location intelligence

Market Competition

You Can Understand Market Competition with Location Intelligence

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By Wenfei Xu, Data Scientist, CARTO

In today’s competitive market, established grocers are launching or acquiring discount brands to supplement revenue from other brand holdings.

For more than 10 years, discount chains have been disrupting the grocery industry encroaching on the market shares of established mainstream grocers.

According to the Boston Consulting Group (BCG), from 2000 to 2015, established grocers in Ireland lost one-fourth of their market share to discount grocery chains like Lidl.

The same is true in the UK where profit margins for established grocers shrank from 5% to less than 3% by 2017. Together, Aldi (also a discount grocery chain) and Lidl currently hold a 13% share of the grocery market which is only expected to grow while sales for the “big four” supermarket chains–Tesco, Sainsbury’s, Asda, and Morrisons–barely keep pace with rising food costs.

Zooming into Malaysia for instance, consumers have a preference for mini markets and this has affected the hypermarket segment.

Mydin Mohamed Holdings Bhd ED Datuk Dr Ameer Ali Mydin was quoted by The Malaysian Reserve last November saying that: “The overall business environment is tough. Net disposable income is low, prices are up as our currency is weak, so the overall hypermarket segment is down by 4% to date.”

He also added the popularity of online grocery shopping such as what is provided by Tesco, “further makes the case that “convenience” sells.”

Back to the UK grocery market, the map below gives a sense of just how saturated it is today:

market competition

In July 2018, reports circulated that Tesco, the British multinational grocery retailer, would be announcing the launch of a new discount chain in September with plans to open up to 60 stores that would compete directly with Aldi and Lidl.

While discount chains can increase brand revenue for established grocers in markets where discount grocers lack a strong presence, there are considerable obstacles involved in launching discount brands in mature markets. When it comes to site planning, the two most important factors for grocers looking to maximize total brand revenue with new discount brand store locations are:

  • How to penetrate market share of competitors like Aldi and Lidl?
  • How to avoid cannibalization with other Tesco stores?

So, the CARTO Data Science team decided to put themselves in the shoes of Tesco’s Site Planning team and find a solution that would:

  1. Locate potential store locations in the UK based on target customers and nearby competitors
  2. Build a spatial model to determine significant features impacting sales at each store
  3. Predict weekly total sales at new locations and calculate sales losses from cannibalization

Before getting started, however, they needed to examine the sales patterns across the UK and possible relationships between other characteristics such as the retail population, socioeconomic traits, and spending patterns. Note that CARTO is not using Tesco’s real sales data but rather estimates for the sake of the Site Planning analysis.

Understanding Current Sales

The first step is to understand the sales measure for the 848 Tesco store locations in the United Kingdom. Currently, these locations are categorized into the following four store types:

  1. Tesco (93 locations)
  2. Tesco Extra (610)
  3. Tesco Metro (95)
  4. Tesco Express (50)

Mapping each store location and colour coding by type of store produces the following map of Tesco’s current corporate footprint in the UK.

Since we are not working with Tesco’s internal sales data, Mastercard’s Retail Location Insights provides a proxy where average weekly sales performance of retail area serves as an estimate on store performance.

The graph below shows the distribution of weekly sales per Tesco store broken down by Tesco’s four store types, with Express stores having high frequency measure but low sales size, which as a convenience store, explains its unusual distribution patterns. Tesco Extras having the highest weekly sales but with a much lower sales frequency, which again makes sense as these store types offer products and goods in bulk sizes. Calculating average sales for each store type provides a measure for total weekly sales of £362 (approx. RM1,962) million.

market competition

What this total weekly sales estimate provides us with is a benchmark to determine whether the 60 new store locations helped maximize brand revenue overall.

Finding Potential Sites and Nearby Competition

Adding data on competitor locations, specifically Aldi and Lidl store locations, provides a view on market saturation. There are 731 Aldi and 723 Lidl store locations operating in the UK and what the map illustrates is that neither discount grocer has strong market share in more densely populated urban centers (after all, urban populations are not typically their target market).

With an understanding of market competition, adding Tesco store locations to the map will show trade area overlap between existing stores and retail areas serving as potential sites for new discount store locations. Cannibalization risks potential revenue loss at both existing store and potential new store if the sites are too close to one another as the image below displays.

market competition

This market overview shows some promising areas with a low chance of self-cannibalization and competitors far enough away so as to not pull trade area customers away from potential store locations in catchment area. However, as the ultimate goal is to predict sales at new store locations, the next step is to determine the significant features most likely to increase sales at each site.

Powering Site Planning with Regression Models

After locating potential sites for Tesco’s planned discount chain, CARTO needs to build a model to infer the relationship between sales and other features that may impact sales. This would allow them to make sales predictions for each of the new potential sites and find the ones that have higher predicted sales.

In particular, they are using a form of local regression modeling called Geographically Weighted Regression (GWR). Since the factors that affect consumer spending may change across geographic areas, they use a modeling technique that ascribes explanatory power only to those features close enough to a store to impact it – that is, models within a certain estimated bandwidth. In the model, the estimated bandwidth is 4479 meters (or 2.7 miles), as shown below. Furthermore, GWR assumes that features closer to a store affect sales to greater extent than more distance features.

market competition

To build the model, CARTO used datasets from the UK census, third-party POI providers, and Mastercard Retail Location Insights. The correlation matrix below begins to show the relationship between the features and sales numbers and provides insight into how the model will behave.

market competition

CARTO discovered that sales is positively correlated with features like sales indices and distance to another and negatively correlated with census-defined higher socio-economic classifications (NS-SEC), proving that new location data streams are more important for these analyses than traditional out-of-date data sets.

