ArcGIS Base Maps: Key Concepts, Issues, and Cartography Techniques

Base Maps: Concept and Purpose

• A base map is the background compilation of data or layers that provides a frame of reference for cartography. It is visually aesthetic and serves as the foundation upon which you load other data sets.
• In practice, you can begin with base maps from canned collections (e.g., ESRI) or by constructing your own base map from local data layers.
• The instructor demos using a roadways layer for Wilson, North Carolina to illustrate how base maps interact with additional data.

How Base Maps are Generated: Caching and Rasterization

• Base map layers are compressed into raster formats (e.g., JPEG) and then cached at multiple zoom levels to optimize performance over the internet.
• Caching means that at different scales, the map loads pre-processed image tiles rather than re-rendering from scratch.
• Example concepts:
  • A cache level might include pixels of size $3''$ (three-inch resolution) when viewed at a scale like $1:100$. Displaying three-inch pixels at this scale would be overkill because the smallest discernible feature is on the order of meters.
  • Good base map systems typically have $4, 5, 6, 7$ cache levels to push data efficiently.
• Visual implication: as you zoom, the base map may switch to different data sources at different cache levels, which can alter color and detail (see next section).

Scale, Cache, and Coloration: What Happens When You Zoom

• When you zoom in, ESRI and other providers may reload the base map with a different underlying data source at a new cache level.
• This can cause noticeable changes in color tint and overall appearance as the data source changes (e.g., from a national imagery program to a local dataset).
• As you zoom and then zoom back out, you may see color shifts and a crisper resolution due to more detailed data becoming available at finer cache levels.
• In layout view, the intended printed resolution is shown (e.g., the scale in layout vs. the data view). You may encounter a mismatch between what you expect to print and what the cache level delivers at that scale, potentially losing detail.
• Practical takeaway: base maps look nice, but if your production scale is far from the base map’s cache levels, quality can degrade and the map may look inconsistent in print or export.

Layout vs. Data View: The Cache Gap Problem

• In layout view, the map is prepared for printing at a specified scale (e.g., $1:1000$ or $1:10{,}000$).
• If the cache levels used by the base map are not aligned with this scale, you’ll get gaps in data resolution or unexpected recombination of tiles.
• This creates a tension between aesthetic basemaps and the fidelity of your actual map product.

Data Currency and Feature Classification: Currency vs. Completeness

• The base map’s data currency (how up-to-date the features are) and the local data’s currency may diverge.
• Examples observed in the transcript:
  • Local roads drawn in the base map may not reflect recent changes, or may be duplicated with different classifications.
  • Municipal roads visible in ESRI’s Streets base map may not be present in the local dataset, or vice versa, leading to discrepancies.
  • A road that exists as a parking lot in reality could be labeled as a roadway in a base map, due to differences in classification conventions.
• General rule of thumb: local data sources are often higher quality than a canned base map, particularly for current, in-situ features.
• Consequences: mismatches between base map data and local data can cause confusion in navigation, analysis, and presentation.

Currency, Completeness, and Classification: Key Problems with Base Maps

• Three main issues identified:
  1) Cache level mismatches: predefined cache levels create gaps or degraded detail when the production scale doesn’t align with the map’s caching strategy.
  2) Data currency and feature classification: time-based discrepancies and differing feature classifications between the base map and local data.
  3) Timeliness and consistency: base map data may not be timely, and classification schemes vary across sources.
• The result is potential misinterpretation or misrepresentation, especially when precise features matter (roads, parking lots, public buildings).

Demonstrations: Base Maps in Action

• ESRI “canned” base maps can be visually appealing but may require careful handling of credits and scale compatibility.
• A common issue shown is credit text cluttering the map; dynamic text (Insert > Dynamic Text > Service Layer Credits) can be used to place credits more cleanly and legibly, and can be adjusted in font and position.
• Base map sources to consider:
  • National maps (e.g., ESRI’s TOVA/National Map base map)
  • USGS topo maps (e.g., topos at $1:25{,}000$ scale; default USGS topo often at $1:24{,}000$)
  • Landscape/base maps (simplified layers such as land use/land cover)
• Real-time or rest services (e.g., weather radar) can be overlaid on base maps for dynamic mapping, but may also introduce label clutter or readability issues.
• Practical tip: you can connect to web services (REST/WMS) as base maps or data layers to quickly assemble maps without downloading data locally.

Working with External Services: REST, WMS, and GIS Servers

• Web services enable you to pull data into ArcGIS without storing it locally.
• REST services: You can locate a REST service directory, copy the URL, and add it as a GIS server in ArcGIS to fetch data layers (e.g., real-time radar data).
• WMS (Web Mapping Service) is another way to access online layers and include them in your map.
• Example workflow shown:
  • Search for real-time radar data via REST services
  • Add the service as a GIS server or as a direct REST/WMS layer
  • Overlay radar data with base maps to visualize events in real time
• Caveat: label rendering can become obscured when overlaid on busy base maps.

