Automating Csv Data Type Detection When Importing Into Gis

The Problem of Inconsistent Data Types

Comma-separated values (CSV) files often lack defined data types for their attributes. When importing these untyped datasets into GIS systems, the software must guess the appropriate types like strings, integers, and dates. Sometimes the detection fails or assigns inaccurate types to columns. This can create major issues down the line with visualization, analysis, and data management.

Operations and tools in GIS work best when data types correctly match the real-world feature a column represents. For example, treating a numerical population attribute as a string prevents mathematical functions like summing totals across towns and counties. Appending the values together as strings outputs nonsense values rather than usable statistics.

In addition to hampering analysis, faulty data types get visualized poorly in maps and charts. Dates may not place properly on time-enabled axes. Numerical ranges fail to classify into graded color ramps or symbol sizes. Boolean flags display as integers, not intuitive checks or X’s. Without parameter tuning, the software cannot intuit the user’s desired output.

Furthermore, imported datasets with improper types slow workflows and conflict with later processing. Before analysis, Experts waste time correcting types across hundreds of fields manually. Or worse yet, invalid assumptions at import propagate through merging and Spatial SQL operations downstream.

Methods of Data Type Detection

Thankfully, GIS programs provide both simple and advanced methods to detect column types while ingesting tabular data. Out-of-the-box import tools do basic checks on values and distributions during the read process. For finer control, data managers can use scripting libraries from their language of choice to parse columns before passing the typed dataset into the application.

Built-in Tools in GIS Software

GIS import wizards infer types by scanning values present in certain rows of the source table. String lengths, numeric decimal places, and date formats provide clues about the attribute’s true nature. For example in ArcMap, the Feature Class to Feature Class tool scans the first 100 rows by default to makes an educated guess.

Many parameters allow tweaking this analysis depending on quirks of the dataset – like disabling automatic date detection, sampling a larger row subset, and specifying field lengths for strings. Consult your software’s documentation for customization details.

Using Programming Libraries and Packages

Scripting languages for data science contain various libraries with type detection functions. These provide fine control for parsing numerics, dates, Boolean flags, and unstructured strings into their proper formats.

For example, Python’s Pandas package detects types during reads from CSV via parameters like parse_dates and dtype specifications. Or JavaScript’s PapaParse library scans values in a flexible, customizable way. This level of programming allows tweaking detections to handle tricky, irregular datasets.

Custom Methods and Functions

For use cases with special needs, advanced GIS users can author custom subroutines for data typing. Techniques like regex strings, character analysis, value range checking, and pattern matching parse out types accurately. These functions integrate into import sequences and modelbuilder workflows.

While demanding expertise to craft, homegrown type detection better handles outlier datasets with domain-specific cases conventional tools miss. The time investment pays off for organizations reliant on importing messy CSVs from niche sources.

Automating Detection During Import

Manually detecting data types can prove tedious, especially for large datasets with hundreds of attributes per table. GIS software allows scripting import sequences to repeat reliably and automatically handle typing behind the scenes each run.

Using Import Wizards and Geoprocessing Tools with Parameters

GUI import tools contain algorithms which can detect types during batch loading datasets via programming sequences. For example, ArcMap’s Feature Class to Feature Class geoprocessing wizard contains optional parameters to define field types and date formats at read time.

Python or ArcPy scripts simply invoke the tools with this metadata supplied, automating away the manual pointing and clicking needed in the user interface. The output feature classes now contain properly defined types matching the source data.

Scripting and ModelBuilding Workflows for Repeatable Processes

Langauges like Python take this automation further by scripting a sequence of import steps with intermittent type detection routines. For example, Pandas can read as generically typed strings first. Then scripts parse subsets by type before passing the now-structured data as input to a geoprocessor object.

GIS platforms like ArcGIS Pro allow encapsulating these scripts into reusable modelbuilder workflows with parameterized inputs. Future runs simply provide new input CSVs to automatically process to properly typed feature classes.

Leveraging Python and ArcPy to Set Data Types Programmatically

For complete control, Python’s arcpy module gives low-level access to set field types in feature class and table definititons programmatically. Scripts can scan rows and columns while reading CSVs, detecting patterns and applying appropriate types for date, numeric and string fields dynamically.

Well-crafted algorithms slot column values into correct ArcGIS field type constants like DOUBLE, DATE, and TEXT based on intelligent parsing. This automation eliminates late-stage corrections down the line.

