# Writing equations in Microsoft Word

A student asked me about using LaTex the other day, and I stated that it is a bit of a hassle for journal articles in our field, so I have begun to use it less. Most of the journals in my field (criminology and criminal justice) make it difficult to turn in an article in that format. Many refuse to accept PDF articles outright, and last time I submitted a LaTex file to JQC (a Springer journal) that would not compile I received zero help from staff over a month of emails, so I just reformatted it to a Word document anyway. Last time I submitted a LaTex document to Criminology a reviewer said it probably had typos — without pointing out any of course. (FYI folks, besides doing the obvious and pointing out typos if they exist, my text editor has a spell checker same as Word to highlight typos.) Besides this, none of my co-workers use LaTex, so it is a non-starter for when I am collaborating. I did my dissertation in LaTex, and I would do that in LaTex again, but smaller articles are not a big deal.

The main nicety of LaTex are math equations. I don’t do too heavy of math stuff, and I have figured out the Microsoft Word equation editor enough to suit most of my needs. So here are a set of examples for many of the use cases I have needed to use in journal articles. I also have this in a Word (docx) document and a PDF for handy reference. Those have a few references I gathered from the internet, but the best IMO is this guys blog (who I think is a developer for Word) and this document authored by the same individual.

One of the things to note about the equation editor in Word is that you can type various shortcuts and then they will be automatically converted. For example, you can type \gamma, hit the space bar, and then the equation will actually change to showing the gamma symbol. So there are some similarities to LaTex. (Another pro-tip, to start an equation in Word you can press Alt=.) In the subsequent examples I will use <space> to represent hitting the space bar, and there are other examples of using <back> (for the left arrow key) and <backspace> for the backspace button.

## Greek characters, subscripts and superscripts

If you type

log<space>(\lambda) = \beta_0<space> + \beta_1<space>(X) + \beta_2<space>(X^2<space>)

you get:

## Accents

For these you need to hit the space key twice, so

x\hat<space><space> = y\bar<space><space>

turns into:

## Expected value and variance

For the equivalent of \mathbb in LaTex, you can do

\doubleV<space>(X)= \doubleE<space>(X)^2<space> + \doubleE<space>(X^2<space>)

## Plain text within equation

To do plain text within an equation, equivalent to \text{*} in LaTex, you can use double quotes. (Note that you do not need a backslash before "log".) So

Y = -1\cdot<space>log<space>("Property Crime"<space>) + (not pretty text)

looks like:

## Sum and product

To get the product symbol is simply \prod<space>, and here is a more complicated example for the sum:

n^-1\cdot<space>\sum^n_(i=1)<space>x_i<space>= x\bar<space><space>

## Square root

Square roots always cause me trouble for how they look and kern (both in LaTex and Word). Here is how I would do an example of Euclidean distance,

d_ij<space>=\sqrt<space><space><back>(x_i-x_j)^2+(y_i-y_j)^2<space>

## Fractions

The big (stacked) fraction is simple, but I had to search for a bit to find how to do inline fractions (what Word calls "linear"). So here back slash followed by forward slash does the inline fraction:

1/n = 1\/n

## Numbering an equation

I’ve seen quite a few different hacks for numbering equations in Word. If you need to number and refer to them in text often, I would use LaTex. But here is one way to do it in Word.

E = mc^2#(30)<enter>

produces below (is it just me or does this make the equation look different than the prior ones in Word?):

## Multiple lines of equations

For a while I did not think this was possible, but I recently found examples of multiline equations (equivalent to \align in Latex). The way this works is you place a & sign before the symbols you want to line up (same as LaTex), but for Word to split a line you use @. So if you type

\eqarray(10x&=4y@5x&=2y)\eqarray<space><backspace>

you will get:

Have any more good examples? Let me know in the comments!

The other nicety of LaTex is formatting references — you are on your own though for that in Word though.

I’ve written quite a few blog posts over the years, and it is getting to be hard for me to keep all of them orderly. So I recently created a page with a simple table of contents with code snippets broken down into categories. There are also a few extra SPSS macros in there that I have not written blog posts about.

Every now and then I see a broken link and update it, but I know there are more I am missing. So just send an email if I have a link to some of my code and it is currently broken.

# Regression to the mean – a tale of change scores

This is a real example from my work illustrating regression to the mean. I have a scale measuring impulsivity of offenders. I had an intervention that used cognitive behavioral thereapy (CBT) in a boot camp for one group, and business as usual for another (just plain old jail). I have measures of impulsivity at pre, post, and 6 month follow up (what I label as post2). CBT is suppossed to reduce impulsivity, and hopefully keep it that way.

I find that those who have gained the most during the intervention tend to revert back to their prior scores once they leave the bootcamp. That is, the measure [post - pre], the gain in bootcamp, has a negative correlation with [post2 - post], the loss after bootcamp. Is this due to the intervention being shitty? No! It is not — this is the result of regression to the mean. This does not show any relationship between the values, it will happen even if the impulse scores are totally random.