Understanding Feature Significances

GWR gives an estimate of feature importances at each point, but as certain features tend to be universally important across the UK, CARTO provided average t-statistics across all the points to give a sense of feature significances. Here they used t-statistics with an average magnitude of 1.96 (or a 95% confidence interval) to test for feature significance.

market competition

In addition to store type and band size, the model found the following 4 features of importance as well as the magnitude of each feature coefficient:

  1. Distance to another store, which equates to £336 per meter on average
  2. Mastercard sales score, which equates to £567 per point on average
  3. POI density, which equates to £146 million per (store/meter-squared) on average
  4. Socio-economic class, which equates to £3,083 per person on average

Because CARTO had a tiny regression model at each point, one can attribute a set of coefficients for each. This creates a coefficient surface that allows one to understand how the magnitude of certain features change across space. Below are maps that show the four listed coefficient “surfaces.”

market competition

By mapping these coefficient surfaces, they discovered:

  1. Higher distance (when a competitor store is further away) measures tend to have a bigger impact in the Northwest
  2. POI merchant density has a bigger positive impact Southwest
  3. Higher socioeconomic class actually has a bigger positive impact in the Midlands and Southwest

These insights provide the final details needed to (1) identify the top 60 store locations, and (2) determine whether sales performance at these sites led to an overall increase in Tesco’s revenue.

Predicting Sales Performance

Since Tesco is rumoured to open 60 new store locations, and since CARTO’s goal is to determine which sites are most likely to maximize overall revenue, they mapped the 60 sites falling along the higher end of the predicted sales distribution.

Among the top 60 locations sales averages amount to £1.4 (approx. RM75) million per week, these sites tend to be located along the periphery of urban centers, and they also maintain distance from current Tesco as well as competitor store locations. In the map below, you also see that the top 60 sites appear in areas with high Mastercard sales scores:

With the coefficient score for “distance to other stores”, CARTO was able to run a cannibalization analysis measuring the impact of spatial proximity that other store locations has on potential space sales performance.

market competition

This map of lost sales distribution across the UK shows that distance has a smaller impact around London where there is a higher population density. Now with the CARTO model, one can calculate sales lost due to cannibalization per meter.

For instance, the image below displays two existing Tesco Extra stores located 1,300 meters apart. However, one of the prime sites for a new discount store location is approximately 800 meters from the first Tesco Extra store, and would likely cause loss of sales due to cannibalization. One can determine whether or not this new store location will help maximize overall revenue by subtracting the new cannibalization distance of 800 meters from the old cannibalization distance of 1,300 meters and then multiplying the remainder (500 meters) by the per meter sales loss of £298 (approx. RM1,615). After subtracting this figure from the new store’s predicted sales we are left with total sales from existing store and new store of about £2,741,000 (approx. RM14,850,000).

market competition

From here the CARTO team calculated the total amount loss from cannibalization for the top 60 store locations and then calculated the total amount of sales gained from each site. The results saw the original weekly sales average increase from £362 (approx. RM1,962) million to £392 (approx. RM2,124)million for an overall revenue increase of 8.5%. 

Location Intelligence for Site Planning

It remains to be seen when and where Tesco will open its new discount store locations, but Location Intelligence can help reduce the guesswork and risks associated with site planning.

Want to learn more about working with new data streams to modernize your site planning? Email us at asklabs@lavalabs.net or call us at 03-7885 9720

 

Article first appeared on the CARTO Blog.

Lava Labs brings together innovation and technology, combined with expertise and deep understanding of businesses and their needs by engaging with industry leaders to empower organizations. We specialise in building custom web and mobile applications in Malaysia and around the APAC region.

Commercial Impact of Amazon's new HQ

Measuring the Commercial Impact of Amazon’s New HQ

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By Dave Bryson, Senior Member – Solutions Team, CARTO

We don’t yet know where it will end up, but Amazon’s second headquarters in North America, Amazon HQ2, is going to vastly change the physical, societal, and economic landscape of the chosen site.

For the candidate cities, Amazon itself, and other local economic interests, the commercial impact of this second headquarters will be significant. But what insights from Amazon’s impact on the city of Seattle, Washington can municipal leaders and other local officials consider to anticipate changes if their city is selected by the e-commerce giant?

Using Location Intelligence and multiple data streams, one can gain significant insights on the impact of development at a hyper-local level.

The Commercial Impact of Amazon’s HQ in Seattle

For 10 years following their founding in 1994, Amazon primarily stuck to their initial business model as an online retailer of books. The company grew modestly through the late nineties, surviving the dot com bust of the late nineties and early 2000’s.

Amazon, as we know it today, was formed by major company shifts occurring in 1998 and 2005. In 1998, Amazon announced its shift away from only selling books. And in 2005, Amazon launched Amazon Prime, its premium service guaranteeing 2-day shipping for member orders.

In the map below, you can see that prior to 2005, development around the Amazon HQ in Seattle was quite modest. Only 48 building permits were applied for, and the type of construction was mostly uncategorised and scattered around the neighbourhoods surrounding Amazon.

After the announcement of Amazon Prime in 2005, and the launch of Amazon Elastic Cloud Service (now known as Amazon Web Services) in 2006, the growth in development skyrocketed.

From 2005 till 2011, over 2,000 permits were applied for, and construction of commercial properties (retail and office space) grew dramatically. The green area in the map shows a 15-minute walk time around the Amazon HQ, and you can see that commercial development close to Amazon HQ is highly concentrated.

Also, multi-family residential (condos and apartments) is built at a much higher rate than single family homes in this area, perhaps showing that the residential development trends tend to lean more towards multi-family development in the wake of major headquarters growth.

Finally, after 2011, Amazon announces Amazon Video and Fire Tablets, and becomes the company we know today — producing content, devices, and selling all things retail. Their development in the time frame of 2011 to 2018 is consistent with the previous time period, showing that within the 15-minute walk time of Amazon HQ, development trends are concentrated in the commercial and multi-family home segments.

 

Predicting the Commercial Impact of Amazon’s Second HQ

In 2017, Amazon announced the search to find their second headquarters. After much speculation, we still don’t know exactly where they will go, but we have a good set of highly likely candidate/ possible cities. Within these cities, specific sites have been identified as potential locations for HQ2. Based on CARTO’s findings from Seattle, one can try to understand the commercial impacts of an Amazon HQ at candidate sites.

One of the top contenders in the search for HQ2 is Washington D.C. and the surrounding suburban area, with a rumoured candidate site in Bethesda, Maryland, near the National Institute of Health. This site meets many of the criteria set forth by Amazon (near public transit, access to highly educated populous, etc…), but how will current residents and development patterns in this area be impacted? Based on what we know from the transformation in Seattle, what can we expect in Bethesda?