Parsing Strings to Appropriate Types

For common pitfalls like numbers and dates hiding erroneously as text strings, scripts apply various parsing techniques to extract proper types:

Techniques for Numeric, Date, Boolean, etc

Regular expressions offer flexible rules to pattern match values like phone numbers, weights with units, grid coordinates, etc. as distinct typed groups. Useful Python examples covered on regex101.com and libraries like re, fnmatch handle common cases out-of-box.

Dates in non-standard string formats parsing correctly via strptime settings and pandas. Parsers accept input varieties like Jan 5, 2022 and 01/13/95 as datetime objects for GIS consistency.

Booleans flags as ‘Y/N’, ‘True/False’ convert using lookup maps and conditions – retaining logical meaning. Useful for typing indicators from statistical surveys as bit fields oruntarily GeoDatabase domain values in workflows.

Using Regular Expressions and Other Parsing Tools

Regular expressions enable matching date patterns days, months, years inIR formats for sequential restructuring into ISO standard datetime objects. Extensive libraries like Python Regex, JavaScript RegEx provide fast optimized rule evaluation engines against CSV cells for type discovery.

Pandas contains vectorized parsers and converters optimized for data science workflows. For example, high-performance apply() and map() methods leverage these across entire series and data frames, avoiding slower iterative type checking.

Special libraries like dateparser exclusively parse temporal strings while accounting for cultures, languages locale – invaluable for munging international CSV data sources.

Applying Detected Types in GIS

Proper typing unlocks better visualization display and analytics. Converting numbers and dates before load avoids intermediary text handling.

Setting Column Types in Attribute Tables

Detected types define fields on import instead of post-hoc. Typed consistently from the source, columns in the field schema display appropriate widths scale decimals and display formats. Dates and text tailor display for easy inspection reading vs truncated values.

On-the-fly Projection and Transformation Benefits

Accurate numeric types allows on import projection of geometry ensuring spatial alignment for analysis. For example, DMS coordinates and other specialized text encodings convert to standard double point representations. It enables correct reprojection.

Well defined datums and spatial references from correctly typed values prevent distortion when layer projected on-the-fly. Numbers parsed as doubles integrate smoothly with planer/geographic coordinate space expectations.

Enabling Better Analysis and Data Quality

Proper types feed into downstream processes instead of failing sequence unexpectedly or propagating improperly typed null values.

Precise range reporting, change detection, classification benefit from numeric vs text handling. Statistical summaries calculate accurately for decision making.

Finally, flag fields as boolean type carry logical meanings into Spatial SQL predicate clauses. QC scripts assess data quality of imports through encoded domain values vs cryptic integers mischaracterizing source data.

Custom Workflows for Specialized Data

Real-world datasets often necessitate specialized handling beyond basic type coercion. Custom import sequences tailor parsing and mappings.

Going Beyond Basic Type Detection

Finely tuned regular expressions extract multiple sub-types from complex conventions like timestamps, delimited normalized numbers, encoded text values. Custom user-define functions organize this metadata through hierarchical typing relationships, preserving it for later deconstruction.

Rules for Handling Idiosyncratic CSV Datasets

Regex patterns and substring operations dispatch one-off formatting quirks into appropriately typed columns – geocodes concatenating country province portions, variable whitespace delimiting changing number values, textual codes repurposed indicators etc.

Loops selectively handling conditional cases isolate issues to column subsets only, while retaining scales domains for clean portions. Custom subtypes discern these quirks from properly structured domains.

Examples and Sample Code

See Github repo github.com/typenet for annotated type detection scripts in Python and JavaScript highlighting common parsing cases. Includes regex visualizers, best practice conventions on typing workflows, and Jupyter Notebook examples users can run live.

Esri’s Sample Scripts collection contains example GP tool parameter settings, label parsers, attribute assistants covering automated typing for custom import models.

Conclusion

Summary of Benefits

Applying automated data type detection when ingesting CSVs into GIS systems reduces errors through analysis visualization while cutting manual cleaning costs. Custom parsing accurately handles quirky one-off cases capturing specialized metadata types companied convention uses. Enabled by scripting capabilities GIS software, properly typed attributes integrate datasets smoothly throughout processing pipeline enabling reliable spatial data-driven decision making.

Additional Resources

Geoprocessing Workspace: blogs.esri.com/support/tech/python

Pandas User Guide: pandas.pydata.org

Esri Knowledge Base: support.esri.com