Posts

Site Suitability Analysis Using a Network-Based Service Area Analysis

-
Image
  When selecting the best location for a new business, public service, or facility, it's not just about where a place is it's about where people can reach it. That’s where Network-Based Service Area Analysis becomes a game-changer in site suitability analysis. Unlike straight-line (as-the-crow-flies) buffers, network-based service area analysis accounts for real-world travel routes, such as roads, speed limits, and traffic patterns. This approach identifies areas that are truly accessible within a set travel distance or time say, all the neighborhoods within a 10-minute drive from a potential site. In a recent GIS project, I used this method to evaluate several candidate locations for a new coffee shop. By mapping 5-mile driving service areas for each site and overlaying population data, we could see which location had the greatest potential customer base. The Downtown location stood out, thanks to its central position and high surrounding population within easy driving rea...
Post a Comment
Read more

Mapping Heat Risk Index: A Tool for Climate Action

-
Image
 As global temperatures rise, urban areas are experiencing more frequent and intense heatwaves, posing serious risks to human health, infrastructure, and ecosystems. To address this growing challenge, Heat Risk Index (HRI) mapping has emerged as a vital tool for assessing and mitigating heat-related impacts. What is a Heat Risk Index? A Heat Risk Index combines multiple factors such as land surface temperature, vegetation cover, population density, and socio-economic vulnerability to identify areas most at risk during extreme heat events. By integrating GIS, remote sensing, and spatial analysis , HRI mapping provides a visual representation of heat exposure across different regions. Why is HRI Mapping Important? Identifies High-Risk Zones  : Helps policymakers and urban planners to pinpoint areas that needs heat mitigation strategies, such as tree planting, reflective surfaces, and cooling centers. Supports Public Health Initiatives  : Assists health agencies in...
Post a Comment
Read more
-
Image
  NDVI Mapping Using ArcGIS Pro Normalized Difference Vegetation Index (NDVI) is a powerful remote sensing tool used to assess vegetation health, monitor changes in land cover, and support environmental management decisions. NDVI uses satellite data to distinguish healthy vegetation from other surface types based on how they reflect light. ArcGIS Pro provides an excellent platform for performing NDVI analysis with its robust set of spatial tools and geospatial capabilities.   Steps to Map NDVI Using ArcGIS Pro          ·             First, we need multispectral satellite images, ideally from Landsat or Sentinel-2 satellites. The key bands for NDVI calculation are the Near-Infrared (NIR) and Red bands.          ·             Open ArcGIS Pro and load the multispectral data into projec...
Post a Comment
Read more

Identification of Solar Potential with the Raster Solar Radiation Tool

-
Image
  Identification of Solar Potential with the Raster Solar Radiation Tool in ArcGIS Pro As the demand for renewable energy grows, understanding solar potential at a localized level has become crucial. In urban planning, energy management, and sustainable infrastructure development, Geographic Information Systems (GIS) play a vital role in assessing solar energy potential. ArcGIS Pro offers a powerful tool the Raster Solar Radiation Tool which enables users to analyze the solar energy potential of rooftops and determine the optimal placement of solar panels.   What is the Raster Solar Radiation Tool? The Raster Solar Radiation Tool in ArcGIS Pro calculates the amount of solar radiation a surface receives over a given period. This tool considers multiple factors, including the angle of the sun, atmospheric conditions, terrain effects, and shading from nearby objects such as buildings and trees. By analyzing these parameters, the tool provides detailed insights into the best locat...
Post a Comment
Read more

Landslide Risk Potential Mapping after wildfires

-
Image
  Landslide Risk potential Mapping after wildfires Wildfires make the landscape more susceptible to landslides when rainstorms pass through an area after wildfires. Post-fire debris flows are particularly hazardous because they can occur with little warning, can exert great impulsive loads on objects in their paths, and can strip vegetation, block drainage ways, damage structures, and endanger human life. Often there is not enough time between a fire and a rainstorm to implement an effective emergency response plan. However, various post-fire debris-flow hazard assessment models have been developed to estimate the probability and volume of debris flows that may occur in response to a storm.   We can use of ArcGIS Pro to create a landslide risk map after wildfire. Through the use of Raster function chain in ArcGIS Pro we can derive a burn severity map, topographic slope map, and a land-cover index map. With the help of Weighted overly function can integrate the all these ma...
Post a Comment
Read more

Mapping Air Pollution Sampling Points with ArcGIS Pro

-
Image
  Mapping Air Pollution Sampling Points with ArcGIS Pro Air pollution is a critical environmental concern impacting public health and ecosystems. Mapping air pollution sampling points helps visualize pollution patterns, identify hotspots, and inform decision-making for mitigation strategies. ArcGIS Pro, a powerful GIS software, offers comprehensive tools for creating and analyzing air quality maps.
Post a Comment
Read more

Hot-spot analysis Using Getis-Ord Gi Statistic (ArcGIS Pro)

-
Image
  Hot-spot analysis of median income using census block-group-level data and Getis-Ord Gi* statistic  (ArcGIS Pro) The Getis-Ord hot spot analysis shows where high and low values are clustered. The tool compares the values of each feature with the neighboring features within a user specified distance. The values for each feature are then color coded to show high and low value clusters. The Gi statistic, also called Gi*, used both the location and the value in the pattern calculations. This is used to see the effect of the value field on the clustering over the user specified distance. The distance is determined by the characteristics of the input dataset. Features representing large, wide groupings may use a larger distance band value, while features representing a local region or smaller feature areas might use a small distance band value. With a larger value, expect to get a few large clusters. A smaller distance value may result in more numerous, smaller clusters.
Post a Comment
Read more