Note that the definition of covariance is:

Cov(X,Y) = E[(x - E[X])*(y - E[Y])]

Where E is representing the expectation, and Cov(X,Y) of course means the covariance between X and Y. Here for easier equations we can assume the mean in the impulse scale is zero across all three waves, which makes the means of the change scores zero as well (without any loss in generality). So dropping the inner expecations this equation reduces to:

Cov(X,Y) = E[x*y]

So defining post-pre = Change1 and post2 - post = Change2, expanding out to the original components we have:

Cov(Change1,Change2) = Cov(post-pre,post2-post) = E[ (post-pre)*(post2-post) ]

The last result can then be expanded to:

E[ post*post2 - post*post - pre*post2 + pre*post ]

Because of the bilinearity of expectation, these can be further teased out:

E[ post*post2 ] - E[ post*post ] - E[ pre*post2 ] + E[ pre*post]

Note we can rewrite this back into variances and covariances of the original levels:

Cov(post,post2) - Var(post) - Cov(pre,post2) + Cov(pre,post)

There are two things to note here. 1) The covariances in the change scores can be entirely written as functions in the covariances of the levels. They do not supply information independent of the levels themselves.

For 2), if the data are random (that is the covariances between all the levels are random), the covariances between the change scores will be negative. This is because of the minus sign in front of the variance of the post term. For random data, all the other covariances are zero. This results in the correlation between the change scores being -1/2.

For a simple example in R:

> set.seed(10)
> n <- 10000 #sample size
> t1 <- rnorm(n) #three random vectors
> t2 <- rnorm(n)
> t3 <- rnorm(n)
> levels <- data.frame(t1,t2,t3)
> differ <- data.frame(c1=t2-t1,c2=t3-t2)
>
> #correlations in levels are approximately zero
> cor(levels)
t1           t2            t3
t1  1.0000000000  0.001874345 -0.0007006367
t2  0.0018743450  1.000000000 -0.0045967380
t3 -0.0007006367 -0.004596738  1.0000000000
>
> #correlation of differences is -0.5
> cor(differ)
c1         c2
c1  1.0000000 -0.4983006
c2 -0.4983006  1.0000000

Sometimes I see people talk about regression to the mean as if it is a sociological thing, like something that needs to be explained in terms of human behavior. It is not, it is entirely mathematical.

This is also one of the reasons I don’t like using change scores, either as independent or dependent variables. They typically can be rewritten in terms of the levels, and involve coeffficient restrictions that can have strange consequences. There are some situations (fixed effects) that make sense for the dependent variable. I haven’t seen a situation in the terms of independent variables where they make sense.

# New course – Advanced Methods in Criminology

This fall I am teaching a PhD course, the course is listed as Crim 7301 – Seminar in Criminology Research and Analysis. The link to UT Dallas’s coursebook description is here, and I have placed a page on my blog that contains the syllabus. My blog is just nicer, since I can include more info. than I can directly on UTD’s page, as well as update material as I go.

The description behind the course in UT Dallas is pretty open, but I am mostly motivated to design a course to go over the regular quasi-experimental research designs I encounter most often in practice. Students also have a lab component which entails actually conducting such data analysis, with code snippets mostly in SPSS, R, and Stata.

For this, as well as other course materials if you cannot access Dropbox links feel free to email me and I will send the material directly.

As with many graduate level methods courses, it is heavily influenced by my personal experience. But I am open to suggestions in the future if you want a particular topic covered. For example I debated on including missing data analysis (e.g. multiple imputation, full information max. likelihood) for a week. If there was interest in that (or some other topic) I could definitely update the syllabus. Just come by for a chat or send me an email with your suggestion.

In general for students, if you have questions or would like my input on project ideas feel free to stop by. My current posted office hours are Tuesday and Friday, 9 to 11 am, but I am at my office during normal work hours for the whole week. (Knock if the door is closed, I am often in here.)

I’ve updated the roadblocks in Buffalo manuscript due to a rejection and subsequent critiques. So be prepared about my complaints of the peer-review!

I’ve posted the original manuscript, reviews and a line-by-line response here. This was reviewed at Policing: An International Journal of Police Strategies & Management. I should probably always do this, but I felt compelled to post this review by the comically negative reviewer 1 (worthy of an article on The Allium).

The comment of reviewer 1 that really prompted me to even bother writing a response was the critique of the maps. I spend alot of time on making my figures nice and understandable. I’m all ears if you think they can be improved, but best be prepared for my response if you critique something silly.

So here is the figure in question – spot anything wrong?

The reviewer stated it did not have legend, so it does not meet "GIS standards". The lack of a legend is intentional. When you open google maps do they have a legend? Nope! It is a positive thing to make a graphic simple enough that it does not need a legend. This particular map only has three elements: the outline of Buffalo, the streets, and the points where the roadblocks took place. There is no need to make a little box illustrating these three things – they are obvious. The title is sufficient to know what you are looking at.

Reviewer 2 was more even keeled. The only thing I would consider a large problem in their review was they did not think we matched comparable control areas. If true I agree it is a big deal, but I’m not quite sure why they thought this (sure balance wasn’t perfect, but it is pretty close across a dozen variables). I wouldn’t release the paper if I thought the control areas were not reasonable.

