specific deterrence, seems sufficient to me personally.

When you propose a book you need to say ‘here are some relevant examples’ – I think the same idea would be sufficient for a lit review. OK here is my study, here are a few additional studies I think the reader may be interested in that are related. This accomplishes what contemporary lit reviews do in a much more efficient manner – citing more articles makes it much more difficult to pull out the really relevant related work. So admit this does not improve the quality of the current manuscript in a specific way, but helps the reader identify other sources of interest. (I as a reader typically go through the citation list and note a few articles I am interested in, this helps me accomplish that task much quicker.)

I’ve already sprinkled a few additional pieces of advice in this blog post (marginal effect estimates, pre-analysis plans, sharing data code), although you may say they don’t belong in the lit review. Whatever, those are things that actually improve either the content of the manuscript or the actual integrity of the research, not some spray paint on your flowers.

- Rossmo, D.K. (2015). Short and Sweet: A Call for Concise Journal Articles.
*The Criminologist*40(2): 8-10. - Blog Post (by yours truly), on
*Co-Author Networks in Criminology*, in which I show the increase in citations over time in Crim/CJ journals. - Blog post by Phil Cohen, Our broken peer review system, in one saga, in which Phil discusses how Sociologists focus far too much on the framing of articles (which my experience in criminology is pretty much the same).

So in spatial criminology, a popular hypothesis is estimating distance decay effects. Ratcliffe (2012) was the first example of using a changepoint regression model to do this, showing a changepoint in the effect of bars on the spatial density of crime nearby. This has been replicated in Xu & Griffiths (2017), and in my work using machine learning and partial dependence plots I show similar changepoint patterns as well (Wheeler & Steenbeek, 2020).

One example use case though I want to mention is not in terms of estimating the spatial density of crime, but with the characteristics of the crime events themselves. Sometimes people I think mistakenly think since I have spatial data, I need to aggregate it to some areal unit, and then do analysis of that areal unit data. That approach is not per-se wrong, but is sometimes a step removed from what you want, and can result in some tricky inferences.

Take for example a recent paper looking at clearances and using RTM by Kennedy et al. (2020). What they do is spatially aggregate homicides cleared and homicides not cleared, and run RTM on each. You might be tempted to interpret if a factor is selected for both models that it does not impact clearances, but it also depends on the size of the effect. So for example, in Brooklyn for drug markets they report a rate ratio of 3.1 and 2.4 (both at the same spatial distance). To translate this into a clearance rate, you need to add the two density estimates for all cases, and then take the cleared cases as the numerator.

```
# Example R code
clear <- exp(-0.1 + log(3.1))
nonclear <- exp(-0.1 + log(2.4))
prop <- clear/(clear + nonclear)
prop #0.5636364
```

Here I am treating `-0.1`

as the intercept. So here this is lower, but close to the overall clearance in Brooklyn, 58%. This 56% will be the estimate iff the intercept for each equation is the same, if they are not though it could change the clearance rate estimate either way. Since the Kennedy paper did not report this, we cannot know. So for instance, if we change the intercept estimates so clearances are higher and non-clearances are lower, we get an estimate that drug markets *increase* clearances slightly, not decrease them:

```
clear <- exp(-0.05 + log(3.1))
nonclear <- exp(-0.2 + log(2.4))
prop <- clear/(clear + nonclear)
prop #0.6001124
```

In this example it probably won’t push them too far either way, but takes a bit of work going from the aggregate data analysis to the estimate we want, how those spatial risk factors impact the clearance rate. There is an easier way though – just incorporate your spatial features, such as the distance the nearest crime generator factor, and estimate a model on the micro level incident data. This is what Kennedy et al. (2020) do later in the paper when incorporating the RTM predictions – I just think they should have done the RTM machinery directly on this problem, instead of the two-step approach.

Examples of my work I have done this approach in the past (incorporating spatial data into the micro level incidents) is with fatalities from gun shot wounds (Circo & Wheeler, 2020). We actually investigated non-linear effects though of distance/drive-time, and did not find evidence of that. Going back to the crime clearance example though, another pre-print I examine the effects of CCTV cameras and find a diminishing effect of case clearances given the distance to the camera (Jung & Wheeler, 2019).

So here we use a pre-post design to show there are some selection effects, and we do further analysis to show this camera bump in clearances is only limited to thefts. But we set the splines at 500, 1000, and 1500 feet pre-emptively for the analysis. A reviewer critique of this is that those three locations are arbitrary (which is correct), so here I will see if I do a changepoint model that allows us to find the knot locations if it will show the same ones.

The idea behind this analysis is that CCTV are often used in investigations. Yeondae is an officer in Korea, same as here in the states first things detectives do is to go and grab CCTV footage. Analysis of cameras are often aggregated to their viewsheds, but I think estimating distance decay effects make as much sense. So events closer to the cameras presumably will provide more clear evidence than events at the border of the viewshed. A second point is that even if the event takes place off-camera, there may be evidence cross by the camera viewshed. Detectives will often try to follow individuals across multiple cameras. So both of those factors suggest a distance decay effect both within a cameras viewshed and a decaying effect even outside of the viewshed. (In addition to this, geo coordinates of crime locations are not perfectly accurate measures either, so that could cause effects outside of the viewshed as well.)

Here I am just limiting the data to the post camera data within 3000 feet for thefts, which still is over 26,000 observations. I’ve posted the data/code to follow along here.