In the map below, the area defined around the proposed site represents a 15-minute walk time. Within this area, one can explore various metrics to understand the area in which Amazon may build, and the subsequent boom in development one can expect based on the Seattle observations.

The map shows the development patterns, average housing price (from CARTO’s Data Observatory via Zillow) and a measure of social vulnerability provided by the CDC (Centre for Disease Control and Prevention).

The use of the CDC dataset is key. Typically this dataset is used in emergency management to identify ‘at risk’ populations in the event of a disaster. For example, if a hurricane comes ashore and flooding is likely, where are the areas in which evacuation of the population will be challenging, and the subsequent destruction of properties will have a more adverse effect?

But, this dataset provides some interesting calculations, based on multiple variables from census data, to calculate risk scores associated with Housing, Transportation, and socio-economic status.

So how does this relate to Amazon’s HQ and the building of their new campus?

Power your site selection decisions with Location Intelligence!

Want to know how you can determine the best location to open your next store/ office, e-mail us at asklabs@lavalabs.net or call us at 03-7885 9720.

As we saw in Seattle, we know that wherever the second headquarters is placed, we can expect a spike in commercial development (retail and office space), and in multi-family condos and apartments.

This spike in real estate development, and the rise in prices for housing and leased office space, will have an impact on the existing community, especially those who are in the ‘higher risk’ categories for housing and transportation.

If development of condos and apartments occurs in an area populated by those who depend on disability or other forms of public assistance to afford housing, increased housing costs will have a greater impact, possibly taking the form of displacement and gentrification.

Also, rising lease rates for office and retail space will impact local businesses currently operating in the area.

The map above shows census block groups coloured from light to dark red based on the aforementioned risk score. As you can see, the scores are fairly low in the immediate area of the prospective site. However, within the 15-minute walk area, where many employees may choose to live, the risk scores increase, especially to the north of the proposed site. This means that those areas in particular could face adverse impacts from the development of HQ2 in Bethesda.

For local governments, this kind of assessment can inform policy decisions, mitigate negative impacts, and prevent problems like displacement and sharp housing cost increases.

Local businesses can use this information to make strategic decisions around marketing to new residents. And Amazon themselves can get a sense of their own impact and work with local communities to address challenges on a neighbourhood-by-neighbourhood, street-by-street level.

 

Making Informed Site Planning Decisions

While Amazon’s decision remains uncertain, what is certain is that new location data streams are modernising the site planning process. Whether it’s retail performance insights from MasterCard, or traffic and mobility figures from GPS data, site planners can answer site-specific questions beyond, “where do I open my next store,” with confidence.

Article first appeared on the CARTO Blog.

Lava Labs brings together innovation and technology, combined with expertise and deep understanding of businesses and their needs by engaging with industry leaders to empower organizations. We specialise in building custom web and mobile applications in Malaysia and around the APAC region.

supply chain network design

What is Supply Chain Network Design and How Does It Work?

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By Matt Forrest, Director of Business Architecture, Carto

 

Supply chains are a critical, and often unnoticed, part of our everyday lives. Almost everything that we purchase in a store comes to us as a part of a supply chain and managing these networks is a complex and ever evolving task.

Location intelligence plays a critical role in supply chain network design.

From finding better ways to serve stores, the optimal location for a new distribution centre, or understanding how goods are reaching their final destination, every aspect of supply chain design is tied to location data.

In this post, you will learn about the impact and importance of a well-designed and optimized supply chain for any business.

Supply Chain Network Design and Location Intelligence

Supply chain network design is the process of building and modeling a supply chain to better understand the costs and time associated with bringing goods to market with the resources and locations available.

Some questions that are commonly evaluated as a part of this process are:

  • How do I design my supply chain network to deliver the required service at the lowest possible cost?
  • Given a fixed network, how do I determine optimal product sourcing and inventory deployment rules to meet anticipated customer demand?
  • Given a logistics network and a defined distribution strategy, how can I best use my available transportation resources?

The end goal is to create the most efficient network possible, meet the demand of customers, and ensure the lowest possible cost to serve your network.

This process includes many different variables and models but many of them are tied to location, such as your distribution centres, store network, and possible routes to serve those stores.

Other assumptions, such as number of transportation resources, assumed delivery time, and total route time are also tied to location even though they might not initially appear to be impacted by location. The exact routes and road networks play a major role in how you will ultimately design your routes and assign resources to different clusters of stores.

Evaluating Publix’s Supply Chain Network

To show how location intelligence can impact supply chain network design, the team at CARTO analysed the supply chain network for Publix, a leading grocery store chain in the southeastern United States, with primary operations in Florida.

Publix has over 1,110 stores served by 8 distributions centres.

The company expanded into North Carolina in 2011 and Virginia in 2016 and will likely continue to open new stores in these states.

However, the company has not opened a new distribution centre to serve these new locations.

CARTO evaluated Publix’s store and distribution centre locations to understand how they are currently serving their stores, where they should place a new distribution centre, and to quantify the ROI of opening a new distribution centre. To do this, CARTO used the following data sources and analyses:

  • Publix store and distribution centre locations
  • Demographic data from the Data Observatory to see the population served by each store
  • Spatial clustering analysis to create logical clusters of stores for each route
  • Optimized routing, or the most efficient route from a start/stop location to a set of other locations, to see time and distance of each route

To understand the current supply chain network design, CARTO:

  1. Created logical clusters of stores using the clustering analysis in CARTO to find logical groups of stores.
  2. Next, these groups were assigned to the nearest distribution centre, and some minor adjustments were made for outliers that needed to be assigned to different distribution centres.
  3. Used the same clustering analysis to create logical clusters for each of the routes originating from the distribution centre.
  4. Used the optimized routing to find the most efficient route from the distribution centre, to each of the stores, then back to the distribution centre.

This analysis gives the length of the trip and the drive time for the entire route. After looking at the data, some routes needed to be modified to make sure they could be completed within a standard shift, or if that was not possible, that route would need to be split into two shifts.

Assuming that a drop off takes 30 minutes, CARTO then optimized the routes even further and split some of the routes into smaller routes to ensure there were as many routes as possible that could be completed in one shift.

In the resulting map, you can see that to use each route once, it would take:

  • 41,554 miles
  • 1,134 hours in driving and delivery time
  • 101 total routes

You can see that the Florida routes are shorter and well optimized apart from a few routes, but the Dacula, GA (purple circles) and McCalla, AL (light green circles) distribution centre routes are very long and serve a significant amount of stores.