Besides arbitrary complaints about the literature review this is probably the most frustrating thing about peer-reviews. Often you will get a list of two dozens complaints, with most being minor and fixable in a sentence (if not entirely arbitrary), but the article will still be rejected. People have different internal thresholds for what is or is not publishable. I’m on the end that even with worts most of the work I review should still be published (or at least the authors given a chance to respond). Of the 10 papers I’ve reviewed, my record is 5 revise-and-resubmits, 4 conditional accepts, and 1 rejection. One of the revise-and-resubmits I gave a pretty large critique of (in that I didn’t think it was possible to improve the research design), but the other 4 would be easily changed to accept after addressing my concerns. So worst case scenario I’ve given the green light to 8/10 of the manuscripts I’ve reviewed.

Many reviewers are at the other end though. Sometimes comically so, in that given the critiques nothing would ever meet their standards. I might call it the golden-cow peer review standard.

Even though both of my manuscripts have been rejected from PSM, I do like their use of a rubric. This experience makes me wonder what if the reviewers did not give a final reject-accept decision – just the editors took the actual comments and made their own decision. Editors do a version of this currently, but some are known to reject if any of the reviewers give a rejection no matter what the reviewers actually say. It would force the editor to use more discretion if the reviewers themselves did not make the final judgement. It also forces reviewers to be more clear in their critiques. If they are superficial the editor will ignore them, whereas the final accept-reject is easy to take into account even if the review does not state any substantive critiques.

I don’t know if I can easily articulate what I think is a big deal and what isn’t though. I am a quant guy, so the two instances I rejected were for model identification in one and for sample selection biases in the other. So things that could not be changed essentially. I haven’t read a manuscript that was so poor I considered it to be unsalvagable in terms of writing. (I will do a content analysis of reviews I’ve recieved sometime, but almost all complaints about the literature review are arbitrary and shouldn’t be used as reasons for rejection.)

Often times I write abunch of notes on the paper manuscript my first read, and then when I go to write up the critique specifically I edit them out. This often catches silly initial comments of mine, as I better understand the manuscript. Examples of silly comments in the reviews of the roadblock paper are claiming I don’t conduct a pre-post analysis (reviewer 1), and asking for things already stated in the manuscript (reviewer 2 asking for how long the roadblocks were and whether they were "high visibility"). While it is always the case things could be explained more clearly, at some point the reviewer(s) needs to be more careful in their reading of the manuscript. I think my motto of "be specific" helps with this. Being generic helps to conceal silly critiques.

# Plotting panel data with many lines in SPSS

A quick blog post – so you all are not continually assaulted by my mug shot on the front page of the blog!

Panel data is complicated. When conducting univariate time series analysis, pretty much everyone plots the series. I presume people do not do this often for panel data because the charts tend to be more messy and less informative. But by using transparency and small multiple plots are easy places to start to unpack the information. Here I am going to show these using plots of arrest rates from 1970 through 2014 in New York state counties. The data and code can be downloaded here, and that zip file contains info. on where the original data came from. It is all publicly available – but mashing up the historical census data for the population counts by county is a bit of a pain.

So I will start with grabbing my created dataset, and then making a default plot of all the lines. Y axis is the arrest rate per 1,000 population, and the X axis are years.

*Grab the dataset.
FILE HANDLE data /NAME = "!!Your File Handle Here!!!".
GET FILE = "data\Arrest_WPop.sav".
DATASET NAME ArrestRates.

*Small multiple lines over time - default plot.
GGRAPH
/GRAPHDATASET NAME="graphdataset" VARIABLES=Year Total_Rate County
/GRAPHSPEC SOURCE=INLINE.
BEGIN GPL
SOURCE: s=userSource(id("graphdataset"))
DATA: Year=col(source(s), name("Year"))
DATA: Total_Rate=col(source(s), name("Total_Rate"))
DATA: County=col(source(s), name("County"), unit.category())
GUIDE: axis(dim(1), label("Year"))
GUIDE: axis(dim(2), label("Total Arrest Rate per 1,000"))
ELEMENT: line(position(Year*Total_Rate), split(County))
END GPL.

That is not too bad, but we can do slightly better by making the lines small and semi-transparent (which is the same advice for dense scatterplots):

*Make them transparent and smaller.
FORMATS Total_Rate (F2.0).
GGRAPH
/GRAPHDATASET NAME="graphdataset" VARIABLES=Year Total_Rate County
/GRAPHSPEC SOURCE=INLINE.
BEGIN GPL
SOURCE: s=userSource(id("graphdataset"))
DATA: Year=col(source(s), name("Year"))
DATA: Total_Rate=col(source(s), name("Total_Rate"))
DATA: County=col(source(s), name("County"), unit.category())
GUIDE: axis(dim(1), label("Year"))
GUIDE: axis(dim(2), label("Total Arrest Rate per 1,000"))
SCALE: linear(dim(1), min(1970), max(2014))
ELEMENT: line(position(Year*Total_Rate), split(County), transparency(transparency."0.7"), size(size."0.7"))
END GPL.

This helps disentangle the many lines bunched up. There appear to be two outliers, and basically the rest of the pack.

A quick way to check out each individual line is then to make small multiples. Here I wrap the panels, and make the plot size bigger. I also make the X and Y axis null. This is ok though, as I am just focusing on the shape of the trend, not the absolute level.

