PostGIS Expert for Mapping Platform Optimisation

@intelliresponse

With over a decade of experience, I have become a specialist in Python and PostgreSQL, skills that align with your specific needs for this mapping platform project. My expertise extends to large dataset management, as showcased through similar projects involving scraping from FlashScore and website automation for VFS global Visa Booking that demanded data extraction from high activity platforms. Inspired by these achievements, I keenly look forward to optimizing your 30-million plus address set. Rest assured, as already illustrated by my own recent projects, your developer and I can form an effective collaborative partnership for a successful outcome.

@devendrathakur12

i’ve done very similar recently, optimizing PostGIS for 20M+ point datasets with sub-second polygon queries. Are your points stored in geometry or geography type? Are queries mostly user-drawn polygons or repeated regions that can be cached? I suggest using GiST indexes with ST_Subdivide on large polygons because it reduces scan load and speeds queries. I also suggest precomputing tiles or hex bins (H3) because it gives near-instant counts for repeated queries. I will review schema and indexes, benchmark slow queries, and fix with proper indexing and query rewrites. Then I will add caching and batching strategies, validate accuracy vs ground truth, and tune for consistent sub-second responses. Best, Dev S.

@extreamcode

Hi there, I’ve read your PostGIS-driven mapping platform brief and I’m confident I can push accuracy toward 99%+ while slashing latency on 30M+ UK addresses. I’m Asad, a geospatial engineer who’s built scalable PostGIS pipelines, index tuning (GiST, SP-GiST), and Python services for polygon, radius, buffer, and boundary queries with GeoJSON or shapefile uploads. What I’ll do: optimize spatial queries with robust indexing, implement efficient batching for large cohorts, and validate accuracy across polygon, radius, and route buffers. I’ll align the Python (FastAPI) service with PostgreSQL + PostGIS best practices, ensure smooth GeoJSON/shapefile ingestion, and leverage caching where meaningful without sacrificing correctness. My approach includes: targeted GiST index tuning, query plan reviews, parallelization strategies, and rigorous unit/integration tests to guarantee consistency across query types. Initial plan: profile current queries, apply index refinements, implement deterministic precision checks, and deploy a near-real-time query path. I’ve shared an initial estimate based on your description, and once we go over a few technical details, I’ll confirm the exact cost and delivery schedule. To move forward, I’d love to confirm: 1) whether you track address-level accuracy by address or by unique coordinates, 2) the expected peak concurrent queries, and 3) preferred hosting/CI approach for deployment. What is the top priority between latency and 99% accuracy for the 3

@FaizalShaik80

Hello, I can deliver what you need. I have reviewed your project and noticed that it is very similar to a task I completed two months ago. I am an experienced and specialized freelancer with 6+ years of practical experience in Python, FastAPI, NoSQL Couch & Mongo, PostgreSQL and I’m able to complete and deliver this project promptly. Please visit my profile to check the latest work and honest client reviews. Connect in chat to discuss details and next steps. Talk soon.

@mobiwebsolutions

✋ Hi There!!! ✋ The Goal of the project:- TO OPTIMIZE A HIGH VOLUME POSTGIS BASED MAPPING PLATFORM FOR FAST AND ACCURATE SPATIAL QUERIES ACROSS 30M PLUS UK ADDRESS DATASET I have carefully reviewed your requirement and clearly understand you need a senior geospatial engineer to improve both performance and accuracy of complex PostGIS queries in a large scale mapping system. I am best fit for this project because I have strong experience in PostgreSQL PostGIS optimization and building high performance geospatial systems with large datasets. 1. Optimization of spatial queries for polygons, radius, buffers, and boundary based searches over 30M plus records 2. Implementation of advanced PostGIS indexing strategies such as GiST and query tuning for near real time response 3. Ensuring accurate results for GeoJSON and GIS uploads with Python FastAPI backend integration I will provide UI design, database management, testing, and full source code delivery with optimized and scalable architecture. I have 9+ years experience as a full stack developer. I have worked on similar geospatial and mapping systems involving large scale PostGIS datasets and performance tuning. Looking forward to chat with you for make a deal Best Regards Elisha Mariam!

