# Kernel Density and Contours in QGIS: Noisy NYC

In spatial analysis, kernel density estimation (colloquially referred to as a type of “hot spot analysis”) is used to explore the intensity or clustering of point-based events. Crimes, parking tickets, traffic accidents, bird sightings, forest fires, incidents of infections disease, anything that you can plot as a point at a specific period in time can be studied using KDE. Instead of looking at these features as a distribution of discrete points, you generate a raster that represents a continuous surface of values. You can either measure the density of the incidents themselves, or the concentration of a specific attribute that is tied to those incidents (like the dollar amount of parking tickets or the number of injuries in traffic accidents).

In this post I’ll demonstrate how to do a KDE analysis in QGIS, but you can easily implement KDE in other software like ArcGIS Pro or R. Understanding the inputs you have to provide to produce a meaningful result is more important than the specific tool. This YouTube video produced by the SEER Lab at the University of Florida helped me understand what these inputs are. They used the SAGA kernel tool within QGIS, but I’ll discuss the regular QGIS tool and will cover some basic data preparation steps when working with coordinate data. The video illustrates a KDE based on a weight, where there were single points that had a count-based attribute they wanted to interpolate (number of flies in a trap). In this post I’ll cover simple density based on the number of incidents (individual noise complaints), and will conclude by demonstrating how to generate contour lines from the KDE raster.

For a summary of how KDE works, take a look at the entry for “Kernel” in the Encyclopedia of Geographic Information Science (2007) p 247-248. For a fuller treatment, I always recommend Christopher Lloyd’s Spatial Data Analysis: An Introduction to GIS Users (2010) p 93-97 by Oxford Press. There’s also an explanation in the ArcGIS Pro documentation.

## Data Preparation

I visited the NYC Open Data page and pulled up the entry for 311 Service Requests. When previewing the data I used the filter option to narrow the records down to a small subset; I chose complaints that were created between June 1st and 30th 2022, where the complaint type began with “Noise”, which gave me about 75,000 records (it’s a noisy town). Then I hit the Export button and chose one of the CSV formats. CSV is a common export option from open data portals; as long as you have columns that contain latitude and longitude coordinates, you will be able to plot the records. The NYC portal allows you to filter up front; other data portals like the ones in Philly and DC package data into sets of CSV files for each year, so if you wanted to apply filters you’d use the GIS or stats package to do that post-download. If shapefiles or geoJSON are provided, that will save you the step of having to plot coordinates from a CSV.

With the CSV, I launched QGIS, went to the Data Source Manager, and selected Delimited Text. Browsed for the file I downloaded, gave the layer a common sense name, and under geometry specified Point coordinates, and confirmed that the X field was my longitude column and the Y field was latitude. Ran the tool, and the points were plotted in the basic WGS 84 longitude / latitude system in degrees, which is the system the coordinates in the data file were in (generally a safe bet for modern coordinate data, but not always the case).

The next step was to save these plotted points in a file format that stores geometry and allows us to do spatial analysis. In doing that step, I recommend taking two additional ones. First, verify that all of the plotted data have coordinates – if there are any records where lat and long are missing, those records will be carried along into the spatial file but there will be no geometry for them, which will cause problems. I used the Select Features by Expression tool, and in the expression window typed “Latitude” is not null to select all the features that have coordinates.

Second, transform the coordinate reference system (CRS) of the layer to a projected system that uses meters or feet. When we run the kernel tool, it will ask us to specify a radius for defining the density, as well as the size of the pixels for the output raster. Using degrees doesn’t make sense, as it’s hard for us to conceptualize distances in degrees, and they are not a constant unit of measurement. If you’ve googled around and read Stack Exchange posts or watched videos where a person says “You just have to experiment and adjust these numbers until your map looks Ok”, they were working with units in fractions of degrees. This is not smart. Transform the system of your layers!

I selected the layer, right clicked, Export, Save Selected Features As. The default output is a geopackage, which is fine. Otherwise you could select ESRI shapefile, both are vector formats that store geometry. For file name I browse … and save the file in a specific folder. Beside CRS I hit the globe button, and in the CRS Selector window typed NAD83 Long Island in the filter at the top, and at the bottom I selected the NAD83 / New York Long Island (ftUS) EPSG 2263 option system in the list. Every state in the US has one or more state plane zones that you can select for making optimal maps for that area, in feet or meters. Throughout the world, you could choose an appropriate UTM zone that covers your area in meters. For countries or continents, look for an equidistant projection (meters again).

