Databases
Identifying Missing Values in CSV Data Thoroughly scanning CSV files for missing data is a critical first step in handling null or blank values. This includes both visually checking for empty cells and programmatically checking for numeric missing value placeholders like -9999. Pay attention to data types as text fields can hide missing numerical data….
Integrating Diverse Data Sources The problem: Silos of location-based data In many organizations, location-based data exists in isolated silos across departments and systems. Valuable data assets like sensor readings, asset inventories, maintenance records, and operational logs often end up fragmented and disconnected. This leads to critical information gaps that hinder analysis and decision making. Data…
Specifying Conditions with Python Variables Python variables can be used within SQL statements to substitute concrete values in place of placeholders. This parameterization technique allows developers to construct reusable queries where the conditions can be changed by altering the Python code rather than having to modify the SQL statements themselves. For example, if we wanted…
The Problem: Displaying Spatial Data Without Coordinate Fields Spatial data such as points, lines, and polygons often lack numeric coordinate fields that specify geographic locations. This poses challenges for QGIS users who need to calculate distances, create spatial indexes, or work with data in coordinate-based workflows. Without longitude and latitude fields, spatial data layers have…
Ensuring Accurate Geospatial Data The quality and consistency of geospatial data is crucial for accurate analysis and decision making in geographic information systems (GIS). Inaccurate or inconsistent data leads to unreliable results and invalid conclusions. There are several best practices organizations can implement to improve the accuracy of their spatial data. The first step is…
Managing Massive Geospatial Datasets The exponential growth of geospatial data presents new challenges in storing, processing, analyzing, and visualizing large datasets. With terabytes of aerial imagery, lidar scans, satellite data, and vector data being generated daily, traditional desktop Geographic Information Systems (GIS) software can no longer handle the massive volume and velocity of geospatial big…
Understanding Validity Errors in Parcel Data Validity errors refer to problems in tax parcel geospatial data that cause the dataset to be invalid or not meet quality standards. These errors prevent parcels from being accurately represented spatially and analyzed properly. Common validity errors include inaccurate coordinate data, overlapping parcels, and gaps between parcels. It is…