@dmytroy7

Hi, What you're really dealing with isn't just slow query responses , it's the risk of user frustration and missed opportunities due to inaccurate data retrieval. Here's how I'd approach it , two solid paths forward: Option A , Spatial Query Optimisation: I would refine your spatial indexing strategy and implement advanced caching techniques to dramatically reduce response times while ensuring 99% accuracy across all query types. Option B , Scalability Enhancements: I could focus on restructuring the database schema and enhancing query efficiency specifically for polygons and distance-based queries, ensuring that performance scales with your 30M+ dataset. Between the two, I'd recommend Option A because it directly addresses both speed and accuracy, which are critical for user satisfaction. My approach starts with analyzing your existing architecture, so the final result fits exactly what you need , not just technically, but for the people actually using it. Best regards, Dmytro.

@MIndlabsAi6

Hello, I am Vishal Maharaj, a seasoned professional with 20 years of expertise in Python, PostgreSQL, FastAPI, and NoSQL Couch & Mongo. I have carefully reviewed the requirements for the Senior Geospatial Engineer role and am confident in my ability to optimize spatial queries for the mapping platform. To ensure near-instant response times and consistent accuracy, I plan to implement efficient spatial indexing using PostGIS/GiST, optimize queries for polygons, radius queries, buffers, boundaries, and GIS uploads, and leverage my experience in working with large geospatial datasets to refine the architecture. I am eager to collaborate with your team to enhance the platform's speed and accuracy. Please initiate a chat to discuss further. Cheers, Vishal Maharaj

@MrBimbola

As an experienced GIS Specialist with a deep understanding of geospatial queries and performance optimization, I would be an ideal addition to your remote team. With over 9 years of experience handling large datasets and utilizing tools such as ArcGIS, QGIS, and Google Earth Engine, I confidently assure you of achieving the speed and accuracy levels closer to the 99% mark you aspire to. In addition to my skills in GeoJSON, PostgreSQL, Python, I have profound expertise in working with PostgreSQL/PostGIS for spatial indexing and database management which is crucial for handling a dataset of your magnitude. To ensure the best results, I'd prioritize spatial data analysis, perform thorough geolocation leveraging my proficiency in GIS tools and python, while also implementing customized GIS solutions using Python, JavaScript and Google Earth Engine. My multi-faceted professionalism covering GIS, Web Development (including Geospatial Web Apps), as well as AutoCAD design for Urban Infrastructure Planning equips me to provide comprehensive solutions needed to optimize your existing mapping platform.

@techsty2014

Interesting project, I will optimize the 30M-point PostGIS workload with a GiST index on geometry, && bounding-box pre-filter before ST_Contains / ST_DWithin, and a geography column for distance work a combo that typically cuts large-polygon counts from seconds to sub-100ms. For the 90→99% accuracy gap, the usual cause is SRID mismatch between drawn polygons and stored points I will audit reprojection across the pipeline. Questions: 1) What SRID are points stored in, and what do drawn Google Maps polygons arrive as? 2) Is GiST indexing already in place, or is that part of the scope? Looking forward to discussing further. Best regards, Faizan

@mzdesmag

Hello sir, Did go through your job description and glad to share that I have enormous experience in working with PostGIS Expert for Mapping Platform Optimisation I'm a seasoned programmer and Engineer with quality experience in Flutter, React, Node.JS, SpringBoot, Frontend and Backend Development, Python, Matlab, R studio, C, C++, C#, OpenCV, OpenGL, Tesseract OCR, google vision, Statistical programming/R progamming data analysis Computing for Data Analysis Time Series & Econometric, Machine learning, AI, Deep learning, Matlab and Mathematica, 3D modeling, CAD/CAM,AutoCAD, 2D, Architectural Engineering, SolidWorks, Unity 3D, PCB, Electronics, Arduino, Automation, Embedded and Firmware , IOT, Electrical/Mechanical Engineering I am a TOP Rated Freelancer, and you can check my reviews here as well: https://www.freelancer.com/u/mzdesmag. Looking forward to potentially working together on this project. Thanks and Best regards, Adekunle.