*Small multiples.
GGRAPH
/GRAPHDATASET NAME="graphdataset" VARIABLES=Year Total_Rate County
/GRAPHSPEC SOURCE=INLINE.
BEGIN GPL
PAGE: begin(scale(1000px,1000px))
SOURCE: s=userSource(id("graphdataset"))
DATA: Year=col(source(s), name("Year"))
DATA: Total_Rate=col(source(s), name("Total_Rate"))
DATA: County=col(source(s), name("County"), unit.category())
COORD: rect(dim(1,2), wrap())
GUIDE: axis(dim(1), null())
GUIDE: axis(dim(2), null())
GUIDE: axis(dim(3), opposite())
SCALE: linear(dim(1), min(1970), max(2014))
ELEMENT: line(position(Year*Total_Rate*County))
PAGE: end()
END GPL.
*Manually edited to make less space between panels.

There are a total of 62 counties in New York, so this is feasible. With panel sets of many more lines, you can either split the small multiple into more graphs, or cluster the lines based on the overall shape of the trend into different panels.

Here you can see that the outliers are New York county (Manhattan) and Bronx county. Bronx is a pretty straight upward trend (which mirrors many other counties), but Manhattan’s trajectory is pretty unique and has a higher variance than most other places in the state. Also you can see Sullivan county has quite a high rate compared to most other upstate counties (upstate is New York talk for everything not in New York City). But it leveled off fairly early in the time series.

This dataset also has arrest rates broken down by different categories; felony (drug, violent, dwi, other), and misdemeanor (drug, dwi, property, other). It is interesting to see that arrest rates have been increasing in most places over this long time period, even though crime rates have been going down since the 1990’s. They all appear to be pretty correlated, but let me know if you use this dataset to do some more digging. (It appears index crime totals can be found going back to 1990 here.)

# In Dallas

For just a quick update, I’ve recently moved to Dallas. I was hired as an Assistant Professor at the University of Texas at Dallas. I’m in the criminology program within the EPPS school (Economic, Political and Policy Sciences).

My office is in Green Hall, room 3.530 – feel free to stop by. (I work pretty regular hours during the week, e.g. 8-4. If the door is closed still knock to see if I am in.) I am mostly around the GIS faculty – so I am not too out of place. For students (or other faculty) in the school wishing to collaborate always feel free to stop in.

The blog has been slow simply due to the move and trying to catch up on other projects. But posts will continue to trickle in when I find time.

Dallas is really nice. The heat is not that bad — y’all are complainers🙂. Here is my newest official mug-shot.

# Comparing samples post-matching – some helper functions after FUZZY (SPSS)

I’ve been conducting quite a few case-control or propensity score matching studies lately. So I wrote some helper functions for use after the SPSS FUZZY command. These create the case-control dataset, plus calculate some of the standardized bias metrics for matching on continuous outcomes.

The use case here is if you have a sub-set of treated individuals, and you want to draw a comparison sample matched on certain characteristics (which can include just one propensity score and/or multiple covariates). Here is the macro to follow along, and I will provide a quick walkthrough of how it works. (There is documentation in the header for what the parameters are and what the function returns.)

So first I am going to import my macro using INSERT:

*Inserting the macro.
INSERT FILE = "C:\Users\andrew.wheeler\Dropbox\Documents\BLOG\Matching_StandBias\PropBalance_Macro.sps".

Now just for illustration I am going to make a fake dataset to illustrate the utility of matching. Here I have a universe of 2,000 people. There is a subset of treated individuals (165), but they are only selected if they are under 28 years old and male.

*Create a fake dataset.
SET SEED 10.
INPUT PROGRAM.
LOOP Id = 1 TO 2000.
END CASE.
END LOOP.
END FILE.
END INPUT PROGRAM.
DATASET NAME OrigData.
COMPUTE Male = RV.BERNOULLI(0.7).
COMPUTE YearsOld = RV.UNIFORM(18,40).
FORMATS Male (F1.0) YearsOld (F2.0).
DO IF Male = 1 AND YearsOld <= 28.
COMPUTE Treated = RV.BERNOULLI(0.3).
ELSE.
COMPUTE Treated = 0.
END IF.
COMPUTE #OutLogit = 0.7 + 0.5*Male - 0.05*YearsOld - 0.7*Treated.
COMPUTE #OutProb = 1/(1 + EXP(-#OutLogit)).
COMPUTE Outcome = RV.BERNOULLI(#OutProb).
FREQ Treated Outcome.

So what happens when we make comparisons among the entire sample, which includes females and older people?

*Compare means with the original full sample.
T-TEST GROUPS=Treated(0 1) /VARIABLES=Outcome.

We get basically no difference, our treated mean is 0.40 and the untreated mean is 0.39. But instead of comparing the 165 to the entire sample, we draw more reasonable control cases. Here we do an exact match on Male, and then we do a fuzzy match on YearsOld to within 3 years.

*Draw the comparison sample based on Male (exact) and YearsOld (Fuzzy).
FUZZY BY=Male YearsOld SUPPLIERID=Id NEWDEMANDERIDVARS=Match1 GROUP=Treated
EXACTPRIORITY=FALSE FUZZ=0 3 MATCHGROUPVAR=MGroup DRAWPOOLSIZE=CheckSize
/OPTIONS SAMPLEWITHREPLACEMENT=FALSE MINIMIZEMEMORY=TRUE SHUFFLE=TRUE SEED=10.

