Experiment Planner

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WDD Counts Planning

Baseline Counts per Unit Time

Treated Reduction

Time Periods

Advanced Settings

Alpha Level

Pre-time Periods

Use Unit Time

Time	Estimate	SE	Lower	Upper

IRR Counts Planning

Control Count per Unit Time

Treated Count per Unit Time

IRR Target (e.g. 0.8)

Pre-time Periods

Time Periods (Post)

Alpha Level

Note: If Pre-time is 0, Control Count is used as the baseline for both groups.

Time	IRR Est	SE (log)	Lower	Upper

Proportion Events Planning

Control Proportion

Treated Proportion

Control Sample Size per Period

Treated Sample Size per Period

Time Periods

Alpha Level

Time	Estimate	SE	Lower	Upper

Simulation Proportion Events

Crime Distribution (Type, Baseline, Proportion)

Note: Baseline is the breakdown of all crime types (e.g. thefts are more common than assaults), while Proportion is the baseline rate that happens for those crimes (e.g. clearances are lower for property than for violent crimes).

Odds Ratio Target Total N Values (comma separated) Simulations Count

Total N	ORR Target	Sim ORR	Bias	SE

Help & Methodology

Citation References

If you use these tools or the WDD statistic in your research, please use the following citations:

WDD Statistic: Wheeler, A. P., & Ratcliffe, J. H. (2018). A simple weighted displacement difference test to evaluate place based crime interventions. Crime Science, 7(1), 11. https://link.springer.com/article/10.1186/s40163-018-0085-5
Wilson IRR Estimator: Wilson, D. B. (2022). The relative incident rate ratio effect size for count-based impact evaluations: When an odds ratio is not an odds ratio. Journal of Quantitative Criminology, 38(2), 323-341. https://link.springer.com/article/10.1007/s10940-021-09494-w
Planning Application: Wheeler, A. P. (2026). Experiment Planning for Crime Analysts. Crime De-Coder. https://crimede-coder.com/graphs/Planning

WDD Counts Planning

The Weighted Displacement Difference (WDD) tool is designed for planning experiments involving crime counts. It helps estimate the precision (Standard Error) of crime reduction estimates over time.

Baseline Counts: The average number of crimes per unit time (e.g., crimes per week) expected in both areas.
Treated Reduction: The absolute decrease in crimes you expect the treatment to cause per unit time.
Pre-time Periods: The number of historical time periods used for the baseline variance calculation. If set to 0, it assumes no historical data is available.
Unit Time: If checked, results are shown as rates per period. If unchecked, results show the cumulative reduction across the study duration.

IRR Counts Planning

The Incident Rate Ratio (IRR) tool uses the Wilson log IRR estimator. It is suitable for comparing crime rates between two areas, with or without historical (pre) data. This tool is valid if you expect a percentage reduction, but the control and treated areas have very different total crime counts (for the DiD estimate). For the continuous monitoring over time though with no pre-treated, treated and control should have similar crime counts.

Control/Treated Count: The baseline number of crimes per unit time in each area.
IRR Target: The expected ratio of the treated rate to the control rate (e.g., 0.8 for a 20% reduction).
Pre-time Periods: If greater than 0, the tool calculates a Difference-in-Differences (DiD) log IRR. If 0, it performs a simple post-period comparison.

Proportion Events Planning

This tool is used for planning experiments where the outcome is a proportion, such as arrest rates per crime incident. It is useful if the events have a consistent rate, like use of force, across treated and control.

Control/Treated Proportion: The expected probability of an event in each group (e.g., 0.4 for a 40% arrest rate).
Sample Size per Period: The number of crime incidents occurring in each arm of the experiment per time unit. This is used to determine the total N as time progresses.
Alpha Level: The significance level (default 0.05 for 95% confidence intervals).

Simulation Proportion Events

A simulation-based approach using logistic regression to plan for experiments with multiple crime types and varying baseline solution rates. This is useful if the baseline events have a different rate (like clearances for different crimes), but you expect the treatment effect to be constant. The simulation estimates a logistic regression equation of the form:

                logit(P(Event)) = β0 + Σ βi·CrimeTypei + βtreat·Treat
            

Where:

Fixed Effects: The β_i terms are fixed effects for each crime type, accounting for different baseline solve rates.
Treatment Effect: The β_treat term represents the consistent log-odds ratio reduction (or increase) applied across all crime types (calculated as ln(Target IRR)).
Bias & SE: By replicating this model many times, the tool estimates the potential bias and the standard error of the treatment effect at various sample sizes.

General Notes

This application runs entirely in your browser using R-WASM (WebR). Calculations are performed locally on your machine. Standard errors and confidence intervals are calculated based on the formulas described in the Crime De-Coder blog posts.

References & Further Reading

WDD Visualizations: WDD Original Test and Extensions for Different Metrics.
Planning Experiments: Blog post on increasing proportions and arrest rates.
Statistical Estimators: WDD vs. Wilson Log IRR Estimator.
R Code & Packages: WDD implementation can be found in the ptools GitHub repository (see wdd.R and wdd_harm.R).
WebR: This app is powered by WebR (R-WASM).