Geospatial Without Maps

Posted on 2025-05-01 by Cercana

When most people hear “geospatial,” they immediately think of maps. But in many advanced applications, maps never enter the picture at all. Instead, geospatial data becomes a powerful input to machine learning workflows, unlocking insights and automation in ways that don’t require a single visual.

At its core, geospatial data is structured around location—coordinates, areas, movements, or relationships in space. Machine learning models can harness this spatial logic to solve complex problems without ever generating a map. For example:

Predictive Maintenance: Utility companies use the GPS coordinates of assets (like transformers or pipelines) to predict failures based on environmental variables like elevation, soil type, or proximity to vegetation (AltexSoft, 2020). No map is needed—only spatially enriched feature sets for training the model.
Crop Classification and Yield Prediction: Satellite imagery is commonly processed into grids of numerical features (such as NDVI indices, surface temperature, soil moisture) associated with locations. Models use these purely as tabular inputs to predict crop types or estimate yields (Dash, 2023).
Urban Mobility Analysis: Ride-share companies model supply, demand, and surge pricing based on geographic patterns. Inputs like distance to transit hubs, density of trip starts, or average trip speeds by zone feed machine learning models that optimize logistics in real time (MIT Urban Mobility Lab, n.d.).
Smart Infrastructure Optimization: Photometrics AI employs geospatial AI to enhance urban lighting systems. By integrating spatial data and AI-driven analytics, it optimizes outdoor lighting to ensure appropriate illumination on streets, sidewalks, crosswalks, and bike lanes while minimizing light pollution in residential areas and natural habitats. This approach not only improves safety and energy efficiency but also supports environmental conservation efforts (EvariLABS, n.d.).

These examples show how spatial logic—such as spatial joins, proximity analysis, and zonal statistics—can drive powerful workflows even when no visualization is involved. In each case, the emphasis shifts from presenting information to enabling analysis and automation. Features are engineered based on where things are, not just what they are. However, once the spatial context is baked into the dataset, the model itself treats location-derived features just like any other numerical or categorical variable.

Using geospatial technology without maps allows organizations to focus on operational efficiency, predictive insights, and automation without the overhead of visualization. In many workflows, the spatial relationships between objects are valuable as data features rather than elements needing human interpretation. By integrating geospatial intelligence directly into machine learning models and decision systems, businesses and governments can act on spatial context faster, at scale, and with greater precision.

To capture these relationships systematically, spatial models like the Dimensionally Extended nine-Intersection Model (DE-9IM) (Clementini & Felice, 1993) provide a critical foundation. In traditional relational databases, connections between records are typically simple—one-to-one, one-to-many, or many-to-many—and must be explicitly designed and maintained. DE-9IM extends this by defining nuanced geometric interactions, such as overlapping, touching, containment, or disjointness, which are implicit in the spatial nature of geographic objects. This significantly reduces the design and maintenance overhead while allowing for much richer, more dynamic spatial relationships to be leveraged in analysis and workflows.

By embedding DE-9IM spatial predicates into machine learning workflows, organizations can extract richer, context-aware features from their data. For example, rather than merely knowing two infrastructure assets are ‘related,’ DE-9IM enables classification of whether one is physically inside a risk zone, adjacent to a hazard, or entirely separate—substantially improving the precision of classification models, risk assessments, and operational planning.

Machine learning and AI systems benefit from the DE-9IM framework by gaining access to structured, machine-readable spatial relationships without requiring manual feature engineering. Instead of inferring spatial context from raw coordinates or designing custom proximity rules, models can directly leverage DE-9IM predicates as input features. This enhances model performance in tasks such as spatial clustering, anomaly detection, and context-aware classification, where the precise nature of spatial interactions often carries critical predictive signals. Integrating DE-9IM into AI pipelines streamlines spatial feature extraction, improves model explainability, and reduces the risk of omitting important spatial dependencies.

Harnessing geospatial intelligence without relying on maps opens up powerful new pathways for innovation, operational excellence, and automation. Whether optimizing infrastructure, improving predictive maintenance, or enriching machine learning models with spatial logic, organizations can leverage these techniques to achieve better outcomes with less overhead. At Cercana Systems, we specialize in helping clients turn geospatial data into actionable insights that drive real-world results. Ready to put geospatial AI to work for you? Contact us today to learn how we can help you modernize and optimize your data-driven workflows.