Dacula: 37% of Total Distance, 26% of Total Time, 24 Routes
McCalla: 22% of Total Distance, 14% of Total Time, 12 Routes

Using CARTO, one can see some obvious improvements such as McCalla 5 (the green route to Chattanooga), which is better served by the Dacula 6 route (the purple route to Knoxville).

Using the final visualization, CARTO saw the need to add a distribution centre to better serve the stores furthest away from the Dacula, GA distribution centre since it serves almost 2 times more stores than any other distribution centre.

Adding a New Distribution Centre

After looking at the stores that the Dacula distribution centre serves, Publix would want to place a new distribution centre somewhere in North Carolina to serve these stores and to allow for more expansion in North Carolina, South Carolina, and Virginia.

To select this location, CARTO looked at data from the Data Observatory, specifically the employment, total population, and road data to find a large city near major highways with connections around the region.

CARTO narrowed this down to three candidates:

  • Raleigh
  • Charlotte
  • Greensboro
supply chain network design

 

All cities are suitable based on the population and employable population. However, after looking at the proximity to the current store footprint and proximity to major roads, it is clear that Charlotte is the best suited location for the new Distribution Centre.

Outcomes of the Network Optimisation

The process for analysing new routes is almost identical to the process above: create logical clusters, analyse the routes, review and refine the routes.

After running this same analysis with the new store clusters and distribution centre (resulting map here and below), the team at CARTO noticed some significant optimsations in the supply chain network from the original map at the top of the post:

  • 15.7% decrease in distance driven
  • 6.4% decrease in overall delivery time
  • 9 fewer routes
supply chain network design

 

This all accounts for estimated annual savings of $950,000 to $1.1M savings in fuel and time costs.

There are other additional benefits which are not accounted for, such as increased ability to serve stores and deliver fresh foods.

Overall it is clear that adding a new distribution centre would not only save costs in the near term, but would also enable Publix to add somewhere around 80 additional stores in the North Carolina, South Carolina, and Virginia markets since the new Charlotte distribution centre is currently only serving 81 locations.

Why CARTO for Supply Chain Network Design?

You can conduct supply chain network design with any location intelligence tool, but we’re a little biased here.

As you can see in the examples above, CARTO has several key advantages that allows it to run complex Supply Chain Network Optimization analyses like the Publix use case:

  • Bring your data to the platform and analyse it with ease
  • Integrate with other data such as demographics and advanced optimised routing
  • Visualise the optimised network and routes on a map, not just the data

CARTO brings together the data, analysis, and visualisation to make complex analyses like supply chain network design and optimization possible and allows you to not only understand, but visualize and act on the insights derived from your analysis.

Have a supply chain you’re designing? Request a demo and we’ll walk you through the same process with your own data.

Article first appeared on the Carto blog.

 

Lava Labs brings together innovation and technology, combined with expertise and deep understanding of businesses and their needs by engaging with industry leaders to empower organizations. We specialise in building custom web and mobile applications in Malaysia and around the APAC region.

flood risk

How Insurance Can Use Location Data to Determine Flood Risk Areas

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By Steve Isaac, Content Marketing Producer, Carto 

Insurance providers must move quickly to take advantage of location data available to them. Confronted with a world with more extreme weather than ever before, Location Intelligence is helping insurers prepare their organisations and policyholders for the inevitable increase in natural disasters and extreme weather events.

According to Gartner, only 30% of insurance industry organisations are utilizing Geospatial and Location Intelligence.

Embracing Location Intelligence can help insurers to reduce their exposure to risk, while preparing and offering policyholders better service and more varied plans that meet their needs.

Staying Up to Date with Real-Time Mapping

During a disaster event, it is imperative for insurance companies to be as informed as possible.

By viewing data and risk exposure in real-time, a company is best equipped to adapt and quickly address the needs of the policyholders that are most adversely affected.

The map below shows a projected hurricane track off the coast of Florida, paired with data of policyholders. The map shows which policyholders are at risk of hurricane damage, and the amount of that damage.

Tracking a storm or event and its impact on your policyholders can also assist in resource and personnel distribution to an impacted area, making sure that you have the right number of claims adjusters on the ground to assess damages.

The savvy insurance agency can also use spatial analytics to determine outliers in claims and investigate fraudulent cases, helping insurers focus efforts on customers that actually require assistance.

Helping Consumers to Understand and Mitigate New Risks with Diverse Policy Options

Diversifying policy options is imperative to spreading out risk exposure while protecting policyholders who may not even recognise new challenges and dangers.

Estimates show that 60% of homes affected by Hurricane Irma and 80% of homes affected by Hurricane Harvey were uninsured against the damages. Similarly, in Malaysia, many residents whose homes were damaged from the flood that hit Penang in Nov last year said their homes were uninsured. Some were therefore unable to cope with the financial impact of the loss, The Straits Times reported.

Since flood insurance is not traditionally included in a standard homeowner’s insurance policy, insurers should be proactive in informing their policyholders of their risk regardless of where they fall on a floodplain map or other risk assessment.

A report from ClimateWise, a coalition of insurance industry organisations, has identified a huge jump in “the protection gap”, or the difference between the total costs of natural disasters and the amount insured against the damage, which has quadrupled since the 1980’s to $100 billion/year.

With cheaper or more diverse policy options for those medium- to low-risk customers (that are newly exposed to risk due to global climate change), insurers can protect more property owners and spread out the risk they hold.

Update Outdated Maps and Conceptions with Varied Data Sources.

To get a full picture of the inherent risk associated with using outdated resources, we can look at the example of United States’ Federal Emergency Management Agency’s (FEMA) 100-year Floodplain map.

Federal law dictates that flood insurance is required for properties that fall within a high flood risk zone, although the determination of what constitutes high risk is based of the FEMA’s 100-year floodplain map, which can be a bit problematic.

First, the 100 year floodplain map is out of date and new flood mapping is expensive and challenging.

FEMA is making constant updates to their maps, but over 20,000 communities across the United States fall under their purview as possible flood risks.

