Iucr Code

[Jonelle Rycroft]

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The following codes should be used with _exptl_absorpt_correction_type. Note that this data item should contain only the type code. A reference to the computer program used to apply the absorption corrections should be given in _exptl_absorpt_process_details.

The following codes should be used with _refine_ls_hydrogen_treatment. Note that this data item should only contain the type code. Any detailed text about the determination and refinement of H-atom parameters should be placed in _publ_section_exptl_refinement.

The following codes should be used with _refine_ls_weighting_scheme. Note that this data item should contain only the type code. The weighting expression should be given in _refine_ls_weighting_details.

Why bother with MongoDB? Although MongoDB is not yet, to my eye, an obvious choice for processing enterprise-level data, it has a useful role in the enterprise. It can be a powerful tool for automating the process of getting hold of data and transforming it into a form that can then be consumed easily by reporting tools or for updating data in a relational database. It can be a useful adjunct to scripting.

MongoDB is best installed on its own server, but it is happy with a reasonably-sized PC. The more memory you can provide it, the happier it is. However, if you install it on your slow workstation or laptop, you will be more attuned to performance issues and keener to deal with them. I usually use Chocolatey to install MongoDB and Studio3T, and I use it to subsequently keep my MongoDB instances up-to-date.

To prevent the MongoDB database server from shutting down, keep this window open and launch a new PowerShell window to follow along with the rest of the article. MongoDB can be alternatively run as a service. (instructions in the link) If you run it on a server as a service and wish to access it across a network, you will need to ensure that there is access to the port you select

Some shell functions return a JSON string that tells you how it did. We can read this in PowerShell to create native PowerShell objects so we can check the status. Also, we would rather like to save the BSON (binary JSON) result as a native PowerShell object.

I wanted a database that provided a good learning potential and had plenty of data. Human nature being what it is, the open data police records are perfect for databases of a reasonable size. It is ideal as a practice database because it has a number of interesting details, including location data. I was interested to find out much more of the detail of the trends in crime in Chicago, so I downloaded the Chicago database of crimes from 2001 to the present provided by the Chicago police department.

Crime levels were steeply on the decline in Chicago, and I wanted to find out more of the detail. Were all types of crime in decline or were we just seeing fewer of the more common crimes such as theft? Was there any truth in the idea that crimes increased significantly on particular days of the week?

Beyond changing the two dates to an ISO form that is suitable for MongoDB, there is going to be very little preparation work that we will need to do , which is just as well if you are re-reading the database from its source regularly to keep it up-to-date.

The Crimes database, at six and a half million records, is too big to go into a spreadsheet but fits happily in either a relational database or MongoDB on the laptop, so it is ideal for experimentation. We aim to get to the level of investigation shown below, in this case using Sparklines. To do this, we need to get the data into Excel.

Before we start using the collection methods for querying and aggregation, I must prepare you for a shock. We are using JavaScript. Whereas, in SQL, we are able to express the result that we want, in MongoDB, we have to explain how to get it, using JavaScript. Actually, MongoDB has a query optimiser and will decide the best indexes to use and so on. More recently, it has been able to change the order of pipeline operators to optimise the process and ensure that any filtering is done as early as possible in the process.

It is possible to create queries and statements in SQL and convert them to MongoDB JavaScript. There are third-party utilities that do this to a small extent but generally they fail to translate any but the simplest SQL code, or you can use a good ODBC driver which will allow you to use SQL to do selection and grouping. With the profiler that I explain here, you can then examine, modify, adapt and use the MongoDB code that you retrieve.

So now we have all the information we need for some pretty cool reporting. We now have a much more manageable collection with just 245,000 records and taking a mere 20Mb of space. This extract will be a lot more serviceable.

We are getting to the point where we can do more exploration using an appropriate tool such as Excel, so we export it to a CSV format that excel can read in. MongoExport.exe throws an unspecified category of error in PowerShell even if it works successfully, hence the awkward code. It works fine in the command shell but fails in PowerShell.

We can also see how the crime level varies according to the time of the year, and a surface map tells us that February is a quiet time for all sorts of crime, both against persons and property, whereas July and August are the most difficult months.

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