Clicked a series of Oks to create the new file. To reset my map window to match CRS of the new file, I selected that file, right clicked, Layer CRS, Set Project CRS from Layer. Removed my original CSV to avoid confusion, and saved my project.

## Kernel Density Estimation

Now our data is ready. Under the Processing menu I opened the toolbox and searched for kernel to find Heatmap (Kernel Density Estimation) under the Interpolation tools. The tool asks for an input point layer, and then a radius. The radius is used to define an area for calculating a local density estimate around each point. We can use a formula to determine an ideal radius; the hopt method seems to be commonly employed for this purpose.

To use the hopt formula, we need to know the standard distance for our layer, which measures the degree to which features are dispersed around the spatial mean or center of the distribution. A nice 3rd party plugin was created for calculating this. I went to the the plugins menu, searched for the Standard Distance plugin, and added it. Searched for it in the Processing toolbox and launched it. I provided my point layer for input, and specified an output file. The other fields are optional (if we were measuring an attribute of the points instead of the density of the points, we could specify the attribute as a weight column). The output layer consists of a circle where the center is the mean center of the distribution, and the circle represents the standard deviation. The attribute table contains one record, with the standard distance attribute of 36,046.18 feet (if no feature was created, the likely problem is you have records in the point file that don’t have geometry – delete them and try again).

Knowing this, I used the hopt formula:

``=((2/(3N))^0.25)SD``

Where N is the number of features and SD is the standard distance. I used Excel to plug in these values and do the calculation.

``((2/(374526))^0.25)36046.18 = 1971.33``

Finally, I launched the heatmap kernel tool, specified my noise points as input, and the radius as 1,971 feet. The output raster size does take some experimentation. The larger the pixel size, the coarser or more general the resolution will be. You want to choose something that makes sense based on the size of the area, the number of points, and / or some other contextual information. Just like the radius, the units are based on the map units of your layer. If I type in 100 feet for Pixel X, I see I’ll have a raster with 1,545 rows and 1,565 columns. Change it to 200 feet, and I get 773 by 783. I’ll go with 200 feet (the distance between a “standard” numbered street block in midtown Manhattan). I kept the defaults for the other options.

The resulting raster was initially displayed in black and white. I opened the properties and symbology menu and changed the render type from Singleband gray to Singleband pseudocolor, and kept the default yellow to red scheme. Voila!

In June 2022 there were high clusters of noise complaints in north central Brooklyn, northern Manhattan, and the southwest portion of the Bronx. There’s a giant red hot spot in the north central Bronx that looks like the storm on planet Jupiter. What on earth is going on there? I flipped back to the noise point layer and selected points in that area, and discovered a single address where over 2,700 noise complaints about a loud party were filed on June 18 and 19! There’s also an address on the adjacent block that registered over 900 complaints. And yet the records do not appear to be duplicates, as they have different time stamps and closing dates. A mistake in coding this address, multiple times? A vengeful person spamming the 311 system? Or just one helluva loud party? It’s hard to say, but beware of garbage in, garbage out. Beyond this demo, I would spend more time investigating, would try omitting these complaints as outliers and run the heatmap tool again, and compare this output to different months. It’s also worth experimenting with the color classification scheme, and some different pixel sizes.

## Contour Lines

Another interesting way to visualize this data would be to generate contour lines based on the kernel output. I did a search for contour in the processing toolbox, and in the contour tool I provided the kernel noise raster as the input. For intervals between contour lines I tried 20 feet, and changed the attribute name to reflect what the contour represents: COMPLAINT instead of ELEV. Generated the new file, overlaid on top of the kernel, and now you can see how it represents the “elevation” of complaints.

Switch the kernel off, symbolize the contours and add some labels, and throw the OpenStreetMap underneath, and now you can explore New York’s hills and valleys of noise. Or more precisely, the hills and valleys of noise complainers! In looking at these contours, it’s important to remember that they’re generated from the kernel raster’s grid cells and not from the original point layer. The raster is a generalization of the point layer, so it’s possible that if you look within the center of some of the denser circles you may not find, say, 340 or 420 actual point complaints. To generate a more precise set of contours, you would need to decrease the pixel size in the kernel tool (from say 200 feet to 100).

It’s interesting what you can create with just one set of points as input. Happy mapping!