Now what the FUZZY command does in SPSS is creates a new variable, named Match1 here, that places the matched Id in the same row as the original treated sample. You cannot easily make the updated comparisons that you want though in this data format. So after writing the code to do this about 7 times, I decided to make it into a simple macro. Here is an example of calling my macro, !MatchedSample.

*Now run my macro to make the matched sample.
!MatchedSample Dataset=OrigData Id=Id Case=Treated MatchGroup=MGroup Controls=[Match1]
MatchVars=[YearsOld] OthVars=Outcome Male.

This then spits out two new datasets, as well as appends a new variable to the original dataset named MatchedSample to show what cases have been matched. Then it is simple to see the difference in our means among our matched sample.

*Now the t-test with the matched sample subset.
DATASET ACTIVATE MatchedSamples.
T-TEST GROUPS=Treated(0 1) /VARIABLES=Outcome.

Which shows the same mean for treated, 0.40 (since all the treated were matched), but the comparison group now has a mean of 0.51, so here the treatment reduced the outcome.

The macro also provides an additional dataset named AggStats that estimates the standardized bias in the original sample vs. the standardized bias in the matched sample. (Standardized bias is just Cohen’s D measure multiplied by 100.) This then also calculates the standardized bias reduction for each continuous covariate. Before I forget, a neat way to test for balance jointly (instead of one variable at a time) is to conduct an additional regression equation predicting treatment and then testing for all coefficients equal to zero.

In this fake example the propensity scores would not be needed, you could just estimate a typical logistic regression equation controlling for YearsOld and Male. But the utility of matching comes from when you don’t know the functional form of how those covariates affect the outcome. So if the outcome was a very non-linear function of age, you don’t have to worry about estimating that function, you can just match on age and still get a reasonable comparison of the mean difference for treated vs. not-treated.

# Weekly and monthly graphs for monitoring crime patterns (SPSS)

I was recently asked for some code to show how I created the charts in my paper, Tables and Graphs for Monitoring Crime Patterns (Pre-print can be seen here).

I cannot share the data used in the paper, but I can replicate them with some other public data. I will use calls for service publicly available from Burlington, VT to illustrate them.

The idea behind these time-series charts are not for forecasting, but to identify anomalous patterns – such as recent spikes in the data. (So they are more in line with the ideas behind control charts.) Often even in big jurisdictions, one prolific offender can cause a spike in crimes over a week or a month. They are also good to check more general trends as well, to see if crimes have had more slight, but longer term trends up or down.

For a preview, we will be making a weekly time series chart:

In the weekly chart the red line is the actual data, the black line is the average of the prior 8 weeks, and the grey band is a Poisson confidence interval around that prior moving average. The red dot is the most recent week.

And we will also be making a monthly seasonal chart:

The red line is the counts of calls per month in the current year, and the lighter grey lines are prior years (here going back to 2012).

So to start, I saved the 2012 through currently 6/20/2016 calls for service data as a csv file. And here is the code to read in that incident level data.

*Change this to where the csv file is located on your machine.
FILE HANDLE data /NAME = "C:\Users\andrew.wheeler\Dropbox\Documents\BLOG\Tables_Graphs".
GET DATA  /TYPE=TXT
/FILE="data\Calls_for_Service_Dashboard_data.csv"
/ENCODING='UTF8'
/DELCASE=LINE
/DELIMITERS=","
/QUALIFIER='"'
/ARRANGEMENT=DELIMITED
/FIRSTCASE=2
/DATATYPEMIN PERCENTAGE=95.0
/VARIABLES=
AlcoholRelated AUTO
Area AUTO
CallDateTime AUTO
CallType AUTO
Domv AUTO
DayofWeek AUTO
DrugRelated AUTO
EndDateTime AUTO
GeneralTimeofDay AUTO
IncidentNumber AUTO
LocationType AUTO
MentalHealthRelated AUTO
MethodofEntry AUTO
Month AUTO
PointofEntry AUTO
StartDateTime AUTO
Street AUTO
Team AUTO
Year AUTO
/MAP.
CACHE.
EXECUTE.
DATASET NAME CFS.

First I will be making the weekly chart. What I did when I was working as an analyst was make a chart that showed the recent weekly trends and to identify if the prior week was higher than you might expect it to be. The weekly patterns can be quite volatile though, so I smoothed the data based on the average of the prior eight weeks, and calculated a confidence interval around that average count (based on the Poisson distribution).

As a start, we are going to turn our date variable, CallDateTime, into an SPSS date variable (it gets read in as a string, AM/PM in date-times are so annoying!). Then we are going to calculate the number of days since some baseline – here it is 1/1/2012, which is Sunday. Then we are going to calculate the weeks since that Sunday. Lastly we select out the most recent week, as it is not a full week.

*Days since 1/1/2012.
COMPUTE #Sp = CHAR.INDEX(CallDateTime," ").
COMPUTE Days = DATEDIFF(CallDate,DATE.MDY(1,1,2012),"DAYS").
COMPUTE Weeks = TRUNC( (Days-1)/7 ).
FREQ Weeks /FORMAT = NOTABLE /STATISTICS = MIN MAX.
SELECT IF Weeks < 233.