References

Clementini, E., & Felice, P. D. (1993). A model for representing topological relationships between complex geometric objects. ACM Transactions on Information Systems, 11(2), 161–193. https://doi.org/10.1016/0020-0255(95)00289-8

AltexSoft. (2020). Predictive maintenance: Employing IIoT and machine learning to prevent equipment failures. AltexSoft. https://www.altexsoft.com/blog/predictive-maintenance/

Dash, S. K. (2023, May 10). Crop classification via satellite image time-series and PSETAE deep learning model. Medium. https://medium.com/geoai/crop-classification-via-satellite-image-time-series-and-psetae-deep-learning-model-c685bfb52ce

MIT Urban Mobility Lab. (n.d.). Machine learning for transportation. Massachusetts Institute of Technology. https://mobility.mit.edu/machine-learning

EvariLABS. (2025, April 14). Photometrics AI. https://www.linkedin.com/pulse/what-counts-real-roi-streetlight-owners-operators-photometricsai-vqv7c/

Data Stewardship in AI, Geospatial, and Security Operations

Posted on 2025-04-30 by Cercana

In today’s AI-driven and geospatially enabled world, data is an organization’s most valuable asset — yet it is often treated as an afterthought until issues arise. Poor data quality, incomplete metadata, and inconsistent governance can quickly derail even the most sophisticated projects. At Cercana, we believe that data stewardship must be intentional, continuous, strategic, and embedded into every phase of the project lifecycle.

Why Data Stewardship Matters More Than Ever

The rapid adoption of artificial intelligence and machine learning means that models are only as good as the data that train them. In geospatial systems, slight inaccuracies — such as misaligned coordinates or outdated basemaps — can cascade into serious operational errors. Furthermore, organizations are increasingly judged not only on the outcomes they produce but also the quality and governance of the data they maintain, particularly under frameworks such as GDPR and emerging AI regulations (Voigt & von dem Bussche, 2017). Effective data stewardship is now essential for both technical success and regulatory compliance.

Research indicates that up to 80% of AI project failures can be traced back to issues of poor data quality (Gartner, 2021). Organizations that invest early in stewardship practices stand a much greater chance of building reliable, resilient systems.

Common Pitfalls When Stewardship Is Ignored

When stewardship is neglected common problems emerge quickly. Disparate data sources lead to inconsistencies that degrade model performance and decision-making. Metadata is often incomplete, particularly for spatial attributes like projection information or temporal validity, limiting future usability. Without lineage tracking, teams cannot verify the origin or reliability of their data, making validation nearly impossible. Additionally, mismatches in coordinate systems, uncontrolled enrichment, and poorly managed access rights introduces risks that compound over time. Without a defined stewardship process even the most promising initiatives can stagnate or fail outright.

Security Risks Tied to Poor Stewardship

In addition to operational challenges, poor data stewardship also introduces serious security risks. Mismanaged datasets can unintentionally expose sensitive information, particularly when metadata or spatial attributes reveal more than intended. Without proper lineage tracking and access control, organizations are vulnerable to unauthorized data manipulation, leakage, or corruption. Furthermore, compliance with emerging security and privacy standards increasingly depends on maintaining disciplined data governance practices (NIST, 2020). Strong stewardship is not only essential for quality and reliability — it is also critical for protecting organizational and national security interests.

Retrieval-Augmented Generation (RAG) systems, which combine data retrieval with AI driven content generation, are particularly vulnerable to a form of attack known as RAG poisoning. In this scenario, malicious or inaccurate data is intentionally inserted into the knowledge base leading the AI to retrieve and generate harmful or misleading outputs. Without strong data stewardship practices, including strict validation, provenance tracking and controlled ingestion pipelines, organizations may unwittingly expose themselves to these sophisticated new threats.

What Effective Data Stewardship Looks Like

Effective stewardship begins with discovery; cataloging all datasets including third-party and open-source feeds. Standardization of schemas, metadata fields and coordinate systems follows, ensuring consistency across applications. Enrichment activities such as gap-filling, normalization, and validation against authoritative sources elevates the quality of data available for analysis. Governance frameworks define who can create, edit, validate and retire datasets, providing necessary accountability. Finally, continuous monitoring using audits and stewardship KPIs ensures that quality standards are sustained overtime (Redman, 2018).