Again given my hardship in coding this up myself in python, I created a simulated data example and checked the results using mcp (which you can check in my code). Since mcp recovered my simulated changepoint, (and my python attempts did not), going to go ahead with the mcp library! First, we will import my clearance data and get rid of a few missing cases.

```
#################
library(mcp)
library(ggplot2)
set.seed(10)
#can see I planned on doing this in pytorch at first!
setwd('D:\\Dropbox\\Dropbox\\Documents\\BLOG\\changepoint_pytorch\\Analysis')
theft_clear <- read.csv('PostTheft_CCTV.csv')
theft_clear <- theft_clear[complete.cases(theft_clear),]
#################
```

So first for a reference, if I *assume* there is a linear changepoint at 1000 feet, here are what my results look like. Note here that this is not aggregated data to spatial locations, each row in this dataset is a theft offense, whether it was cleared, and the distance to the nearest CCTV camera.

```
#################
#What are the coefficients if assume a changepoint of 1000 feet
theft_clear$x_dif <- (theft_clear$CAM.DIST - 1000)*(theft_clear$CAM.DIST > 1000)
theft_mod <- glm(formula = 'STATUSi ~ CAM.DIST + x_dif', family = "binomial", data = theft_clear)
summary(theft_mod) #This gives an estimate of
#################
```

And here you can visualize the results alittle easier than trying to back out probabilities for the regression equation:

```
#################
pred_mod <- predict(theft_mod,type='response')
plot(theft_clear$CAM.DIST,pred_mod, main="Changepoint at 1000 ft",
xlab="Distance from Camera (ft)", ylab="Probability Clearance")
#################
```

So this shows clearances nearby cameras in Dallas are around 15%, and they trail off to around 9% at 1000 feet. After that they continue to tail off, but are nearly flat. But again that is *assuming* a change point at 1000 feet. But the mcp package lets us actual estimate the changepoint itself using Bayesian regression. Here is the set up that is equivalent to my formulation earlier, in that the changepoint cannot be discontinuous.

```
#################
theft_clear$x <- theft_clear$CAM.DIST
model = list(
STATUSi | trials(const) ~ 1 + x,
~ 0 + x #joined changing rate
)
fit = mcp(model, data = theft_clear, family = binomial(), iter = 3000, adapt = 500)
#################
```

And then if you are following along you can go ahead and take a nap (maybe took 2 hours on my machine?), and when we get back `summary(fit)`

gives us:

So we have very similar coefficients to the manual changepoint model earlier, but the changepoint is around 1600 feet, not 1000. (Although note these are Bayesian credible intervals, not frequentist confidence intervals.) And now to make a nice plot of the fitted model.

```
#Fitted values for new data
newdat <- data.frame(x = (0:300)*10)
newdat$const <- 1
newdat$CAM.DIST <- newdat$x
res <- fitted(fit, newdata = newdat)
p_pred <- ggplot(data=res) +
geom_line(size=1.2, color='black', aes(x = x, y = fitted)) +
geom_ribbon(alpha=0.5, fill='black', aes(x = x, ymin=Q2.5 , ymax=Q97.5)) +
scale_x_continuous(name="Feet from Camera",breaks=seq(0,3000,500),minor_breaks=NULL) +
scale_y_continuous(name="P(Clearance)",breaks=seq(0.06,0.16,0.02),minor_breaks=NULL) +
theme_bw() + theme(panel.grid.major = element_line(colour = 'grey', linetype = 'dashed', size=0.1)) +
theme(text = element_text(size=20))
p_pred
```

So you can see that here it is a nearly linear drop off until 1600 feet, and then starts to climb back up. The climb up *I think* is likely due to selection effects, but we can’t 100% rule out displacement effects. Displacement effects *could* occur with cameras if detectives prioritize events around cameras and de-prioritize other events not nearby cameras. Skeptical that applies to thefts in Dallas though, as they very rarely will be assigned a detective at all.

So this ended up taking me for a few different turns. One of the things I wanted to be able to test multiple changepoints, maybe if I can ever get pymc3 to give me a reasonable fit, this example is a good illustration. That should also maybe say if you should have no changepoint as well. I think maybe it is much harder to fit those models with binomial data though than with continuous (maybe good for another blog post as well, did simulations at first with 1000 observations and that was a bad idea).

One thing that would be good for evaluating whether change points are reasonable are out of sample predictive comparisons. So say estimate a no changepoint model, a linear changepoint model, and then a model with fixed spline locations. Then see which of those better fits the out of sample data. But since this is a blog post, will leave it as is. But this is a simple illustration to extend prior spatial analysis of changepoints in distance decay effects to one example – crime clearances and CCTV cameras – that I think makes alot of sense.

- Circo, G. M., & Wheeler, A. P. (2020). Trauma Center Drive Time Distances and Fatal Outcomes among Gunshot Wound Victims.
*Applied Spatial Analysis and Policy*, Online First. - Kennedy, L. W., Caplan, J. M., Piza, E. L., & Thomas, A. L. (2020). Environmental Factors Influencing Urban Homicide Clearance Rates: A Spatial Analysis of New York City.
*Homicide Studies*, Online First. - Lindeløv, J. K. (2020).
*mcp: An R Package for Regression With Multiple Change Points*. Preprint, OSF. - Jung, Y., & Wheeler, A. (2019).
*The effect of public surveillance cameras on crime clearance rates*. Preprint, OSF. - Ratcliffe, J. H. (2012). The spatial extent of criminogenic places: a changepoint regression of violence around bars.
*Geographical Analysis*, 44(4), 302-320. - Wheeler, A. P., & Steenbeek, W. (2020). Mapping the risk terrain for crime using machine learning.
*Journal of Quantitative Criminology*, Online First. - Xu, J., & Griffiths, E. (2017). Shooting on the street: Measuring the spatial influence of physical features on gun violence in a bounded street network.
*Journal of Quantitative Criminology*, 33(2), 237-253.