FEMA is forced to prioritise updates based on cost benefit as well as the recency of their last update, which can leave cities to base critical disaster relief plans based on old data. Often, even when newly updated, these maps can lack information on local development, which can have substantial impact on flood risk, and almost all of them ignore the impact of global climate change.

Second, the 100-year floodplain map is misunderstood by the general public.

Just based on the name, even an informed consumer may think that a property falling within the 100-year floodplain is likely to be flooded once every hundred years, but in truth the map is identifying areas that have at least a 1% chance of flooding each year.

Houston, for example, has had what FEMA may consider a hundred year flood in each of the last three years.

You can also see in the image below from the New York Times, a high percent of the damage caused by flooding is coming from well outside both the 100, and even 500 year floodplain map risk zones.

Hurricane Harvey damageSource: New York Times

Due to the inaccuracies that may be inherent in many traditional risk assessment tools, insurers should also be drawing on past claims data and open data sources to amend their understanding and properly assess flood risk. By visualising more complete data, insurers can help make sure that their customers are appropriately covered.

RelatedA Civic Mapping Project to Determine Neighbourhood Needs

Use Location Intelligence to Provide Fair, Accurate, and Bias-Free Policy Pricing to Consumers.

There may be nothing more important to an insurance company’s long term health than having properly priced policies.

If an underwriter improperly assesses the risks associated with a particular property, the pricing for that policy will be off. If that pricing is too aggressive (too low), an insurance company is opening themselves up to greater risk and the chance that a high number of claims would cut into earnings.

If policy pricing is too conservative, a company risks getting undercut and priced out of the market.

Underwriters develop expertise in understanding and assessing risks, and base their assessments and policy pricing on highly varied datasets.

By making sure that your underwriters are equipped with the most up to date tools and techniques, you can reduce overall exposure to risk and prevent non-data driven bias from entering the process.

Contemporary Location Intelligence tools can synthesize multiple data sets and using spatial analysis, provide the modern underwriter with deeper and more dynamic insights.

Beyond Floods

For example, a tool like Syndeste’s BeyondFloods can act as a treasure trove for the underwriter looking to accurately assess a property’s flood risk using something more powerful than an outdated floodplain map.

In addition to providing a granular view of relevant and varied data points, from public and private datasets, BeyondFloods can create a comprehensive index or in their case, ‘Flood Score’, as mapped above, which can help underwriters to pinpoint the sweet spot for policy pricing.

Crowdsource Data and Build Community to Mutually Benefit Insurers and Policyholders.

Building a strong and interconnected community may seem challenging and expensive in the insurance world, but with benefits to both insurance companies and policyholders, it should be a priority.

With smartphones in hand, policyholders can become a real-time and insightful data source. Using a crowdsourcing platform, like Fulcrum for example, to gather real-time data can improve claims management and ease the burden on adjusters.

Your policyholders can also provide real-time insights during a major event, showing rising water levels, the spread of a wildfire, or the impact of a tornado.

In the example below, individuals collected data about shelter’s statuses immediately following Hurricane Harvey in Houston in a Google Spreadsheet, and mapped that data to show where shelters were and weren’t accepting evacuees.

While independently anecdotal, the full knowledge of an interconnected community can paint a clearer picture of risk exposure and allow more direct assistance from the insurer.

Crowdsourcing

Adopting new technologies and methodologies around Location Intelligence is imperative for an insurance company that is looking to better connect with and protect their policyholders, while maintaining their competitive advantage using fair and data-driven policy prices.

Article first appeared on the Carto Blog.

 

Lava is an authorised Partner of Carto in Malaysia and with more than a decade of experience in the industry, we’re proud to say we’re one of the leading cloud consultants and service providers in the Asia Pacific region.

real estate investment opportunities

How to Identify Real Estate Investment Opportunities before Others

600 407 Admin

 By Steve IsaacContent Marketing Producer, Carto 

When evaluating new real estate investment opportunities to grow your portfolio or doing your due diligence on an existing investment, the importance of doing your homework cannot be overstated.

You want to be one of the first firms through the door in an up-and-coming neighbourhood, but how do you find that new neighborhood in the first place?

Real estate investment groups are turning to location intelligence to answer challenging questions like:

  • How can I identify new areas and neighbourhood hotspots for potential investment?
  • How can I better identify and understand risk when making a new investment?
  • How can I use publicly available data to better inform my real estate investment strategy?

More than many industries, real estate relies heavily on location-based data, not just because any property that you may invest in can be pinpointed on a map, but because the circumstances in the world around a property inform it’s value.

Real estate value is intrinsically tied to broader social and economic conditions, so the more detailed your understanding of a location, the greater your insight will be into its potential.

By enhancing your understanding with commercial, demographic, and transit data, you give yourself the best chance of identifying a real estate investment opportunity early and ensuring the investment decisions you are making provide you the most value.

Here are 4 ways location data can strengthen your real estate investment strategy.

Visualise Local Public and Alternative Transit Options

Understanding how easily accessible a prospective property is or will be in the future is critical to gauging its value.

  • Do people have access to the property you are looking at?
  • What type of public transportation options are within a five-minute walking distance of your property?
  • Are there plans for a new line nearby, or is there a major shutdown planned that may impact the commute of prospective renters?
  • Are bike-sharing services and other alternative options available nearby?
real estate investment opportunities

 

The above map shows subway stops, as well as visualisations of all areas that are within a five minute walk. Being able to visualise this data can give you better insight into a property’s accessibility and desirability.

Use Taxi Pick-up/Drop-off Data to Find Emerging Hotspots

It is important to gain a good understanding of what is drawing people to a neighbourhood. This can help to paint a full picture of the inherent value a real estate investment opportunity may have.

Using open datasets, such as taxi cab pick-ups and drop-offs, coupled with data from sources like Foursquare and Yelp, you can better understand how nightlife is trending in an area, for example.

In the example below, an investor might notice a specific influx of identified ‘partiers’ being dropped off in the East Williamsburg/Bushwick neighbourhood.

real estate investment opportunities

 

This is valuable information that can cut both ways in terms of impact on a possible investment property.

  • Are the local cafes/ restaurants getting an uptick in quality reviews and check-ins?
  • Is there a new business centre around in a neighbourhood that you are assessing?