@johnallenvillan1

Hello, I’ve reviewed your need to push a 30M‑point PostGIS dataset to near‑instant query speeds while tightening accuracy beyond the current 90%. I’ve delivered similar results on UK address‑scale datasets, including one project where I reduced polygon query latency from 1.8s to under 150ms using GiST/BRIN hybrid indexing and query plan restructuring. I see the real challenge here being less about raw indexing and more about avoiding expensive rechecks, ensuring correct geography/geometry usage, and preventing PostGIS from falling back to sequential scans on complex polygons or multipolygons. Handling buffers and route geometries at scale also requires strict control over SRIDs, caching and tiling strategy. I’ll profile your existing queries, restructure spatial indexes, and implement bounding‑box prefilters to ensure predictable performance. I’ll also refine your Python layer to reuse prepared statements and tuned connection pooling. Before starting, I need to confirm your current SRID strategy, whether geography is used anywhere, and the prototype’s hardware environment. I can deliver an optimised baseline quickly. Best regards, John allen.

@adisonw

Dear Sir, I am thrilled to bid your project. I have strong experience working with Python and PostGIS for large-scale geospatial systems, including spatial indexing, query optimization, and high-performance location-based analytics on datasets containing millions of points. I can support your team in refining the current architecture to improve both speed and accuracy for polygon, radius, buffer, and boundary-based queries over 30M+ UK address records. My approach focuses on leveraging PostGIS best practices such as GiST/SP-GiST indexing, optimized geometry types, bounding-box pre-filters, and query planning improvements to reduce scan overhead while maintaining spatial precision. I also have experience designing FastAPI-based geospatial services that efficiently handle GeoJSON uploads, route buffers, and real-time map interactions while maintaining low latency response times under heavy query loads. For accuracy improvements, I would audit current spatial assumptions (projection, geometry validity, edge-case handling) and introduce validation layers to ensure consistency across all query types. One key question to align optimization strategy correctly: is your current system primarily using point-in-polygon queries on indexed geometries, or are you also relying on custom pre-aggregation or tiling strategies for performance? Sincerely, Adison.

@rbtech1984

Hi There!!! ★★★★ ( PostGIS optimisation for 30M+ UK address geospatial performance tuning ) ★★★★ Project understanding: You need a senior geospatial engineer to optimise PostGIS queries over 30M+ UK addresses to achieve near-instant results and improve accuracy from ~90% to 99%. Focus is on polygons, buffers, radius queries and scalable spatial indexing. ⚜ PostGIS/GiST indexing optimisation ⚜ Large dataset query tuning (30M+ points) ⚜ Polygon, radius & buffer query handling ⚜ FastAPI + PostgreSQL architecture tuning ⚜ GeoJSON / GIS upload support ⚜ Performance benchmarking & accuracy fixes ⚜ Scalable geospatial architecture design I have strong experience with PostgreSQL, PostGIS and Python based geospatial systems, including query optimisation and handling large location datasets with performance tuning. Approach: I will analyse current query plans, optimise spatial indexes, partition large datasets if needed, refine geometry operations, and ensure efficient use of GiST/SP-GiST indexes for high-speed responses. Let’s connect and discuss current prototype setup, I can help improve both speed and accuracy step by step. Warm Regards, Farhin B.

@chiragpipal

⚠️ If you're not happy, you don’t pay. ⚠️ Hi there, Thank you for checking my proposal and sharing the detailed Geospatial Engineer project brief. I can build your high-performance mapping platform using Python (FastAPI) and PostGIS with a focus on fast, scalable, and highly accurate spatial queries. I will deliver: • Optimized spatial queries for 30M+ points • Near-instant response times • Best practices for spatial indexing (PostGIS / GiST) • Support for various query types like polygons, radius queries, buffers, and GIS uploads • Consistent accuracy across all query types You will also receive: • Documentation on spatial query optimization techniques I am confident I can execute your vision professionally and efficiently. Looking forward to discussing timeline and next steps. Best regards, Chirag.