Many modern data platforms implement these stewardship principles using structured frameworks like the Medallion Architecture, which organizes data into Bronze (raw), Silver (cleaned) and Gold (curated) layers. This structured progression enforces discovery, standardization, enrichment and governance practices in a scalable way (Armbrust, Das, Zhu, & Xin, 2021). By applying stewardship systematically across each stage, organizations can build more resilient and trustworthy AI and geospatial systems.

Organizations leveraging open datasets must pay particular attention to validation as not all external sources meet the same quality thresholds required for mission-critical work.

How Cercana Helps Clients Get It Right

At Cercana, data stewardship is foundational not optional. We work with leading technologies such as Apache Airflow for orchestration, dbt for data transformation, Delta Lake for storage reliability, and PostGIS for advanced geospatial data management. We embed stewardship practices at the earliest phases of our projects ensuring strong, reliable data pipelines from day one. Our team brings expertise across metadata cataloging, ETL/ELT pipelines, geospatial validation and stewardship strategy development. Beyond tools and processes, we assist organizations in building a sustainable stewardship culture through team training and change management. We believe that good stewardship is as much about people and processes as it is about technology.

Conclusion

Data stewardship is no longer a back-office concern; it is a mission critical capability that underpins the success of AI, machine learning and geospatial analytics initiatives. Organizations that prioritize stewardship today will be best positioned to lead in an AI-driven, regulation-conscious future. To learn how Cercana can help you strengthen your data stewardship practices, contact us today.

References

Armbrust, M., Das, T., Zhu, X., & Xin, R. (2021). Delta Lake: High-performance ACID table storage over cloud object stores. Proceedings of the VLDB Endowment, 13(12), 3411–3424. https://doi.org/10.14778/3415478.3415560

Gartner. (2021). Gartner predicts 80% of AI projects will remain alchemy, run by wizards whose talents won’t scale. Retrieved from https://www.gartner.com/en/newsroom/press-releases/2021-03-17-gartner-predicts-80–of-ai-projects-will-stagnate

NIST. (2020). Security and Privacy Controls for Information Systems and Organizations (NIST Special Publication 800-53 Rev. 5). National Institute of Standards and Technology. https://doi.org/10.6028/NIST.SP.800-53r5

Redman, T. C. (2018). Data Driven: Profiting from Your Most Important Business Asset. Harvard Business Review Press.

Voigt, P., & von dem Bussche, A. (2017). The EU General Data Protection Regulation (GDPR): A Practical Guide. Springer.

Demystifying the Medallion Architecture for Geospatial Data Processing

Posted on 2025-02-26 by Cercana

Introduction

Geospatial data volumes and complexity are growing due to diverse sources, such as GPS, satellite imagery, and sensor data. Traditional geospatial processing methods face challenges, including scalability, handling various formats, and ensuring data consistency. The medallion architecture offers a layered approach to data management, improving data processing, reliability, and scalability. While the medallion architecture is often associated with specific implementation such as the Delta Lake, its concepts are applicable to other technical implementations. This post introduces the medallion architecture and discusses two workflows—traditional GIS-based and advanced cloud-native—to demonstrate how it can be applied to geospatial data processing.

Overview of the Medallion Architecture

The medallion architecture was developed to address the need for incremental, layered data processing, especially in big data and analytics environments. It is composed of three layers:

Bronze Layer: Stores raw data as-is from various sources.
Silver Layer: Cleans and transforms data for consistency and enrichment.
Gold Layer: Contains aggregated and optimized data ready for analysis and visualization.

The architecture is particularly useful in geospatial applications due to its ability to handle large datasets, maintain data lineage, and support both batch and real-time data processing. This structured approach ensures that data quality improves progressively, making downstream consumption more reliable and efficient.

Why Geospatial Data Architects Should Consider the Medallion Architecture

Geospatial data processing involves unique challenges, such as handling different formats (raster, vector), managing spatial operations (joins, buffers), and accommodating varying data sizes. Traditional methods struggle when scaling to large, real-time datasets or integrating data from multiple sources. The medallion architecture addresses these challenges through its layered approach. The bronze layer preserves the integrity of raw data, allowing for transformations to be traced easily. The silver layer handles transformations of the data, such as projections, spatial joins, and data enrichment. The gold layer provides ready-to-consume, performance optimized data ready for downstream systems.

