Tag Archives: spatial analysis
As retailers in 2017 continue to face headwinds, the competition is more challenging than ever. Customers are empowered by 24/7 access to the global marketplace. For many retailers, new store growth has slowed, so sales and margin growth are increasingly being driven more by incremental growth from existing stores. To continue to excel in this environment, every business should be mindful of three trends in retail big data:
- Sustainable growth – To grow sales in their existing stores, retailers must find innovative ways to reach customers and drive loyalty.
- Connected consumers – As consumers have become accustomed to growing transparency around the prices and quality of what they buy, retailers must find new ways to engage with and earn the loyalty of their customers.
- Explosion of big data – With the Internet of Things (IoT) maturing, retailers must leverage the vast amounts of useful data available within the network of devices and sensors that are connected online.
Retailers already have access to myriad data from sources like point of sale (POS), mobile devices, inventory management systems, and in-store sensors. As useful as this data is on its own, real insights happen when retailers can connect disparate data to see the conditions that bring success. And one powerful way to do this is by viewing data through the lens of location. Maps enable people to instantly spot and explore patterns and relationships in data.
Location is the common thread of data and enables decisions to be made about matters such as where to position existing merchandise and where to site new stores. Spatial analysis also allows retailers to more efficiently drive traffic through stores by effectively using the data typically only used in the online shopping environment. By tapping into insights derived from in-store sensors and customer mobile devices, retailers can make better decisions about where to allocate goods and employees in a strategic way that is targeted to consumer behavior. Forming a business strategy that leverages integrated location data helps retailers match the in-store customer experience with what consumers experience when shopping online. This is made possible by analyzing demographics, buying patterns, and customer movement in the context of space and time.
Spatial analysis is the key to understanding where, when, and why things happen. With this insight, retailers can engage existing and potential customers and spur in-store sales.
Jim Young, Esri Head of Business Development will be the guest at the Geoawesomness GeoChat on December 1, 2016. Young leads business development activities for Esri in Portland, Oregon and works closely with tech companies and developers to explore the use of location-aware application program interfaces (API) and spatial analytics. He analyzes data from phones, cameras, vehicles, and beacons to find patterns. Young seeks to apply spatial analysis along with computer vision to help retailers, advertisers, and tech companies gain market advantage.
Geoawesomeness is a blog about geospatial technology and all the exciting things surrounding it. With a team of people passionate about GIS from all around the world, Geoawesomeness aims to be not only the best geo-news platform, but also to provide constructive commentary about everything happening in the geo-industry.
GeoChat is a kind of town hall Q&A session hosted by Geoawesomeness, with guests representing the most cutting edge geo companies today.
In the US, drug overdose is the leading cause of accidental death, with opioid addiction driving the epidemic. Opioids include both legal drugs, used for pain relief, and illegal drugs like heroin. These drugs are highly addictive, and anyone from any walk of life can become dependent on them. In 2014, more than 29,000 opioid related deaths occurred in the US.
How Spatial Analysis Leads to Insight
Spatial analysis allows you to solve complex problems and better understand where and what is occurring in your world. It goes beyond mapping alone to let you study the characteristics of places and the relationships between them. If the spatial component is important to the problem, spatial analysis lends perspective to your decision-making.
Spatial Problem Solving
Have you ever looked at a map of crime in your city and tried to figure out what areas have high crime rates? Have you explored other types of information, like school locations, parks, and demographics to try to determine the best location to buy a new home? Whenever we look at a map, we inherently start turning that map into information by analyzing its contents—finding patterns, assessing trends, or making decisions. This process is called “spatial analysis,” and it’s what our eyes and minds do naturally whenever we look at a map.
Spatial analysis is the most intriguing and remarkable aspect of GIS. Using spatial analysis, you can combine information from many independent sources and derive new sets of information (results) by applying a sophisticated set of spatial operators. This comprehensive collection of spatial analysis tools extends your reach toward answers to your questions. Statistical analysis can determine if the patterns that you see are significant. You can analyze various layers to calculate the suitability of a place for a particular activity. By employing image analysis, you can detect change over time. These tools and many others, which are part of ArcGIS, enable you to address critically important questions and decisions that are beyond the scope of simple visual analysis. Here are some of the foundational spatial analyses and the ArcGIS tools that get them done.
Everybody gather around the map and make better decisions in real time.
You might think of spatial analysis as a process that can help you make sense of large amounts of current and/or historical information. And you’d be right about that. But spatial analysis works equally well in real time.
Imagine a metropolitan police department working to stay on top of everything that happens in a big city. Crimes, accidents, and traffic, along with all the mobile and stationary assets that they need to track come together to create an overwhelming task of real-time data collection and analysis.
But in law enforcement, time is of the essence. They need to get a handle on all of this data—and fast. They need to respond in the moment. The real-time data coming into their system needs to be understood and acted upon in real time. Continue reading
Asking questions and developing answers using a common vocabulary leads to better decision making.
As discussed in a previous post, spatial analysis can be viewed as a kind of common language used across an organization. It starts with a set of questions, such as Where are things located in the world?, What is nearby?, and How are things connected?, and then sets about answering those questions by leveraging the power of GIS.
Imagine a bank with a number of different branch locations, along with locations of all the customers they service in a specific geographic region. The bank can use spatial analysis to better balance its service to these customers based on drive time analysis and delineate geographic areas with similar capacity. Continue reading
Spatial analysis is built in to who we are, and is becoming a common language across organizations
You may not realize it, but you learned about spatial analysis at an early age—probably around the time you started walking. At around two years old, you started to become aware of where you were at any given moment. Soon after that, you started learning how to navigate—from room to room, from inside to outside, and learning how to get from home to school. And at some point, you developed the ability to recognize spatial patterns—a street changed from being safe to dangerous—neighborhoods had their own characteristics.
Spatial analysis is how we understand our world—mapping where things are, how they relate, what it all means, and what actions to take. That’s why whenever we look at maps, we inherently start turning them into instruments for making decisions. Continue reading
Last update: January 21, 2017 Spatial analysis has always been a hallmark of GIS, the “numerical recipes” which set GIS apart from other forms of computerized visualization and information management. With GIS we can pose questions and derive results using … Continue reading
Last Update: December 10, 2015 In early January of 2014, we heard quite a bit about the polar vortex (not a new term, by the way) as North America struggled with some of the most frigid and dangerous temperatures seen … Continue reading
In an earlier post, I had mentioned Esri’s involvement in the large National Science Foundation-funded project known as CyberGIS, which aims to establish a fundamentally new software framework via a seamless integration of cyberinfrastructure, GIS, and spatial analysis/modeling capabilities, particularly … Continue reading