# QGIS 3.16 Tutorial Workbook

I just released a new edition of my introductory QGIS manual for QGIS 3.16 Hannover (the current long term release), and as always I’m providing it under Creative Commons for classroom use and self-directed learning. I’ve also updated my QGIS FAQs handout, which is useful for new folks as a quick reference. This material will eventually move to a Brown University website, but when that happens I’ll still hold on to my page and will link to the new spot. I’m also leaving the previous version of the tutorial written for QGIS 3.10 A Coruna up alongside it, but will pull that down when the fall semester begins.

The new edition has a new title. When I first wrote Introduction to GIS Using Open Source Software, free and open source (FOSS) GIS was a novelty in higher ed. QGIS was a lot simpler, and I had to pull in several different tools to accomplish basic tasks like CRS transformations and calculating natural breaks. Ten years later, many university libraries and labs with GIS services either reference or support QGIS, and the package is infinitely more robust. So a name change to simply Introduction to GIS with QGIS seemed overdue.

My move from Baruch CUNY to Brown prompted me to make several revisions in this version. The biggest change was swapping the NYC-based business site selection example with a Rhode Island-based public policy one in chapters 2 and 3. The goal of the new hypothetical example is to identify public libraries in RI that meet certain criteria that would qualify them to receive funding for after school programs for K-12 public school students (replacing the example of finding an optimal location for a new coffee shop in NYC). In rethinking the examples I endeavored to introduce the same core concepts: attribute table joins, plotting coordinates, and geoprocessing. In this version I do a better job of illustrating and differentiating between creating subsets of features by: selecting by attributes and location, filtering (a new addition), and deleting features. I also managed to add spatial joins and calculated fields to the mix.

Changes to chapter 4 (coordinate reference systems and thematic mapping) were modest; I swapped out the 2016 voter participation data with 2020 data. I slimmed down Chapter 5 on data sources and tutorials, but added an appendix that lists web mapping services that you can add as base maps. Some material was shuffled between chapters, and all in all I cut seven pages from the document to slim it down a bit.

As always, there were minor modifications to be made due to changes between software versions. There were two significant changes. First, QGIS no longer supports 32 bit operating systems for Windows; it’s 64 bit or nothing, but that seems to be fairly common these days. Second, the Windows installer file is much bigger (and thus slower to download), but it helps insure that all dependencies are there. Otherwise, the differences between 3.16 and 3.10 are not that great, at least for the basic material I cover. In the past there was occasionally a lack of consistency regarding basic features and terminology that you’d think would be well settled, but thankfully things are pretty stable this time around.

If you have any feedback or spot errors feel free to let me know. I imagine I’ll be treading this ground again after the next long term release take’s 3.16’s place in Feb / Mar 2022. For the sake of stability I always stick with the long term release and forego the latest ones; if you’re going to use this tutorial I’d recommend downloading the LTR version and not the latest one.

# OpenStreetMap Data with ArcGIS Pro and QGIS

A couple years ago I wrote a post that demonstrated how to use the QuickOSM plugin for QGIS to easily extract features from the OpenStreetMap (OSM). The OSM is a great source for free and open GIS data, especially for types of features that are not captured in government sources, and for parts of the world that don’t possess a free or robust GIS data infrastructure. I’ve been using ArcGIS Pro more extensively in my new job and was wondering how I could do the same thing: query features from the OSM based on keys and values (denoting feature type) and geographic area and extract them as a vector layer. I’m looking for straightforward solutions that I could use for answering questions from students (so no command line tricks or database stuff). In this post I’ll cover three approaches for achieving this in ArcGIS Pro, with references to QGIS.

## File Approach

The most straightforward method would be to export data directly from the main OSM page by zooming into an area and hitting the Export button. This is a pretty blunt approach, as you have to be zoomed in pretty close and you grab every possible feature in the view. The “native” file format of OSM is the osm / pbf format; .osm is an XML file while .pbf is a compressed binary version of the osm. QGIS is able to handle these files directly; you just add them as a vector layer. ArcGIS Pro cannot. You have to download and install a special Data Interoperability extension, which is an esoteric thing that’s not part of the standard package and requires a special license from your site license coordinator.

A better and more targeted approach is to download pre-created extracts that are provided by a number of organizations listed in the OSM wiki. I started with Geofabrik in Germany, as it was a source I recognized. They package OSM data by geographic area and feature type. On their main page they list files that contain all features for each of the continents. These are enormous files, and as such they are only provided in the osm pbf format as shapefiles can’t effectively handle data that size. Even if you downloaded the osm pbf files and added them to QGIS, the software will struggle to render something that big.

