GIS Data Creation
Creating GIS (Geographic Information System) data involves several steps to gather, organize and input geographical information into a digital database for spatial analysis and mapping. Here is a detailed guide on how to create GIS data:
Digitize & Capture
By effectively digitizing and capturing GIS data, you ensure accurate and reliable representation of geographic features, supporting various spatial analysis and decision-making processes.
✓ Converting hardcopy maps, plans, or images into vector data by defining the spatial features.
✓ Using GIS software, import the scanned or digital image and trace the spatial features such as points, lines and polygons.
✓ Ensure high precision by setting appropriate snapping options and using tools like heads-up digitizing for accurate feature placement.
Data Capture
✓ Data capture involves obtaining spatial and attribute data through direct measurement, GPS, remote sensing, or surveying.
✓ Use GPS devices to collect spatial coordinates of points, tracks and polygons directly in the field.
✓ Utilize satellite imagery or aerial photography to capture land cover, land use and other spatial information.
✓ Conduct ground surveys to capture detailed spatial data, e.g: property boundaries, infrastructure details.
Attribute Assignment & Georeferencing
✓ Once the spatial features are digitized or captured, assign descriptive attributes such as land use type, elevation, population, etc.. to the features.
✓ Use standardized attribute classifications and coding systems for consistency and ease of analysis.
Standardizing Data Attributes & QA/QC
Standardizing data attributes in Geographic Information Systems (GIS) is crucial for ensuring consistency and accuracy in spatial analysis.
Accuracy Considerations
✓ Calibrate digitizing tools to ensure accurate scaling and positioning.
✓ Validate captured data against real-world ground truth to ensure accuracy and reliability.
✓ Implement quality control measures to minimize errors during digitizing and data capture processes.
Quality department plays major role in CloudWayZ work flow.
It is connected to all departments.
In every stage data passes through Quality department.
QC process is carried out by following methods.
Visual QC – This is carried out by checking data visually by comparing with different sources.
Automated QC – This is carried out by checking data with automated tools developed by CloudWayZ team. There are various tools developed to check accuracy, completeness and correctness of every attribute as per specification.
In CloudWayZ QC is carried out following stages.
Phase–1 QC ( Passing Criteria QC % ❱ 96% )
Phase–2 QC ( Passing Criteria QC % ❱ 98% )
Phase–3 QC on merged data ( Passing Criteria QC % ❱ 98% )
Completeness Test – PR –DCT QC
Correctness Test – Field Test QC
Data Collection & File Conversion
When it comes to data collection and file conversion in Geographic Information Systems (GIS), several essential considerations come into play. Here's a comprehensive breakdown:
✓ Identification of Primary DataSources data from authoritative sources, surveys, fieldwork and remote sensing to ensure data accuracy and reliability.
✓ Accessing secondary data from archives, government agencies and commercial databases. Assess the quality and relevance of secondary data to verify its suitability for analysis.
✓ Field Data Collection utilizing GPS-enabled devices for field data collection, ensuring accurate geospatial information. Integrate collected field data with existing GIS datasets for comprehensive analysis.
✓ Implementing QC to validate the accuracy and precision of collected data through ground truthing and statistical validation techniques.
✓ Convert data between different GIS file formats such as shapefile (SHP), GeoJSON, KML and GeoPackage to ensure interoperability and compatibility across GIS platforms.
✓ Use specialized tools or techniques to convert raster data (e.g: satellite imagery) to vector format for efficient analysis and visualization.
✓ Convert data to different coordinate reference systems (CRS) using suitable projection algorithms to ensure consistency in spatial analysis and mapping.
✓ When converting data files, ensure that metadata such as attribute definitions, coordinate system information and data sources are preserved to maintain data integrity.
✓ Employ batch conversion tools or scripts to streamline the process of converting multiple files, ensuring efficiency and accuracy.
Cost Savings
We work with you to develop the most cost-effective geospatial data solution to meet your organization’s needs and budget
Time Efficiency
Our Team has 4 departments all having experienced veterans in GIS, making sure all the processes are streamlined and within the timeline
Skill Set
Having deep understanding of mathematical principles, analyzing complex problems and proficiency in using GIS engineering tools and software
Quality Control
We have Regular QC processes to help ensure that GIS data meets industry standards, regulatory requirements and organizational needs, contributing to accurate and informed spatial analysis
