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The following step-by-step instructions and screenshots are based on the Windows 10 operating system with the Windows Classic desktop theme and ArcGIS Pro 2.1.3 software. If your personal system configuration varies, you may experience minor differences from the instructions and screenshots.

PART I: Install the R-ArcGIS bridge

1. Download R and RStudio
   R link:
   R Studio link:
   
   
2. Prepare data on Houston crime statistics
   Thogh you can Google search Houston crime GIS data and find the shapefile (with the link: https://cohgis-mycity.opendata.arcgis.com/datasets/hpd-nibrs-crime), it only contains around 4,000 cases, which is far from complete. As a remedy, we need to download the data from Houston Crime Statistics (with the link: https://www.houstontx.gov/police/cs/Monthly_Crime_Data_by_Street_and_Police_Beat.htm). For example, we can focus on the data of year 2020 (see the following screenshot).
   
   
   
   Note that the data is in .xlsx format. We need to save it as .csv file and then geocode the addresses to make it a shapefile. You need to use credits for geocoding in ArcGIS Pro. For convenience, I have already downloaded the data (and cleaned a little in terms of addresses) and geocoded it into a shapefile. You can downloaded the .zip rar file attaced here houston-crime.gdb.rar and proceed. Notice that, for simplicity, I only use the crime cases of the first 10 days of year 2020, that is, dating from January 1, 2020 to January 2, 2020 (I choose 10 days because the step in Part II using Create Space Time Cube by Aggregating Points requires at least 10 time step intervals (in this case, it requires at least 10 days if the time step interval is set as 1 day).
   
   
3. Create a new project in ArcGIS Pro
   
   
4. Install the R-ArcGIS bridge: automatical method/manual method
   Manual method: https://github.com/R-ArcGIS/r-bridge-install

PART II: Basic statistical analysis

1. Project the shapefile
   
   
2. Aggregate point data by counts within a defined location
   
   
3. Analyze crime hot spots: One step forward

PART III: Enhance the dataset with additional attributes

1. Add additional attributes to the original dataset
   
   
2. Further prepare the dataset

PART IV (KEY SECTION): Conduct statistical analysis using R and ArcGIS Pro

1. Bridge your data into R
   
   
2. Calculate smoothed crime rates
   
   
3. Continue analysis in ArcGIS Pro
   
   
4. Identify areas with unusually high crime rates
   

PART V (KEY SECTION): Identify attributes that influence crime

1. Create a correlation matrix in R to evaluate attribute relationships