Building Your Own Base Map: A Practical Workflow

• Rationale: create a customized base map that aligns with your data’s projection, scale, and styling, avoiding the limitations of canned base maps.
• Steps demonstrated:
  • Reproject data to the desired coordinate system (e.g., from Web Mercator to UTM zones such as 18N or 15N NAD83).
  • Build a local dataset such as Ballard County 911 base map with road network and associated attributes (e.g., roadway class).
  • Create a roadway class attribute (local, state, US) to drive symbology.
  • Use the Symbology tab to classify by categories (local, state, US) and assign a common white line style, then refine with thicker lines for major roads.
  • Adjust symbol properties to achieve a visually pleasing map that resembles familiar basemaps (e.g., Google-like road styling) while preserving data accuracy.
  • Use symbol levels to ensure proper casing and connectivity where lines meet.
  • Tweak label placement using Maplex label engine, and apply halos for readability (e.g., halo size around labels).
  • Define label classes with SQL-like expressions (e.g., roadclass = 'L' or roadclass = NULL) to tailor labeling by category.
  • For complex label management, convert labels to annotation for manual adjustment (rotation, offset, alignment) within the map frame.
  • Create multiple labeled representations for different scales and copy data to multiple layers for consistent label and symbol behavior across zoom levels.
• Result: a reusable base map layered with your own data, which can be exported as a Layer Package for offline sharing or reuse.

Cartography Techniques: Labeling, Symbolization, and Annotation

• Labeling challenges:
  • Choosing an appropriate font and size (e.g., moving from Arial to Garamond for readability).
  • Enabling Maplex label engine for better placement and legibility.
  • Setting placement properties (e.g., horizontal, curved, centered) and adjusting leading (the vertical space between lines).
  • Adding a halo (mask) behind labels to improve contrast against busy backgrounds (e.g., halo size 1.5).
  • Grouping or categorizing features for multi-class labeling (local, state, US highways) with distinct label styles.
  • Aliasing issues when a single feature’s label changes as you move across Z-levels; mitigate via explicit fields for highway numbers and aliases.
• Symbolization strategies:
  • Create a local road class with a simple white line; add a “cased” look by surrounding with a darker border; use symbol levels to ensure proper stacking and connectivity.
  • Differentiate highways (state vs US) with distinct line widths and symbol markers to resemble real-world signage (e.g., using predefined US Highway markers).
  • Adjust font size and label orientation to maintain readability across the map.
• Data structure and labeling:
  • Ensure attribute tables include fields needed for labeling (e.g., roadclass, roadnumber, street_name) and verify that fields exist to support the labeling rules.
  • If necessary, create additional fields or adjust expressions to avoid aliasing (e.g., label shows a name instead of a number when appropriate).
  • Use annotation mode for precise label placement and to edit individual labels after conversion.

Credits and Citations: Cartography Etiquette

• Base map credits can clutter the map. It’s often useful to:
  • Move credits off the map area using Insert > Dynamic Text > Service Layer Credits and adjust font, size, and placement.
  • Remove or simplify credit text when it interferes with readability, but ensure proper credit to data sources when required by licensing. Note that deleting credits may cause them to reappear due to map documents’ dynamic text rules.
• Best practice: keep credits legible but unobtrusive to preserve marginalia and data emphasis.

Practical Workflow Tips and Real-World Relevance

• When starting a session, the first loaded layer often sets ArcMap’s default projection; base maps often use Web Auxiliar y Mercator. Reprojection on the fly can be costly and slow; plan by:
  • Starting with the base layer that matches your projection, or
  • Reprojecting your data to the base map’s projection before combining layers.
• Understand the trade-offs between convenience and accuracy when using canned base maps:
  • Pros: quick, good-looking basemaps; easy to layer on data; access to up-to-date imagery in some cases.
  • Cons: potential scale issues, misalignment with local data, data currency concerns, and possible labeling conflicts.
• In disaster mapping or time-sensitive work, using REST/WMS services and combining base maps with live data can be powerful but requires careful handling of projection, scale, and label readability.
• Version considerations: ArcGIS versions (e.g., 10.3 vs 10.5) may influence compatibility and available features; consider what your target users run when sharing maps.
• Final takeaway: base maps are useful, but professional cartography often benefits from creating your own base map (layered, tailored to the data, and optimized for the intended print or web scale) to ensure consistency, readability, and control over the final product.

Summary in one sentence: Base maps provide ready-made cartographic backdrops, but their internal caching, data sources, and projections require careful management and, when necessary, custom base maps built from your own data for precise, publication-quality outputs.