Example Workflow 1: Traditional GIS-Based Workflow

For organizations that rely on established GIS tools or operate with limited cloud infrastructure, the medallion architecture provides a structured approach to data management while maintaining compatibility with traditional workflows. This method ensures efficient handling of both vector and raster data, leveraging familiar GIS technologies while optimizing data accessibility and performance.

This workflow integrates key technologies to support data ingestion, processing, and visualization. FME serves as the primary ETL tool, streamlining data movement and transformation. Object storage solutions like AWS S3 or Azure Blob Storage store raw spatial data, ensuring scalable and cost-effective management. PostGIS enables spatial analysis and processing for vector datasets. Cloud-Optimized GeoTIFFs (COGs) facilitate efficient access to large raster datasets by allowing partial file reads, reducing storage and processing overhead.

Bronze – Raw Data Ingestion

The process begins with the ingestion of raw spatial data into object storage. Vector datasets, such as Shapefiles and CSVs containing spatial attributes, are uploaded alongside raster datasets like GeoTIFFs. FME plays a crucial role in automating this ingestion, ensuring that all incoming data is systematically organized and accessible for further processing.

Silver – Data Cleaning and Processing

At this stage, vector data is loaded into PostGIS, where essential transformations take place. Operations such as spatial joins, coordinate system projections, and attribute filtering help refine the dataset for analytical use. Meanwhile, raster data undergoes optimization through conversion into COGs using FME. This transformation enhances performance by enabling GIS applications to read only the necessary portions of large imagery files, improving efficiency in spatial analysis and visualization.

Gold – Optimized Data for Analysis and Visualization

Once processed, the refined vector data in PostGIS and optimized raster datasets in COG format are made available for GIS tools. Analysts and decision-makers can interact with the data using platforms such as QGIS, Tableau, or Geoserver. These tools provide the necessary visualization and analytical capabilities, allowing users to generate maps, conduct spatial analyses, and derive actionable insights.

This traditional GIS-based implementation of medallion architecture offers several advantages. It leverages established GIS tools and workflows, minimizing the need for extensive retraining or infrastructure changes. It is optimized for traditional environments yet still provides the flexibility to integrate with hybrid or cloud-based analytics platforms. Additionally, it enhances data accessibility and performance, ensuring that spatial datasets remain efficient and manageable for analysis and visualization.

By adopting this workflow, organizations can modernize their spatial data management practices while maintaining compatibility with familiar GIS tools, resulting in a seamless transition toward more structured and optimized data handling.

Example Workflow 2: Advanced Cloud-Native Workflow

For organizations managing large-scale spatial datasets and requiring high-performance processing in cloud environments, a cloud-native approach to medallion architecture provides scalability, efficiency, and advanced analytics capabilities. By leveraging distributed computing and modern storage solutions, this workflow enables seamless processing of vector and raster data while maintaining cost efficiency and performance.

This workflow is powered by cutting-edge cloud-native technologies that optimize storage, processing, and version control.

Object Storage solutions such as AWS S3, Google Cloud Storage, or Azure Blob Storage serve as the foundation for storing raw geospatial data, ensuring scalable and cost-effective data management. Apache Spark with Apache Sedona enables large-scale spatial data processing, leveraging distributed computing to handle complex spatial joins, transformations, and aggregations. Delta Lake provides structured data management, supporting versioning and ACID transactions to ensure data integrity throughout processing. RasterFrames or Rasterio facilitate raster data transformations, including operations like mosaicking, resampling, and reprojection, while optimizing data storage and retrieval.

Bronze – Raw Data Ingestion

The workflow begins by ingesting raw spatial data into object storage. This includes vector data such as GPS logs in CSV format and raster data like satellite imagery stored as GeoTIFFs. By leveraging cloud-based storage solutions, organizations can manage and access massive datasets without traditional on-premises limitations.