But all is not lost; Geofabrik and many other providers package data in a shapefile format for smaller areas, provided that the size and number of features is not too great. For instance, on Geofabrik’s download page if you click on North America you’re presented with country extracts for that continent (see images below). You can get shapefiles for Greenland and Mexico, but not Canada or the US as the files are still too big. Click on US, and you’re presented with files for each of the states. No luck for California (too big), but the rest of states are small enough that you can get shapefiles for all of them.

I downloaded and unzipped the file for Rhode Island. It contains a number of individual shapefiles classified by type of feature: buildings, land use, natural, places, places of worship (pofw), points of interest (pois), railways, roads, traffic, transport, water, and waterways. Many of the files appear twice: files with an “a” suffix represent polygons (areas) while files without that suffix are points or lines. Some OSM features are stored as polygons when such detail is available, while others are represented as points.

For example, if I add the two places of workship files to a map, for some features you have the outline of the actual building, while for most you simply have a point. After adding the layers to the map, you’ll probably want to use Select by Attribute to select the features you want based on OSM tags with keys and values, and Select by Location in conjunction with a separate boundary file to pull data out for a smaller area. The Geofabrik OSM attribute table is limited to basic attributes: an OSM ID, feature code and class, and name. It’s also likely that you’ll want to unify the point and polygon features of the same type into one layer, as they’re usually mutually exclusive. Use the Centroid (Polygon) tool in the toolbox to turn the polygons into points, and the Merge tool to meld the two point layers together. In QGIS the comparable tools under the Vector menu are Centroids and Merge Vector Layers. WGS 84 is the default CRS for the layers.

Geofabrik is just one option. There are several others and they take different approaches for structuring their extracts. For example, BBBike.org organizes their layers by city for over 200 cities around the world, and they provide a number of additional formats beyond OSM PBF and shapefiles, such as Garmin GPS, GeoJSON, and CSV. They divide the data into fewer files, and if they don’t compile data for the area you’re interested in you can use a web-based tool to create a custom extract.

## Plugin Approach

It would be nice to use a plugin, as that would allow you to specify a custom geographic area and retrieve just the specific features you want. QuickOSM works quite nicely for QGIS. Fortunately there is a good ArcGIS Pro solution called OSMquery. It works for both Pro and Desktop, tested for Pro 2.2 and Desktop 10.6. I’m using Pro 2.7 and the basic tool worked fine. It’s well documented, with good instructions for installation and use.

The plugin is written in Python and you add it as a tool to your ArcToolbox. Download the repo from the OSMquery GitHub as a ZIP file (click the green code button and choose Download ZIP). Save it in or near your ArcGIS project folders, and unzip it. In Pro, go into a project and open a Catalog Pane in the View ribbon. Right click on Toolbox to add a new one, and browse to the folder you unzipped to add the tool. There are two scripts in the box, a basic and an advanced version. The basic tool functioned without trouble for me. The advanced tool threw an error, probably some Python dependency issue (I didn’t investigate as the basic tool met my needs).

In the basic tool you choose the key and value for the features you want to extract; the dropdown menu is automatically populated with these options. For the geographic extent you can enter a place name, or you can use the extent of the current map window or of a layer in the project, or you can manually type in bounding box coordinates. Another nice option is you can transform the CRS of the extracted features from WGS 84 to another system, so it matches the CRS of layers in your existing project. Run the tool, and the features are extracted. If the features exist as both points and polygons, you get two separate files for each. If you choose, you can merge them together as described in the previous section; this is a bit tougher as the plugin approach yields a much wider selection of fields in the attribute table, and not all of the point and polygon attributes align. With the Merge tool in Pro you can select which attributes you want to hold on to, and common ones will be merged. QGIS is a bit messier in this regard, but in my earlier post I outlined a work-around using a spatial database.

## Web Feature Service

This initially seemed to be the most promising route, but it turned out to be a dud. Like QGIS, Pro allows you to add OSM as a tiled base map. But ESRI also offers OSM as a web feature service: by hitting Add Data on the Map ribbon and searching the Living Atlas for “OpenStreetMap” you can select from a number of OSM web feature services, organized by continent and feature type. Once you add them to a map, you can select and click on individual features to see their name and feature type. The big problem is that you are not allowed to extract features from these layers, which leaves you with an enormous and heterogeneous mix of features for an entire continent. You can interact with the features, selecting by attribute and location in reference to other spatial layers, but that’s about it.