Here I do weeks since a particular date, since if you do XDATE.WEEK you can have not full weeks. The magic number 233 can be replaced by sometime like SELECT IF Weeks < (\$TIME - 3*24*60*60). if you know you will be running the syntax on a set date, such as when you do a production job. (Another way is to use AGGREGATE to figure out the latest date in the dataset.)

Next what I do is that when you use AGGREGATE in SPSS, there can be missing weeks with zeroes, which will mess up our charts. There end up being 22 different call-types in the Burlington data, so I make a base dataset (named WeekFull) that has all call types for each week. Then I aggregate the original calls for service dataset to CallType and Week, and then I merge the later into the former. Finally I then recode the missings intos zeroes.

*Make sure I have a full set in the aggregate.
FREQ CallType.
AUTORECODE CallType /INTO CallN.
*22 categories, may want to collapse a few together.
INPUT PROGRAM.
LOOP #Weeks = 0 TO 232.
LOOP #Calls = 1 TO 22.
COMPUTE CallN = #Calls.
COMPUTE Weeks = #Weeks.
END CASE.
END LOOP.
END LOOP.
END FILE.
END INPUT PROGRAM.
DATASET NAME WeekFull.

*Aggregate number of tickets to weeks.
DATASET ACTIVATE CFS.
DATASET DECLARE WeekCalls.
AGGREGATE OUTFILE='WeekCalls'
/BREAK Weeks CallN
/CallType = FIRST(CallType)
/TotCalls = N.

*Merge Into WeekFull.
DATASET ACTIVATE WeekFull.
MATCH FILES FILE = *
/FILE = 'WeekCalls'
/BY Weeks CallN.
DATASET CLOSE WeekCalls.
*Missing are zero cases.
RECODE TotCalls (SYSMIS = 0)(ELSE = COPY).

Now we are ready to calculate our statistics and make our charts. First we create a date variable that represents the beginning of the week (for our charts later on.) Then I use SPLIT FILE and CREATE to calculate the prior moving average only within individiual call types. The last part of the code calculates a confidence interval around prior moving average, and assumes the data is Poisson distributed. (More discussion of this is in my academic paper.)

DATASET ACTIVATE WeekFull.
COMPUTE WeekBeg = DATESUM(DATE.MDY(1,1,2012),(Weeks*7),"DAYS").

*Moving average of prior 8 weeks.
SORT CASES BY CallN Weeks.
SPLIT FILE BY CallN.
CREATE MovAv = PMA(TotCalls,8).
*Calculating the plus minus 3 Poisson intervals.
COMPUTE #In = (-3/2 + SQRT(MovAv)).
DO IF #In >= 0.
COMPUTE LowInt = #In**2.
ELSE.
COMPUTE LowInt = 0.
END IF.
COMPUTE HighInt = (3/2 + SQRT(MovAv))**2.
EXECUTE.

If you rather use the inverse of the Poisson distribution I have notes in the code at the end to do that, but they are pretty similar in my experience. You also might consider (as I mention in the paper), rounding fractional values for the LowInt down to zero as well.

Now we are ready to make our charts. The last data manipulation is to just put a flag in the file for the very last week (which will be marked with a large red circle). I use EXECUTE before the chart just to make sure the variable is available. Finally I keep the SPLIT FILE on, which produces 22 charts, one for each call type.

IF Weeks = 232 FinCount = TotCalls.
EXECUTE.

*Do a quick look over all of them.
GGRAPH
/GRAPHDATASET NAME="graphdataset" VARIABLES=WeekBeg TotCalls MovAv LowInt HighInt FinCount MISSING=VARIABLEWISE
/GRAPHSPEC SOURCE=INLINE.
BEGIN GPL
SOURCE: s=userSource(id("graphdataset"))
DATA: WeekBeg=col(source(s), name("WeekBeg"))
DATA: TotCalls=col(source(s), name("TotCalls"))
DATA: MovAv=col(source(s), name("MovAv"))
DATA: LowInt=col(source(s), name("LowInt"))
DATA: HighInt=col(source(s), name("HighInt"))
DATA: FinCount=col(source(s), name("FinCount"))
SCALE: pow(dim(2), exponent(0.5))
GUIDE: axis(dim(1))
GUIDE: axis(dim(2), label("Crime Count"))
ELEMENT: line(position(WeekBeg*TotCalls), color(color.red), transparency(transparency."0.4"))
transparency.interior(transparency."0.4"), transparency.exterior(transparency."1"))
ELEMENT: line(position(WeekBeg*MovAv))
ELEMENT: point(position(WeekBeg*FinCount), color.interior(color.red), size(size."10"))
END GPL.
SPLIT FILE OFF.

This is good for the analyst, I can monitor many series. Here is an example the procedure produces for mental health calls:

The current value is within the confidence band, so it is not alarmingly high. But we can see that they have been trending up over the past few years. Plotting on the square root scale makes the Poisson count data have the same variance, but a nice thing about the SPLIT FILE approach is that SPSS does smart Y axis ranges for each individual call type.

You can update this to make plots for individual crimes, and here I stuff four different crime types into a small multiple plot. I use a TEMPORARY and SELECT IF statement before the GGRAPH code to select out the crime types I am interested in.