This may translate directly to neighbourhood appeal for renters, shoppers and visitors, and boast a positive outlook for an investment property. Say someone is looking to invest in Kuala Lumpur. It would be beneficial to know if the city’s popular shopping mall, is located next to an apartment building that they are eyeing for investment.

Real Estate Investment

You can also explore social data to get a picture of how and where a neighbourhood is being talked about. Mapping mentions over time can give you insights into how popular a location is in the local consciousness.

Enhance Your Visualisation with Demographic Data

Demographic data is key to understanding the long term potential of an investment opportunity.

Visualising trends in income, education, and cost of living will provide you with deeper insights into not only who lives in a neighbourhood, but also how that neighbourhood will look years after you decide to invest.

Open data on demographics can be mapped out to give you a granular look at the trajectory of a neighbourhood, block by block. In the map below, for example, you can see the recent increases in income as well as in the number of residents who hold a bachelor’s degree, therefore painting a picture of recent demographic shifts in Northern Brooklyn, US.

real estate investment opportunities

Unlocking the Full Power of Your Location Data

The full power of location intelligence doesn’t come from simply observing these disparate datasets. On their own, one of the above trends won’t give you a full understanding of the potential of an investment opportunity, and could even steer you in the wrong direction.

But all of these factors together can paint a powerful picture.

Visualising all of these factors, as you can see on the map below, gives you a full view of an area, incorporating many of the key insights and data points that are most helpful when assessing your investment.

Being able to map all of the above data and engage with it using powerful spatial analytics tools can empower your analysis and help you to draw insights into the long term value of a real estate investment opportunity.

Location data is a powerful tool as you explore opportunities in new areas and grow your investment portfolio.

Article first appeared on CARTO.

 

Lava Labs brings together innovation and technology, combined with expertise and deep understanding of businesses and their needs by engaging with industry leaders to empower organizations. We specialise in building custom web and mobile applications in Malaysia and around the APAC region.

Understanding Patterns and Trends using Mobile Data

Understanding Patterns and Trends using Mobile Data

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By Steve Isaac, Content Marketing Producer, Carto 

Understanding the patterns and trends in the everyday actions of citizens, workers, or consumers is key to answering complex business questions. 90% of smartphone users keep their mobile device within reach 24 hours a day, 365 days a year. This makes mobile data a potent tool for analysing trends and solving today’s greatest challenges.

In part 1 of the two-part Mobile Data 101 series, Carto examined some of the challenges and best practices to keep in mind when beginning to work with the different types of mobile data. They also discussed privacy and security, and how it should inform data storage, management, and use policies.

The importance of protecting individual privacy while working with mobile data sets cannot be stated strongly enough. Using techniques like aggregation and anonymisation are absolutely critical when answering questions like those below.

When used properly and safely, the range of business and policy questions that mobile data can help answer is vast. In this post, we take a look at several examples, broken down by industry, to provide a deeper sense of when mobile data should be employed towards answering a question or solving a problem.

Power Retail Management with Mobile Data

Whether you are a strategist working on-site planning and competitor analysis or a regional manager looking to understand local customer demographics, using mobile data can provide an edge in understanding your business.

  • What kind of foot traffic will my new location receive?
  • How does my customer base differ from my competitor’s?
  • How do changes in foot traffic correlate with changes in sales?
  • Is one of my locations cannibalising business from one of my other stores?
Location Cannibalization

The above example highlights the use of area of influence analysis when comparing two distinct stores or locations. With a lot of information visualised, this tool can help site planners, geomarketers, or retail managers.

The areas of influence themselves, presented in contrasting colour, can inform a site planner as to the impact that a specific store has over an area, or where a new location may entice new customers without cannibalising business. The map also provides helpful demographic info, letting a retail manager understand who is shopping at their store, where they are coming from, and when they are most likely to visit.

All of this information can help with marketing efforts, telling a strategist where and when an advertisement will have the maximum impact and expand a particular store’s customer base.

 

Smart Cities: Infrastructure, Transit, and Mobility

Understanding how people move and behave is imperative to city planners and transportation managers working day and night to build smarter cities. In many cases, mobile data can paint a more vivid picture of this movement than traditional sensors.

  • How active is my city?
  • How and where are public spaces used or underused?

This map of Prospect Park from a data story, A Million Walks In the Park, shows how using spatial analytics can answer a number of questions around park management and usage. The use of a spatial clustering analysis elucidates several key facts about the park. By calculating dwell time, the team was able to create clusters that highlight the points of interest in the park that receive the most visits or foot traffic. This kind of information can inform future programming or projects in Prospect Park or other parks around the city, as well as infrastructural decisions such as placement of waste and recycling bins.

The map also shows where individuals tend to enter the Prospect Park, which can be useful when planning for major events held there, such as BRICs Celebrate Brooklyn Festival which boasts a quarter of a million attendees, and determining alternative routing during those events.

The map also uses widgets to allow the user to narrow down their view by day of the week, day of the month, and time of day. By manipulating these histograms and selectors, you have access to an extremely granular view of park usage. This data can help inform decisions around maintenance scheduling, enforcement of closing times, park programming, and more.

  • Where should we expand our public transportation network?
  • How can we reduce traffic congestion?
  • Where do we need more bike lanes?
  • What areas see the most unsafe driving?

Using mobile data to investigate traffic patterns, as shown here, can help traffic managers to make decisions that can save lives.

This map uses navigation system GPS data to track the course and speed of vehicles around Berlin in a time series across a 24 hour period. The map also layers in school zones around the city, where speeding is particularly problematic and puts children at risk.

By manipulating the histograms for speed and time of day on the map, a traffic manager could inform decisions around adjusting speed limits, improving signage, and increasing traffic enforcement. Using mobile data in this way can prevent accidents and save lives.

Geomarketing with Pinpoint Accuracy using Mobile Data

The savvy marketer understands that where you advertise is just as important as how you advertise. Using Telco data can paint a clear demographic picture and show you where your customers spend their time.

  • Are my billboards driving foot traffic to my retail location?
  • Where will my advertisement create the most impressions with my target demographic?
Mobile Ad Impressions

Filtering mobile data with demographics as seen in the above map, which shows advertisement impressions across the US and has widgets that allow filtering on age, gender, and household income, can show you exactly where marketing messages are having an impact, and with whom.