## In Summary

I would recommend taking the step of downloading the OSMquery plugin for ArcGIS Pro if you want to take a highly targeted approach to OSM feature extraction (for QGIS users, enable the QuickOSM plugin). This approach is also best if you can’t download a pre-existing extract for your area because it’s too large or has too many features, and if you want to access the fullest possible range of attribute values. Otherwise, you can simply download one of the pre-created extracts, and use your software to winnow it down to what you need (or if you do need everything, the file approach makes more sense). Since the file-based option includes fewer attributes, converting polygon features to points and merging them with the other point features is a bit simpler.

# A New Year and a New Start

I have some news! After 13 1/2 years, January 31, 2021 will be my last day as the Geospatial Data Librarian at Baruch College, City University of New York (CUNY). On February 1, 2021, I will be the new GIS and Data Librarian at Brown University in Providence, Rhode Island!

It’s an exciting opportunity that I’m looking forward to. I will be building geospatial information and data services in the library from the ground up, in concert with many new colleagues. I will be working closely with the Population Studies Training Center (PSTC) and the Spatial Structures in Social Sciences (S4) as well as the Center for Digital Scholarship within the library. Several aspects of the position will be similar, as I will continue to provide research and consultation services, create research guides and tutorials, teach workshops, collect and create datasets, and eventually build and manage a data repository and small lab where we’ll provide services and peer mentor students.

The resources I’ve created at Baruch CUNY will remain accessible, and eventually a new person will take the reins. I have moved the latest materials for the GIS Practicum, my introductory QGIS tutorial and workshop, to this website and I hope to continue updating and maintaining this resource. There are a lot of people throughout CUNY that I’m going to miss, at: the Newman Library, the CUNY Institute for Demographic Research, the Weissman Center for International Business, the Marxe School, Baruch’s Journalism Department, the Geography Department at Lehman College, the Digital Humanities program and the CUNY Mapping Service at the CUNY Graduate Center, and many others.

I will continue writing posts and sharing tips and resources here based on my new adventures at Brown, but may need a little break as I transition… stay tuned!

Best – Frank

# Adding Basemaps to QGIS With Web Mapping Services

For this final post of 2020, I was looking back through recent projects for something interesting yet brief; I’ve been writing some encyclopedia-length posts lately and wanted to keep this one on the lighter side. In that vein, I’ve decided to share a short list of free web mapping services that I use as basemaps in QGIS (they’ll work in ArcGIS too). This has been on my mind as I’ve recently stumbled upon the OpenTopoMap, which is an alternate stylized version of the OpenStreetMap that looks pretty sharp.

See this earlier post for details, but in short, to connect to these services in QGIS:

1. Select the appropriate web map service type in the browser panel (usually WMS / WMTS or XYZ Tiles), right click, and add new connection.
2. Give it a meaningful name, paste the appropriate URL into the URL box, click OK.
3. In the browser panel drill down to see the service, and for WMS / WMTS layers you can drill down further to see specific layers you can add.
4. Select the layer and drag it into the window, or select, right click, and add the layer to the project.
5. If the resolution looks off, right click on a blank area of the toolbar and check the Tile Scale Panel. Use this to adjust the zoom for the web map. If the scale bar is greyed out you’ll need to set the map window to the same CRS as the map service: select the layer in the panel, right click, and choose set CRS – set project CRS from layer.
6. Some web layers may render slowly if you’re zoomed out to the full extent, or even not at all if they contain many features or are super detailed. Conversely, some layers may not render if you’re zoomed too far in, as tiles may not be available at that resolution. Experiment!

If you’re an ArcGIS user see these concise instructions for adding various tile layers. This isn’t something that I’ve ever done, as ArcGIS already has a number of accessible basemaps that you can add.

In the list below, links for the service name take you to either the website version of the service, or to a list of additional layers that you can connect to. The URLs that follow are the actual connections to the service that you’ll use within your GIS package. If you use OSM, OTP, or Stamen in your maps, make sure to cite them (they use Creative Commons Licenses – follow links to their websites for details). The government sources are public domain, but you should still cite them anyway. Happy mapping, and happy holidays!