Silver – Data Processing and Transformation

At this stage, vector data undergoes large-scale processing using Spark with Sedona. Distributed spatial operations such as filtering, joins, and projections enable efficient refinement of large datasets. Meanwhile, raster data is transformed using RasterFrames or Rasterio, which facilitate operations like mosaicking, resampling, and metadata extraction. These tools ensure that raster datasets are optimized for both analytical workloads and visualization purposes.

Gold – Optimized Data for Analysis and Visualization

Once processed, vector data is stored in Delta Lake, where it benefits from structured storage, versioning, and enhanced querying capabilities. This ensures that analysts can access well-maintained datasets with full historical tracking. Optimized raster data is converted into Cloud-Optimized GeoTIFFs, allowing efficient cloud-based visualization and integration with GIS tools. These refined datasets can then be used in cloud analytics environments or GIS platforms for advanced spatial analysis and decision-making.

This cloud-native implementation of medallion architecture provides several advantages for large-scale spatial data workflows. It features high scalability, enabling efficient processing of vast datasets without the constraints of traditional infrastructure, parallelized data transformations, significantly reducing processing time through distributed computing frameworks, and cloud-native optimizations, ensuring seamless integration with advanced analytics platforms, storage solutions, and visualization tools.

By adopting this approach, organizations can harness the power of cloud computing to manage, analyze, and visualize geospatial data at an unprecedented scale, improving both efficiency and insight generation.

Comparing the Two Workflows

Aspect	Traditional Workflow (FME + PostGIS)	Advanced Workflow (Spark + Delta Lake)
Scalability	Suitable for small to medium workloads	Ideal for large-scale datasets
Technologies	FME, PostGIS, COGs, file system or object storage	Spark, Sedona, Delta Lake, RasterFrames, object storage
Processing Method	Sequential or batch processing	Parallel and distributed processing
Performance	Limited by local infrastructure or on-premise servers	Optimized for cloud-native and distributed environments
Use Cases	Small teams, traditional GIS setups, hybrid cloud setups	Large organizations, big data environments

Key Takeaways

The medallion architecture offers much needed flexibility and scalability for geospatial data processing. It meshes well with traditional workflows using FME and PostGIS, which is effective for organizations with established GIS infrastructure. Additionally, it can be used in cloud-native workflows using Apache Spark and Delta Lake to provide scalability for large-scale processing. Both of these workflows can be adapted depending on the organization’s technological maturity and requirements.

Conclusion

Medallion architecture provides a structured, scalable approach to geospatial data management, ensuring better data quality and streamlined processing. Whether using a traditional GIS-based workflow or an advanced cloud-native approach, this framework helps organizations refine raw spatial data into high-value insights. By assessing their infrastructure and data needs, teams can adopt the workflow that best aligns with their goals, optimizing efficiency and unlocking the full potential of their geospatial data.

Choosing Between an iPaaS and Building a Custom Data Pipeline

Posted on 2024-02-12 by Cercana

In today’s data-driven world, integrating various systems and managing data effectively is crucial for organizations to make informed decisions and remain responsive. Two popular approaches to data integration are using an Integration Platform as a Service (iPaaS) or building a custom data pipeline. Each approach has its advantages and challenges, and the best choice depends on your organization’s specific needs, resources, and strategic goals.

Understanding iPaaS

An iPaaS is a hosted platform that provides a suite of tools to connect various applications, data sources, and systems, both within the cloud and on-premises. It enables businesses to manage and automate data flows without the need for extensive coding, offering pre-built connectors, data transformation capabilities, and support for real-time integration.

For example, the image below shows an integration done in FME, an iPaaS that is commonly used in geospatial environments but has native support for common non-spatial platforms such as Salesforce. This integration creates a Jira ticket each time a new Salesforce opportunity object is created. It also posts notifications to Slack to ensure the new tickets are visible to assignees.

iPaas Salesforce-to-Jira pipeline in FME

This integration illustrates the typical visual nature of the iPaaS design approach, where flows and customizations are designed primarily through configurations, rather than through the development of custom code. This low-code approach is one of the primary value propositions of iPaaS solutions.

Advantages of iPaaS:

Speed and Agility: Quick setup and deployment of integrations with minimal coding required.
Scalability: Easily scales to accommodate growing data volumes and integration needs.
Reduced Maintenance: The iPaaS provider manages the infrastructure, ensuring high availability and security.

