Benefits of Spatial Analytics for Decision Making

🌍🩺 Attention Healthcare Providers, Planners, and Policymakers! Discover how GIS mapping software can transform healthcare access in our latest article, "Enhancing Healthcare Access with Mapping and GIS." Are you tackling challenges related to healthcare access, resource allocation, or service coverage? Want to make data-driven decisions that improve health outcomes and reduce disparities? Here’s what you’ll uncover: 📍 Identifying Healthcare Deserts: Pinpoint regions lacking medical facilities and target interventions effectively. 🏥 Optimizing Facility Placement: Strategically position new healthcare facilities where they are needed most. 🚗 Analyzing Travel Times: Reduce geographic barriers by understanding travel times to hospitals and clinics. 🗺️ Assessing Service Areas: Visualize the reach of healthcare facilities and identify coverage gaps. 📊 Case Studies: Learn how Maptitude was used to improve rural healthcare access in Alabama and address disparities in emergency general surgery care nationwide. Maptitude equips you with powerful GIS tools to make healthcare more equitable, efficient, and responsive to community needs. 🔗 Don’t miss out—explore the full article and see how GIS can drive better healthcare access for everyone! #TechForGood #LocationIntelligence #DigitalHealth #SmartHealthcare #DataAnalytics #HealthDisparities #HealthcareInnovation #PublicHealth #RuralHealthcare #HealthPolicy #HealthcarePlanning #EmergencyCare #HealthOutcomes #GIS #MappingSoftware #HealthcareAccess #HealthTech #GeospatialAnalysis #HealthEquity #DataDrivenDecisions #Maptitude

Geospatial Without Maps [blog post] -- https://lnkd.in/gh8Q8eww <-- shared blog posts -- H/T Miguel Marques [I will be honest, I can’t remember the last time I made a map personally…] “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… [The] examples [at the link] 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…” #GIS #spatial #mapping #mapless #AllDataIsSpatial #usecase #appliedscience #appliedtechnology #automation #coding #machinelearning #AI #maintenance #utilities #GPS #infrastructure #datasupply #dataflow #spatialdata #spatialanalysis #spatiotemporal #farming #agriculture #precisionagriculture #yield #InternetOfThings #integration #smartinfracture #earthobservation #model #modeling #routing #urban #planning #spatiallogic #proximity #zonal #statistics #geostatistics #context #systemsintegration #workflows #processmodels #spatialintelligence #decisionsystems #businessrules #efficiency 

Real data is one of the most powerful tools we have in affordable housing. Esri details how cities are using spatial insights to connect planning with economic development, helping everyone involved see where investment can have the greatest impact. The data identifies patterns that financial models alone might miss, like access to jobs, transit, and community infrastructure. It also helps identify neighborhoods where investment can support long-term stability rather than displacement. And it creates opportunities to work alongside local leaders who know what their communities need. Better data leads to better decisions. And better decisions lead to housing and infrastructure that revitalize and empower communities.