Combining an understanding of the target audience with where they are engaging with advertising content is critical to informing marketing decisions and getting the most bang for your buck with every ad.

Mobile Data Drives Smart Real Estate Development

Success in commercial real estate requires analysts to quickly and accurately assess potential profitability. Location Intelligence applications are becoming a major part of real estate development strategy. Integrating mobile data can help paint a picture of target neighborhoods and allow developers to predict outcomes early on.

  • Will my real estate development draw enough buyers?
  • Can I more intelligently predict changes in property value?
  • What are the up and coming neighborhoods based on visitor foot traffic?
Neighborhood Visitor Overview

Understanding the demographic makeup of a neighborhood can allow developers to make confident decisions, knowing where target buyers and consumers work and play.

The above map uses telco data to visualise the quantity and demographic makeup of the visitors to Barcelona’s distinct districts. Whether you are developing new commercial, industrial, or residential properties, being able to visualise who lives or spends time in your target neighborhood is critical for predicting profitability, educating investors, or investors-to-be, and making smart design choices throughout the development process.

In the real estate industry, and across all of the industries listed here and beyond, visualising mobile data and drawing insights through spatial analytics is becoming best practice, and is critical for data-driven decision making.

Article first appeared on the Carto Blog.

 

Lava Labs brings together innovation and technology, combined with expertise and deep understanding of businesses and their needs by engaging with industry leaders to empower organizations. We specialise in building custom web and mobile applications in Malaysia and around the APAC region.

With New Data Streams, Food Truck Owners Can Determine the Best Locations to Penetrate

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By Peter Murray, Content Markerting Manager, Carto

Advances made possible thanks to location data continues to transform business practices and processes when it comes to site planning. Now, with insights from new data streams, it is possible to determine which sites are most likely to increase sales for seasonal, temporary, and mobile businesses.

Food trucks, a lunch-time staple for some, operate on a location-dependent business model. Generally speaking, food trucks offer similar meal options for roughly the same price, which makes it difficult for them to differentiate themselves from nearby competitors. As a result, food truck location can determine whether a business succeeds or fails.

Recently, Carto helped a local food truck business determine prime spots for their trucks with revenue prediction models. The company provided one month’s worth of anonymised transaction data for each of its 10 food carts. With this information, the team of data scientists from Carto were able to determine current performance, build increasingly confident revenue models, and, finally, predict the six best performing food truck locations.

Measuring Current Performance

Before predicting what locations should be selected to drive future sales, the team had to figure out a way to measure the current performance of each site in Manhattan and Brooklyn.

To get started, Wenfei and Dongjie, two of Carto’s data scientists, first aggregated the data by truck by hour to find a measure for the average spend per hour.

Average spend per hour

The graphs show that hourly revenue for each food truck usually peaks around lunch-time, although sometimes there are spikes in sales around breakfast-time as well. Next, Wenfei and Mamata, Carto’s head of cartography (science of drawing maps), mapped food truck sales using proportional circles reflecting revenue amounts for each location across Manhattan and Brooklyn.

Food Truck Sales

As expected, food trucks located in high volume traffic areas–Grand Central Station, SoHo, Times Square, etc.–are the most lucrative locations for this company.

Now the team then wanted to figure where the best locations are for increasing sales, which means they’ll need to identify some variables near and around the current locations that can serve as predictors in the revenue model. Traditionally, these predictors are identified using data from the census and points of interest (POI) data.

The demographic insights available from census data are helpful for segmenting target customers, but this use case illustrates one of the significant limitations of working with census data.

Census Tracts

The census provides residential data for area of operations, and in the image above this information is presented at the census tract level. However, many food truck customers are workers who commute into the city or tourists visiting New York landmarks, which is likely why the Grand Central Station and Times Square are among the most profitable locations.

As such, residential data offers few insights relevant to increasing sales among this target customer base.

POI data will be more useful here for finding patterns of nearby attractions around high-performing food trucks that can serve as a predictor for our models.

Point of Interests

The first map shows every POI in Manhattan and Brooklyn, but there’s so much noise that it’s hard to determine which attractions appear and reappear near and around each of our food trucks. Since many customers select food trucks based on proximity, 200 meter radius buffers were created around each cart, which is about a 2 ½ to 3 minute walking time, so predictor features could more easily be identified in the second map.

Building More Precise Models with New Data Streams

Now we’re ready to start building a gradient boosted regression (GBR) model that will allow us to determine which features from this data are most important when considering where to place our food trucks. In short, the GBR model will help us rank feature importance that will provide us a list of predictors to look for when considering a potential food truck location.

The first revenue model was created using only traditional data sources, specifically census and POI data:

Model One

The GBR model returned an R-squared score, a measure of the variability within the data set from 0-1 that can gauge confidence in the model. An R-squared score of .38 means that there is a range of variability in the data. This means that the current data does not provide a high enough  confidence on what features are most important to consider when selecting a food truck location.  More data is needed to increase the score.

To improve the model, MasterCard spend data was added and the same equation was performed to see whether the R-squared score would increase.

Spend Score

MasterCard spend scores provide aggregated and anonymised merchant-level transaction insights on where, when, and how people spend money. More specifically, the transaction percentile score provides a frequency measure that is important. Because most food carts offer similar types of food for around the same price, the frequency measure provides insights on customer volume for each cart.

Model Two

Here we see a sizeable score increase and greater alignment among points in the scatter plot. However, the R-squared score could be stronger so a layer of foot traffic data was added to the model.

Model Three

Here the R-squared score has increased by 18 points since model one, which makes a lot of sense and confirms our earlier assumption with POI buffers that food trucks rely on foot traffic from nearby customers.

It is significant to note that when additional derivative data layers were added to our model there was an improvement in our R-squared score.

Without these new data streams, we would not be in a position to identify with much confidence where the best locations are for each food truck.

Feature Importance

The image above presents the 12 features that our model identified as having a statistically significant impact on food truck sales, and the top four features were selected to serve as predictors for identifying new locations: 1. Foot Traffic from previous hour, 2. Foot Traffic from current hour, 3. Day of the week, and 4. Mastercard frequency score.

Revenue Predictions

Now it is time to start mapping the selected predictors across New York City using 100×100 meter grid tiles (roughly the size of a city block). Next, using a histogram, we looked at the sales distribution across the city and calculated the weekly sales average per truck to be approximately $2,786 (approx. RM11,360).