@samiacartech

Hello, I've worked heavily with large-scale geospatial systems, combining Python, PostGIS and high‑volume spatial indexing to deliver fast and precise results. Handling multi‑million point datasets is part of my daily workflow, especially where accuracy and speed must coexist. For your mapping platform, I can help refine spatial indexing, restructure query logic and apply advanced PostGIS techniques so your 30M+ UK address dataset responds near‑instantly. From polygon ops to radius and buffer queries, I can optimise the geometry pipeline and ensure accuracy climbs from 90% to the high‑99% range. I’m confident I can support your developer and push the platform’s performance to where it needs to be. Best regards, Sami

@johnedwardl

Hi, I will optimize your spatial queries to ensure fast and accurate results for your mapping platform. With extensive experience in PostGIS and handling large geospatial datasets, I understand the intricacies of achieving near-instant response times while maintaining high accuracy. My approach will focus on implementing efficient spatial indexing strategies tailored for your dataset of over 30 million points, ensuring optimal performance with operations like polygons, radius queries, and buffers. I’ve successfully refined similar architectures, achieving response times that meet exacting standards. To further enhance the accuracy of your existing prototype, I’ll analyze your current query logic and employ best practices to push accuracy closer to 99%. Could you share insights on your current indexing strategy and any specific performance benchmarks you've observed? Let’s connect to discuss how I can help elevate your platform's capabilities. Thank you.

@Yevhen1987

Hi. Optimizing spatial queries for 30M+ points often hits a wall with slow response times and accuracy issues, especially when dealing with complex polygons or buffers. I bypass this cleanly by focusing on deep POSTGIS indexing strategies and query tuning. Instead of just running queries, I'd first audit your existing POSTGIS schema and indexes. We need proper GIST or BRIN indexes for those 30M+ points. I'll look at query plans for your polygon, radius, and buffer operations. PYTHON (FASTAPI) integration means the database layer needs to be ultra-efficient. For 99% accuracy, we confirm data integrity first. I can tackle this for £150 over 2-4 days. We could start with a quick initial performance audit to pinpoint bottlenecks. Message me if you want to proceed. Are you using a specific SRID for your UK addresses, or are they standard WGS84 lat/lng? Best regards, Yevhen.

@Kishoreindia

Dear Sir/Madam, I am an experienced Python Developer with strong expertise in building scalable backend systems, APIs, automation tools, and full-stack applications. I specialize in delivering clean, efficient, and production-ready solutions. I have successfully developed and deployed multiple live applications including healthcare platforms, legal service apps, school management systems, fintech apps, and real-time communication systems. My Core Python Expertise ✔ Django & Django REST Framework ✔ FastAPI (High-performance APIs) ✔ Flask ✔ SQLModel / SQLAlchemy ✔ PostgreSQL / MySQL / MongoDB ✔ Supabase Integration ✔ Authentication (JWT, OAuth) ✔ Payment Gateway Integration (PhonePe, Razorpay, Stripe) ✔ Web Scraping (BeautifulSoup, Selenium) ✔ Automation Scripts ✔ WebSocket & Real-time Systems ✔ Docker Deployment ✔ AWS / VPS Deployment ✔ REST API Design & Optimization What I Can Build For You Secure REST APIs SaaS backend architecture Admin dashboards Real-time chat systems Payment systems Data processing systems Microservices architecture AI/ML API integration Custom business logic systems Recent Project Experience Healthcare booking & wallet system Legal consultation backend platform School ERP & management API Fintech wallet & transaction management Real-time chat application (WebSocket + MQTT) Location-based services & geo APIs

@albertoprata

Hello, are your address points stored as geography or geometry, and how are you currently indexing them for polygon queries? I’ve worked on PostGIS systems handling 20M+ points with sub-second spatial queries. I’ve also fixed accuracy issues in polygon and radius searches at scale. I’d review your current query patterns, switch to the right data types, and tighten GiST indexing and bounding box filtering. Then refine polygon handling and edge cases to push accuracy closer to 99% without slowing things down. Thanks