Challenges of iPaaS:

Limited Customization: While iPaaS solutions are flexible, there may be limitations to how much the integrations can be customized to meet unique business requirements.
Subscription Costs: Ongoing subscription fees can add up, especially as your integration needs grow.

Building a Custom Data Pipeline

Creating a custom data pipeline involves developing a bespoke solution tailored to your specific data integration and management needs. This approach allows for complete control over the data flow, including how data is collected, processed, transformed, and stored. This will typically be done using a mix of tools such as Python, serverless functions, and/or SQL.

Advantages of Custom Data Pipelines:

Complete Customization: Tailor every aspect of your data pipeline to fit your business’s unique needs.
Integration Depth: Address complex or unique integration scenarios that off-the-shelf solutions cannot.
Ownership and Control: Full ownership of your integration solution, allowing for adjustments and optimizations as needed.

Challenges of Custom Data Pipelines:

Higher Costs and Resources: Significant upfront investment in development, plus ongoing costs for maintenance, updates, and scaling. Proper cost modeling over a reasonable payback period can give a more accurate picture of costs. Many costs will be fixed and may dilute as your organization scales when compared to iPaaS consumption pricing.
Longer Time to Market: Development and testing of a custom solution can be time-consuming.
Expertise Required: Need for skilled developers with knowledge in integration patterns and technologies.

Making the Choice

When deciding between an iPaaS and building a custom data pipeline, consider the following factors:

Complexity of Integration Needs: For complex, highly specialized integration requirements, a custom pipeline might be necessary. For more standardized integrations, an iPaaS could suffice. For example, an ELT pipeline may lend itself more to an iPaaS since transformations will be performed after your data reaches its desitnation.
Resource Availability: Do you have the in-house expertise and resources to build and maintain a custom pipeline, or would leveraging an iPaaS allow your team to focus on core business activities? Opportunity cost related to custom development should be considered over the development period.
Cost Considerations: Evaluate the total cost of ownership (TCO) for both options, including upfront development costs for a custom solution versus ongoing subscription fees for an iPaaS. iPaaS tytpically has lower upfront onboarding costs than custom development, but long-term costs can rise as your organization scales.
Scalability and Flexibility: Consider your future needs and how each option would scale with your business. An iPaaS might offer quicker scaling, while a custom solution provides more control over scaling components.

Conclusion

Ultimately, the decision between an iPaaS and a custom data pipeline is not one-size-fits-all. It requires a strategic evaluation of your current and future integration needs, available resources, and business objectives. By carefully weighing these factors, you can choose the path that best supports your organization’s data integration and management goals, enabling seamless data flow and informed decision-making.

Contact us to learn more about our services and how we can help turn your data integration challenges into opportunities.

Using Hstore to Analyze OSM in PostgreSQL

Posted on 2023-12-05 by Cercana

OpenStreetMap (OSM) is a primary authoritative source of geographic information, offering a variety of community-validated feature types. However, efficiently querying and analyzing OSM poses unique challenges. PostgreSQL, with its hstore data type, can be a powerful tool in the data analyst’s arsenal.

Understanding hstore in PostgreSQL

Before getting into the specifics of OpenStreetMap, let’s understand the hstore data type. Hstore is a key-value store within PostgreSQL, allowing data to be stored in a schema-less fashion. This flexibility makes it ideal for handling semi-structured data like OpenStreetMap.

Setting Up Your Environment

To get started, you’ll need a PostgreSQL database with PostGIS extension, which adds support for geographic objects. You will also need to add support for the hstore type. Both PostGIS and hstore are installed as extensions. The SQL to install them is:

create extension postgis;
create extension hstore;

After setting up your database, import OpenStreetMap data using tools like osm2pgsql, ensuring to import the data with the hstore option enabled. This step is crucial as it allows the key-value pairs of OSM tags to be stored in an hstore column. Be sure to install osm2pgsql using the instructions for your platform.

The syntax for importing is as follows:

osm2pgsql -c -d my_database -U my_username -W -H my_host -P my_port --hstore my_downloaded.osm

Querying OpenStreetMap Data

With your data imported, you can now unleash the power of hstore. Here’s a basic example: Let’s say you want to find all the coffee shops in a specific area. The SQL query would look something like this:

SELECT name, tags
FROM planet_osm_point
where name is not null
and tags -> 'cuisine' = 'pizza'

This query demonstrates the power of using hstore to filter data based on specific key-value pairs (finding pizza shops in this case).

