Key Points

Introduction to Data Processing

Processing turns collected data into analysis-ready data: inspect, clean, transform, combine, derive
Keep raw data unchanged and write results somewhere else
Record every step: a notebook or script, the QGIS Processing History or a model, and a short log
Check after every step, with counts, totals and a quick map
Use QGIS for quick, visual work and Python (or a QGIS model) for work you will repeat

Inspect before changing anything: shape, types, minimum and maximum reveal most problems
Read identifiers as text, convert numbers stored as text, and turn special codes into missing values
ACS estimates have margins of error; the coefficient of variation shows which ones are too uncertain to rank
Decide what makes two records the same before removing duplicates
Define a CRS to say what coordinates are; reproject to change them
Check every step with a count, a join or a map, and write your decisions down

Inspect before changing anything: sorting a column quickly shows text that should be numbers and special codes
Refactor Fields renames fields, sets their types and fills them with expressions in one step, without changing the original
to_int(), if(... < 0, NULL, ...) and nullif(x, 0) handle text numbers, codes and division by zero
ACS estimates have margins of error; the coefficient of variation shows which are too uncertain to rank
Create points from coordinates with the CRS they are in; decide what makes two records the same before deleting
Check validity, remove unwanted features and dissolve split ones, and confirm each step with a count

Project every layer to a CRS in meters (EPSG:26916 for Indiana) before buffering, measuring or overlaying
Buffer draws zones, dissolve merges them, clip and intersect cut shapes, difference keeps what lies outside, and a nearest join finds the closest feature and its distance
The spatial predicate (intersects, within, centroid within) changes which features are selected; report it
Keep the original area before cutting, so areal weighting can split counts by area share
Sum counts, never rates or medians; areal weighting assumes an even spread and straight lines are not travel

Reproject every layer to a CRS in meters (EPSG:26916 for Indiana) before buffering, measuring or overlaying; on-the-fly display does not change the data
Buffer draws zones, Dissolve merges them, Clip and Intersection cut shapes, Difference keeps what lies outside, and Join attributes by nearest finds the closest feature and its distance
The predicate in Select by Location (intersect, are within, centroid within) changes the answer; report it
Store the original area before cutting, then use area($geometry) / "tract_m2" * count for areal weighting
Sum counts, never rates or medians; processing results are temporary until you make them permanent

Map algebra works cell by cell on rasters that share CRS, cell size and extent; resample categories with nearest neighbor
Comparisons make Boolean rasters, lookup tables reclassify codes, and & combines conditions
Zonal statistics summarize cells inside polygons: means for measurements, class counts for categories
Cell counts times cell area give areas, in a CRS measured in meters
Dasymetric estimation uses land cover to place people; compare it with areal weighting

Rasters combined cell by cell must share CRS, cell size and extent; resample categories with nearest neighbor
The Raster Calculator makes comparisons and combines conditions (AND, OR); results are 1 and 0
Reclassify by table groups codes; set the range boundaries to min <= value <= max for whole-number codes
Zonal statistics summarize measurements per polygon; Zonal histogram counts categories per polygon
Check results with the unique values report: cells times 900 m² gives areas

Automate work you will repeat; record work you will not
A function packages steps; parameters are the parts that change, defaults the usual values
Test on a known answer, check inputs, and report counts so every run leaves evidence
Loops (or groupby) run the same workflow for many inputs and collect the results in one table
Combine units by adding counts first, then computing rates
Catch errors in long loops, but record them and check the totals at the end

Automate work you will repeat; record work you will not (Processing > History)
A model packages several tools; its inputs are the parts that change
Test a model on a known answer before using it on new questions
Run as Batch Process repeats a tool or model with different settings, one row per run
Iterate over this layer runs a tool once per feature
Make common mistakes impossible inside the model, for example by reprojecting every input