FORMATS TotCalls MovAv LowInt HighInt FinCount (F3.0).
TEMPORARY.
SELECT IF ANY(CallN,3,10,13,17).
GGRAPH
/GRAPHDATASET NAME="graphdataset" VARIABLES=WeekBeg TotCalls MovAv LowInt HighInt FinCount CallN MISSING=VARIABLEWISE
/GRAPHSPEC SOURCE=INLINE.
BEGIN GPL
PAGE: begin(scale(900px,600px))
SOURCE: s=userSource(id("graphdataset"))
DATA: WeekBeg=col(source(s), name("WeekBeg"))
DATA: TotCalls=col(source(s), name("TotCalls"))
DATA: MovAv=col(source(s), name("MovAv"))
DATA: LowInt=col(source(s), name("LowInt"))
DATA: HighInt=col(source(s), name("HighInt"))
DATA: FinCount=col(source(s), name("FinCount"))
DATA: CallN=col(source(s), name("CallN"), unit.category())
COORD: rect(dim(1,2), wrap())
SCALE: pow(dim(2), exponent(0.5))
GUIDE: axis(dim(1))
GUIDE: axis(dim(2), start(1), delta(3))
GUIDE: axis(dim(3), opposite())
GUIDE: form.line(position(*,0),color(color.lightgrey),shape(shape.half_dash))
ELEMENT: line(position(WeekBeg*TotCalls*CallN), color(color.red), transparency(transparency."0.4"))
transparency.interior(transparency."0.4"), transparency.exterior(transparency."1"))
ELEMENT: line(position(WeekBeg*MovAv*CallN))
ELEMENT: point(position(WeekBeg*FinCount*CallN), color.interior(color.red), size(size."10"))
PAGE: end()
END GPL.
EXECUTE.

You could do more fancy time-series models to create the confidence bands or identify the outliers, (exponential smoothing would be similar to just the prior moving average I show) but this ad-hoc approach worked well in my case. (I wanted to make more fancy models, but I did not let the perfect be the enemy of the good to get at least this done when I was employed as a crime analyst.)

Now we can move onto making our monthly chart. These weekly charts are sometimes hard to visualize with highly seasonal data. So what this chart does is gives each year a new line. Instead of drawing error bars, the past years data show the typical variation. It is then easy to see seasonal ups-and-downs, as well as if the latest month is an outlier.

Getting back to the code — I activate the original calls for service database and then close the Weekly database. Then it is much the same as for weeks, but here I just use calendar months and match to a full expanded set of calls types and months over the period. (I do not care about normalizing months, it is ok that February is only 28 days).

DATASET ACTIVATE CFS.
DATASET CLOSE WeekFull.

COMPUTE Month = XDATE.MONTH(CallDate).
COMPUTE Year = XDATE.YEAR(CallDate).

DATASET DECLARE AggMonth.
AGGREGATE OUTFILE = 'AggMonth'
/BREAK Year Month CallN
/MonthCalls = N.

INPUT PROGRAM.
LOOP #y = 2012 TO 2016.
LOOP #m = 1 TO 12.
LOOP #call = 1 TO 22.
COMPUTE CallN = #call.
COMPUTE Year = #y.
COMPUTE Month = #m.
END CASE.
END LOOP.
END LOOP.
END LOOP.
END FILE.
END INPUT PROGRAM.
DATASET NAME MonthAll.

MATCH FILES FILE = *
/FILE = 'AggMonth'
/BY Year Month CallN.
DATASET CLOSE AggMonth.

Next I select out the most recent month of the date (June 2016) since it is not a full month. (When I originally made these charts I would normalize to days and extrapolate out for my monthly meeting. These forecasts were terrible though, even only extrapolating two weeks, so I stopped doing them.) Then I calculate a variable called Current – this will flag the most recent year to be red in the chart.

COMPUTE MoYr = DATE.MDY(Month,1,Year).
FORMATS MoYr (MOYR6) Year (F4.0) Month (F2.0).
SELECT IF MoYr < DATE.MDY(6,1,2016).
RECODE MonthCalls (SYSMIS = 0)(ELSE = COPY).

*Making current year red.
COMPUTE Current = (Year = 2016).
FORMATS Current (F1.0).

SORT CASES BY CallN MoYr.
SPLIT FILE BY CallN.

*Same thing with the split file.
GGRAPH
/GRAPHDATASET NAME="graphdataset" VARIABLES=Month MonthCalls Current Year
/GRAPHSPEC SOURCE=INLINE.
BEGIN GPL
SOURCE: s=userSource(id("graphdataset"))
DATA: Month=col(source(s), name("Month"), unit.category())
DATA: MonthCalls=col(source(s), name("MonthCalls"))
DATA: Current=col(source(s), name("Current"), unit.category())
DATA: Year=col(source(s), name("Year"), unit.category())
GUIDE: axis(dim(1))
GUIDE: axis(dim(2), label("Calls"), start(0))
GUIDE: legend(aesthetic(aesthetic.color.interior), null())
SCALE: cat(aesthetic(aesthetic.color.interior), map(("0",color.lightgrey),("1",color.red)))
ELEMENT: line(position(Month*MonthCalls), color.interior(Current), split(Year))
END GPL.

You can again customize this to be individual charts for particular crimes or small multiples. You can see in the example at the beginning of the post Retail thefts are high for March, April and May. I was interested to examine overdoses, as the northeast (and many parts of the US) are having a problem with heroin at the moment. In the weekly charts they are so low of counts it is hard to see any trends though.