Advanced Analysis Techniques

While basic queries are useful, the real power of hstore comes with its ability to facilitate complex analyses. For example, you can aggregate data based on certain criteria, such as counting the number of amenities in a given area or categorizing roads based on their condition.

Here is an example that totals the sources for each type of cuisine available in Leonardtown, Maryland:

SELECT tags -> 'cuisine' AS amenity_type, COUNT(*) AS total
FROM planet_osm_point
WHERE tags ? 'cuisine'
AND ST_Within(ST_Transform(way, 4326), ST_MakeEnvelope(-76.66779675183034, 38.285044882153485, -76.62251613561185, 38.31911201477845, 4326))
GROUP BY tags -> 'cuisine'
ORDER BY total DESC;

The above query combines hstore analysis with a PostGIS function to limit the query to a specific area. The full range of PostGIS functions can be used to perform spatial analysis in combination with hstore queries. For instance, you could analyze the spatial distribution of certain amenities, like public toilets or bus stops, within a city. You can use PostGIS functions to calculate distances, create buffers, and perform spatial joins.

Performance Considerations

Working with large datasets like OpenStreetMap can be resource-intensive. Indexing your hstore column is crucial for performance. Creating GIN (Generalized Inverted Index) indexes on hstore columns can significantly speed up query times.

Challenges and Best Practices

While hstore is powerful, it also comes with challenges. The schema-less nature of hstore can lead to inconsistencies in data, especially if the source data is not standardized. It’s important to clean and preprocess your data before analysis. OSM tends to preserve local flavor in attribution, so a good knowledge of the geographic area you are analyzing will help you be more successful when using hstore with OSM.

Conclusion

The PostgreSQL hstore data type is a potent tool for analyzing OpenStreetMap data. Its flexibility in handling semi-structured data, combined with the spatial analysis capabilities of PostGIS, makes it an compelling resource for geospatial analysts. By understanding its strengths and limitations, you can harness the power of PostgreSQL and OpenStreetMap in your work.

Remember, the key to effective data analysis is not just about choosing the right tools but also understanding the data itself. With PostgreSQL and hstore, you are well-equipped to extract meaningful insights from OpenStreetMap data.

Do You Need a Data Pipeline?

Posted on 2023-05-17 by Cercana

Do you need a data pipeline? That depends on a few things. Does your organization see data as an input into its key decisions? Is data a product? Do you deal with large volumes of data or data from disparate sources? Depending on the answers to these and other questions, you may be looking at the need for a data pipeline. But what is a data pipeline and what are the considerations for implementing one, especially if your organization deals heavily with geospatial data? This post will examine those issues.

A data pipeline is a set of actions that extract, transform, and load data from one system to another. A data pipeline may be set up to run on a specific schedule (e.g., every night at midnight), or it might be event-driven, running in response to specific triggers or actions. Data pipelines are critical to data-driven organizations, as key information may need to be synthesized from various systems or sources. A data pipeline automates accepted processes, enabling data to be efficiently and reliably moved and transformed for analysis and decision-making.

A data pipeline can start small – maybe a set of shell or python scripts that run on a schedule – and it can be modified to grow along with your organization to the point where it may be driven my a full-fledged event-driven platform like AirFlow or FME (discussed later). It can be confusing, and there are a lot of commercial and open-source solutions available, so we’ll try to demystify data pipelines in this post.

Geospatial data presents unique challenges in data pipelines. Geospatial data are often large and complex, containing multiple dimensions of information (geometry, elevation, time, etc.). Processing and transforming this data can be computationally intensive and may require significant storage capacity. Managing this complexity efficiently is a major challenge. Data quality and accuracy is also a challenge. Geospatial data can come from a variety of sources (satellites, sensors, user inputs, etc.) and can be prone to errors, inconsistencies, or inaccuracies. Ensuring data quality – dealing with missing data, handling noise and outliers, verifying accuracy of coordinates – adds complexity to standard data management processes.