OpenStreetMap XYZ Tile (global)

``http://tile.openstreetmap.org/{z}/{x}/{y}.png``

OpenTopoMap XYZ Tile (global)

``https://tile.opentopomap.org/{z}/{x}/{y}.png``

Stamen XYZ Tile (global) see their website for examples; the image topping this post is from watercolor

``http://tile.stamen.com/terrain/{z}/{x}/{y}.png``
``http://tile.stamen.com/toner/{z}/{x}/{y}.png``
``http://tile.stamen.com/watercolor/{z}/{x}/{y}.jpg``

USGS National Map WMTS (global, but fine detail is US only)

``````Imagery:
https://basemap.nationalmap.gov/arcgis/rest/services/USGSImageryOnly/MapServer/WMTS/1.0.0/WMTSCapabilities.xml

Imagery & Topo:
https://basemap.nationalmap.gov/arcgis/rest/services/USGSImageryTopo/MapServer/WMTS/1.0.0/WMTSCapabilities.xml

Topographic:
https://basemap.nationalmap.gov/arcgis/rest/services/USGSTopo/MapServer/WMTS/1.0.0/WMTSCapabilities.xml``````

US Census Bureau TIGERweb WMS (US only) see their website for older vintages

``````Current TIGER features:
https://tigerweb.geo.census.gov/arcgis/services/TIGERweb/tigerWMS_Current/MapServer/WMSServer

Current physical features:
https://tigerweb.geo.census.gov/arcgis/services/TIGERweb/tigerWMS_PhysicalFeatures/MapServer/WMSServer``````

# QGIS 3.10 Tutorial Workbook

I just posted an updated version of my QGIS tutorial / workbook manual, Introduction to GIS Using Open Source Software. This version was written for QGIS 3.10 A Coruña, which recently superseded QGIS 3.4 Madeira as the current Long Term Release (LTR). The LTR is intended to be more stable than the current releases and is supported for at least a year.

The workbook was designed to accompany a day-long introductory workshop that I teach and is divided into five chapters. Chapter 1 is a broad and concise overview of GIS, chapters 2 to 4 are hands-on exercises that cover: the basics of using the interface and the difference between vectors and rasters (chapter 2), a site selection analysis that demonstrates geoprocessing, spatial selection, table joins, coordinate plotting, expressions, and spatial analysis (chapter 3), and a thematic mapping example that illustrates coordinate reference systems (CRS), data classification, and mapping (chapter 4). Chapter 5 summarizes data sources and resources for learning more about GIS. In chapters 2 to 4 the steps for doing the exercises are kept concise with many screenshots, while detailed commentary explaining how everything works follows.

The manual and tutorial data are freely available for personal and classroom use under a Creative Commons license. I’m providing the material for both 3.10 A Coruna and 3.4 Madeira for now, but will take down the latter at the end of the spring semester (late May 2020).

The changes between 3.4 and 3.10 are not dramatic as far as the basic tools and principles that I cover in the book go, but I thought an update was worthwhile as there are just enough changes that could trip up new users (see the 3.10 visual change log for a full list of software updates).  In addition to incorporating changes to the interface, I also took the opportunity to tighten up and condense the material. In particular, I consolidated the coordinate reference system (CRS) exercises in chapter 4 from two sections to one, because in practice I found that it was overkill for a one-day session.

Here are a few noteworthy changes to the tutorial and software that impact novice users:

1. The default setting for the toolbar buttons is rather small, so during the setup phase in chapter 2 I inserted an optional step to make them bigger. Go to: Settings > Options > General tab, and under the Application section change the icon size from 16 to 24.
2. In 3.10, when new files are generated from geoprocessing operations and added to your project, the layers appear in the layers panel with the name you give them. In 3.4 they were assigned generic aliases like “Clipped” and “Buffer” based on the process you ran.
3. In 3.10 the “Quantiles” classification scheme has been replaced with “Equal Counts”. Same scheme, different terminology.
4. There’s now a dedicated north arrow button in the map layout screen. In 3.4 and earlier versions you added an arrow by selecting the add image button.
5. In 3.10, every time you add a layer with a CRS that doesn’t match the existing CRS of the window, you’re presented with a datum transformation screen to modify the file you’re adding. This is a helpful warning if you already have existing layers in your project that match the window and your new file doesn’t, but it’s annoying when you’re trying to add files to a blank window in a new project. You can turn this feature off under: Options > Settings > CRS tab, under Default Datum Transformations uncheck the box for Ask for datum transformation.

It’s hard to believe that this is the 10th edition I’ve published in the past ten years. QGIS has certainly come a long way during that time. For a trip down memory lane, look at the 1st edition I wrote for QGIS 1.5 Tethys in 2011! Back then I wrote the whole thing in HTML… thankfully I “discovered” LaTeX a year later, and have used it for writing tutorials ever since.

If you wanted to learn GIS in general and QGIS in particular, spend a day with the manual and work through the exercises and you’ll have a good foundation. All the basics are there, as well as best practices and the “gotchas” that tend to trip people up.