Geospatial data is the geographical data having location data recorded with references to coordinates (latitude and longitude), address and postal code. Geospatial analytics services combine geographic data and business data to gain insights that support business decisions.

In today’s competitive market, to achieve growth and success businesses need realistic insights about their business. Companies need to analyse data from multiple sources, analyse data to get some meaningful information which will help to drive the business. Spatial Analytics is an interesting, complex process that gives very useful insights for business success. Sathyamedha’s “Spatial Analytics for Epidemiology and Health” services will gather, analyse, deliver clearly presented insights to businesses to ensure business success to the customers. It makes a clear path to competitive advantage, allowing customers to substantially enhance their services and profitability. Our service provides access to varying content repositories, presenting and delivery within the company or externally over the web.

Sathyamedha's spatial analytics team has experience in “Network Analysis”, “Geo-Visualization” aspects of spatial analytics. The team at Sathyamedha is providing best spatial data analytics services to ensure business results to the customers. Sathyamedha's spatial data analytics team will research, analyse, plan and execute the “Spatial Analytics for Medical Survey” to get better result to the clients’ business. Sathyamedha’s spatial analytics team’s capabilities range to process spatial data related to complete landscapes as well as individual pieces of information.

• Spatial ESRI – File processing
  • Different types of shapes
  • ESRI – File generation
• Data simulation for spatial model using various distributions
  • Normal
  • Binomial
  • Log uniformed
• Normalization of data
  • Row stochastic normalization
• Model prediction
  • Prediction of model data
  • Prediction of model parameters
    • EM (Estimate maximizations)
    • ML (Maximization of likelihood)
  • BLUP or BLUE (To be elevated)
  • MSE, R-Square and SD calculation prediction
  • Model parameter estimation and model missing data estimation with MSE, R2 and SD industry best values
  • Cluster and non-cluster processing
• Spatial contiguity
  • Different type of spatial contiguity
• Spatial lag
• Spatial heterogeneity
• Parallel processing of spatial model
• Models supported
  • SAR (Spatial Auto Regression)
    • Solving model
    • Conditional mean of equations(Y) – Mue
    • Co-variance structure of equation – Sign
    • Precision matrix of equation – Q
  • SDM (spatial Durbin Model)
    • Solving model
    • Conditional mean of equations(Y) – Mue
    • Co-variance structure of equation – Sign
    • Precision matrix of equation – Q
  • SEM (Spatial Error Model)
    • Solving model
    • Conditional mean of equations(Y) – Mue
    • Co-variance structure of equation – Sign
    • Precision matrix of equation – Q
  • SLM (Spatial Lag Model)
    • Solving model
    • Conditional mean of equations(Y) – Mue
    • Co-variance structure of equation – Sign
    • Precision matrix of equation – Q
• Visualization of estimation using scale corrected bar chart
• Different methods applied
  • Aggregation methods
  • Clustering methods
  • Smoothing methods
  • Interpolation methods
  • Spatial regression

• Delivery
  • Professional on-time delivery of projects
  • Efficient project management
  • Regular and transparent reporting
  • Single point of contact working with customer
  • Access to “Information reporting services” lead
  • Experienced to understand and address privacy, legal aspects of geospatial data processing
• Increase Revenue
  • Scientific team to quickly analyse and apply correct models to the business situation
  • Complex spatial models’ application is elegantly explained to the customers
  • Matured team to work along with customer in an agile way keeping customer informed at each stage
  • Increase in quality leads from local geography
  • Accurate visualization to support decision making – No unnecessary showcasing
  • Build “Spatial Analytics for Epidemiology and Health” features specifically for your business
  • Quality deliverable instills confidence and reduces questions
  • Support for varied sizes of businesses from part-time sellers up to enterprise clients
• Customer experience
  • Very flexible delivery
  • 24/7 support
  • On-boarding process support

• Spatial Analytics in GIS of Health care
  • Data exploration
  • Hypothesis testing
  • Modelling
• Applications Spatial Analysis in GIS:
  • Direct patient care
  • Epidemiologic research
  • Disease prevention
  • Intervention
  • Strategic planning
  • The spatial patterning of resources by race or socioeconomic status
  • Differential access to health care Analysis
  • Differential access to alcohol stores Analysis
  • Differential access to health promoting features analysis
  • Global cluster detection analysis
  • Local cluster detection analysis
  • Multivariate spatial regression analysis