Standardization and interoperability challenges, while not unique to geospatial data, present additional challenges due to the nature of the data. There are many different formats, standards, and coordinate systems used in geospatial data (for example, reconciling coordinate systems between WGS84, Mercator, state plane, and various national grids). Transforming between these can be complex, due to issues such as datum transformation. Furthermore, metadata (data about the data) is crucial in geospatial datasets to understand the context, source, and reliability of the data, which adds another layer of complexity to the processing pipeline.

While these challenges make the design, implementation, and management of data pipelines for geospatial data a complex task, they can provide significant benefits to organizations that process large amounts of geospatial data:

Efficiency and automation: Data pipelines can automate the entire process of data extraction, transformation, and loading (ETL). Automation is particularly powerful in the transformation stage. “Transformation” is a deceptively simple term for a process that can contain many enrichment and standardization tasks. For example, as the coordinate system transformations described above are validated, they can be automated and included in the transformation stage to remove human error. Additionally, tools like Segment Anything can be called during this stage to turn imagery into actionable, analyst-ready information.
Data quality and consistency: The transformation phase includes steps to clean and validate data, helping to ensure data quality. This can include resolving inconsistencies, filling in missing values, normalizing data, and validating the format and accuracy of geospatial coordinates. By standardizing and automating these operations in a pipeline, an organization can ensure that the same operations are applied consistently to all data, improving overall data quality and reliability.
Data Integration: So far, we’ve talked a lot about the transformation phase, but the extract phase provides integration benefits. A data pipeline allows for the integration of diverse data sources, such as your CRM, ERP, or support ticketing system. It also enables extraction from a wide variety of formats (shapefile, GeoParquet, GeoJSON, GeoPackage, etc). This is crucial for organizations dealing with geospatial data, as it often comes from a variety of sources in different formats. Integration with data from business systems can provide insights into performance as relates to the use of geospatial data.
Staging analyst-ready data: With good execution, a data pipeline produces clean, consistent, integrated data that enables people to conduct advanced analysis, such as predictive modeling, machine learning, or complex geospatial statistical analysis. This can provide valuable insights and support data-driven decision making.

A data pipeline is first and foremost about automating accepted data acquisition and management processes for your organization, but it is ultimately a technical architecture that will be added to your portfolio. The technology ecosystem for such tools is vast, but we will discuss a few with which we have experience.

Apache Airflow: Developed by Airbnb and later donated to the Apache Foundation, Airflow is a platform to programmatically author, schedule, and monitor workflows. It uses directed acyclic graphs (DAGs) to manage workflow orchestration. It supports a wide range of integrations and is highly customizable, making it a popular choice for complex data pipelines. AirFlow is capable of being your entire data pipeline.
GDAL/OGR: The Geospatial Data Abstraction Library (GDAL) is an open-source, translator library for raster and vector geospatial data formats. It provides a unified API for over 200 types of geospatial data formats, allowing developers to write applications that are format-agnostic. GDAL supports various operations like format conversion, data extraction, reprojection, and mosaicking. It is used in GIS software like QGIS, ArcGIS, and PostGIS. As a library it can also be used in large data processing tasks and in AirFlow workflows. Its flexibility makes it a powerful component of a data pipeline, especially where support for geospatial data is required.
FME: FME is a data integration platform developed by Safe Software. It allows users to connect and transform data between over 450 different formats, including geospatial, tabular, and more. With its visual interface, users can create complex data transformation workflows without coding. FME’s capabilities include data validation, transformation, integration, and distribution. FME in the geospatial information market and is the most geospatially literate commercial product in the data integration segment. In addition it supports a wide range of non-spatial sources, including proprietary platforms such as Salesforce. FME has a wide range of components, making it possible for it to scale up to support enterprise-scale data pipelines.

In addition to the tools listed above, there is a fairly crowded market segment for hosted solutions, known as “integration platform as a service” or IPaaS. These platforms all generally have ready-made connectors for various sources and destinations, but spatial awareness tends to be limited, as does customization options for adding spatial. A good data pipeline is tightly coupled to the data governance procedures of your organization, so you’ll see greater benefits from technologies that allow you customize to your needs.

Back to the original question: Do you need a data pipeline? If data-driven decisions are key to your organization, and consistent data governance is necessary to have confidence in your decisions, then you may need a data pipeline. At Cercana, we have experience implementing data pipelines and data governance procedures for organizations large and small. Contact us today to learn more about how we can help you.