We can see that overdoses were high in March. The other highest line are months in 2015, so it looks like a problem here in Burlington, but it started around a year ago.

For low counts of crime (say under 20 per month) seasonality tends to be hard to spot. For crimes more frequent though you can often see pits and peaks in summer and winter. It is not universal that crimes increase in the summer though. For ordinance violations (and ditto for Noise complaints) we can see a pretty clear peak in September. (I don’t know why that is, there is likely some logical explanation for it though.)

My main motivation to promote these is to replace terrible CompStat tables of year-over-year percent changes. All of these patterns I’ve shown are near impossible to tell from tables of counts per month.

Finally if you want to export your images to place into another report, you can use:

OUTPUT EXPORT /PNG IMAGEROOT = "data\TimeGraphs.png".

PNG please – simple vector graphics like these should definately not be exported as jpegs.

Here is a link to the full set of syntax and the csv data to follow along. I submitted to doing an hour long training session at the upcoming IACA conference on this, so hopefully that gets funded and I can go into this some more.

# Sentence length in academic articles

While reviewing a paper recently it struck me that the content was (very) good, but the writing was stereotypical academic. My first impression was that this was caused by monotonously long sentences. See this advice from Gary Provost (via Francis Diebold). Part of the reason why long sentences are undesirable is not only for aesthetic reasons though — longer sentences are harder to parse, hold in memory, and subsequently understand. See Steven Pinker’s The Sense of Style writing guide for discussion.

So I did some text analysis of the sentences. To do the text analysis I used the nltk library in python, and here is the IPython notebook to replicate for yourself if you care to do so. In the notebook I have saved two text corpuses, one my finished draft of this article. I compared the sentence length to Mark Twain’s Huckleberry Finn (text via here).

For a simple example getting started with the library, here is an example of tokenizing a string into words and sentences:

#some tests for http://www.nltk.org/, nice book to follow along
import nltk

#this splits up punctuation
test = """At eight o'clock on Thursday morning Arthur didn't feel very good. This is a second sentence."""
tokens = nltk.word_tokenize(test)
print tokens

ts = nltk.sent_tokenize(test)
print ts

The first prints out each individual word (plus punctuation in some circumstances) and the second marks individual sentences. I have the line #nltk.download('punkt') commented out, as I downloaded it once already. (Running once in Wakari I did not need to download it again – I presume it would work similarly on your local machine.)

So what I did was transfer the PDF document I was reviewing to a text file and then clean up things like the section headers (ditto for my academic articles I compare it to). In Huckleberry I took out the table of contents and the "CHAPTER ?" parts. I also started a list of variables that were parsed as words but that I did not want to count after the sentences and words were tokenized. For example, an inline cite such as (X, 1996) would be split into 4 words with the original tokenizer, (, X, 1996 and ). The "x96" is an en-dash. Below takes those instances out.

#Get the corpus
f = open('SmallSample_Corpus.txt')

#Count number of sentences
sent_tok = nltk.sent_tokenize(raw)
ns = len(sent_tok)

#Count number of words
word_tok = nltk.word_tokenize(raw) #need to take out commas plus other stuff
NoWord = [',','(',')',':',';','.','%','\x96','{','}','[',']','!','?',"''",""]
word_tok2 = [i for i in word_tok if i not in NoWord]
nw = len(word_tok2)

#Average Sentence length are words divided by sentences
print float(nw)/ns

There are inevitably more instances of things that shouldn’t be counted as words, but that makes the sentences longer on average. For example, I spotted a few possessive 's that were listed as different words. (The nltk library is smart and lists contractions as seperate words.)

So someone may know a better way to count the words, but all the articles should have the same biases. In my tests, here are the average number of words per sentence:

• article I was reviewing, 28
• my small sample article, 27
• my working article (that has not undergone review), 25
• Huck Finn, 20

So the pot is calling the kettle black here – my writing is not much better. I looked at the difference between an in-print article and a working draft, as responses to reviewers I bet will make the sentences longer. Hedges in statements that academics love.

Looking at the academic article histograms they are fairly symmetric, confirming my impression about monotonous sentence length. To make the histograms I used the panda’s library, which has a nice simple method.

sent_len = []
for i in sent_tok:
sent_w1 = nltk.word_tokenize(i)
sent_w2 = [i for i in sent_w1 if i not in NoWord]
sent_len.append(len(sent_w2))

import pandas as pd

dfh = pd.DataFrame(sent_len)
dfh.hist(bins = 50);

Here is the histogram for my small sample paper:

And here it is for Huck Finn

(I’m not much of an exemplar for making graphs in python – forgive the laziness in the figures.) Apparently analyzing sentence length has a long history, see a paper by G. Udny Yule in 1939! From a quick perusal the long right tail is more usual for analyzing texts. The symmetry I see for this sample of academic articles is not the norm.

There could be more innocuous reasons for this. Huck Finn has dialogue with shorter sentences, and the academic articles have numbers and citations. (Although I think it is reasonable to count those things towards sentence complexity, "1" or "one" should have the same complexity.)

I will have to keep this in mind in the future (maybe I should write my articles in poem form)!