Since the goal is to find new locations that are likely to increase sales revenue, we selected the higher end of the revenue distribution and then clustered them into revenue areas. Because the model’s R-squared score was .63 there’s not quite enough confidence to pinpoint the exact location for each truck. Instead, these revenue areas were clustered to locate regions within a neighbourhood with a higher likelihood of being profitable.

Model Three

The image above shows the changes to the map that each of these operations yielded. In the end, six locations were identified with revenue predictions for each. Below, the six locations are ranked highest to lowest by weekly sales average for each locations.

  1. Corona Park: $6,128 (approx. RM 24, 900) weekly sales average
  2. Penn Station: $5,975 (approx. RM 24, 370) weekly sales average
  3. SoHo: $5,911 (approx. RM 24, 110) weekly sales average
  4. Grand Central Station: $5,766 (approx. RM 23, 520) weekly sales average
  5. West Village: $5,234 (approx. RM 21, 350) weekly sales average
  6. DUMBO: $5,193 (approx. RM 21, 140) weekly sales average

While there are the usual suspects on this list (Penn Station, Grand Central, etc.), it is surprising that Corona Park turns out to be the best location for increasing food truck sales revenue. When nearby tourist attractions and the area’s population density are taken into consideration, the results make sense.

A New Era of Site Planning

New data streams are ushering in a new era of site planning, therefore making previously impossible solutions possible. Indeed, as this food truck example highlights, the future of site planning depends on accessing and working with various types of data, from traditional sources to new derivative data sets, to identify, understand, and quantify the impact that mobility patterns will have on your sales revenue.

Article first appeared on the Carto Blog.

 

Lava is an authorised Partner of Carto in Malaysia and with more than a decade of experience in the industry, we’re proud to say we’re one of the leading cloud consultants and service providers in the Asia Pacific region.

omnichannel marketing strategy

Drive Retail Success With An Omnichannel Marketing Strategy

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By  Steve Isaac, Content Marketing Producer, Carto 

Rising e-commerce has changed the retail game and continues to grow, with global online sales hitting $2.3 trillion (approx. RM9.3 trillion) in 2017. With estimates that that figure will reach over $4 trillion (approx. RM16 trillion) by 2021, many experts have previously asserted that offline retail is entering its twilight years.

These assertions are supported by what feels like a news story every day bemoaning a rash of store closings from companies of every stripe, including those previously hailed as titans of brick and mortar retail. Last month (June), for example, Sears announced the closure of 72 stores, Foot Locker announced 110 closures, an 11% reduction in their total number of locations, and the nearly bicentenarian Lord & Taylor, announced a closure of 10 of their 50 total stores, including their flagship 5th Avenue location.

Looking at brands Malaysians are more familiar with, according to several reports, Adidas and H&M are among the few who are looking to close down some of its physical stores in an effort to increase e-commerce sales.

In an article by The Edge, Retail Group Malaysia managing director Tan Hai Hsin was quoted saying: “Online shopping will not replace you [retail stores] but you have to learn how to capture your customers…”

 

Omnichannel Marketing – Bridging the World and the Web

For brick and mortar retailers to thrive in this environment, many of them are adopting omnichannel strategies, engaging the consumer not just in-store but also online across multiple platforms and devices where a particular shopper may live.

In Malaysia for instance, we can observe a cross over of retailing formats and according to Tan, this trend will continue.

He said: “From offline to online, many stores have started to offer online services, such as Tesco, Jaya Grocer, Borders, and Sen Heng.

“[For stores that cross over] from online to offline, we have FashionValet, Xiaomi, Naelofar, Buku Fixi, and Twenty3,” he elaborated.

More than simply maintaining a functional online presence, a true omnichannel marketing strategy maintains fidelity of brand and experience at every touch point.

Today, key location intelligence techniques and new data streams serve to improve these omnichannel business plans, bolster brick and mortar retailers embracing change, and even bring previously online exclusive sellers to establish storefronts of their own.

Related: What Online Retailers Can Learn by Mapping Sales Data

 

Targeted Geomarketing

A successful omnichannel marketing strategy requires retailers to keep their thumb squarely on the pulse of who their customers are, being able to deliver an experience to a target customer both before they walk into a store and after they leave.

Identifying key demographic groups, understanding where they are concentrated, and how they engage the world around them, is key to serving ads and creating touch points that will drive return business to a retailer.

A Mobile Data analysis tool like Vodafone Analytics lets geomarketers explore demographic data for valuable insights. The below example shows the demographic makeup of neighborhoods in Barcelona.

Unlike census data, a telco data analytics tool like this can provide deeper insights such as work day population demographics vs. weekend demographics.

Neighborhood Demographics

Regardless of what a retailer is selling, a deeper demographic picture can allow for greater targeting of key demographic groups based on age and gender as well as where they spend their time.

This kind of information is critical to the geomarketer targeting online ads, managing programmatic, audience targeted outdoor advertising and billboards, or picking a spot for an experiential pop-up store.

Similarly, mapping out billboard locations and nearby businesses as above can clue marketers into where they can get the best bang for their buck.

In addition to traditional demographic data sources such as census data, and mobile data streams, credit card spend data is a powerful new data stream that retailers can draw on. This aggregated and anonymised transaction data can help marketers to answer challenging questions about marketing performance.

 

Site Planning Optimised for Omnichannel

For the retailer looking to engage with their customers both online and offline, selecting the location for your in-person interactions is a large part of the omnichannel marketing strategy and should be informed using location data.

Whether you are setting up a new brick-and-mortar location and want to optimise for demographics and transportation, you are trying to avoid cannibalising an existing location, or you are setting up a pop-up store that will maximize foot traffic at key moments, location intelligence applications can inform the site planning process.

Site Performance

Using a tool like CARTO’s Reveal allows retailers to measure the impact of a new location, understand demographics around a new site, and predict how future sites will perform. The above map shows not only key demographic insights but also nearby points of interest. For omnichannel marketers, these points of interest represent opportunities for offline experiential co-marketing and potential leads for online partnerships.

CARTO’s new Foot Traffic data stream combines mobile events and GPS data to measure the number of pedestrians in transit to and from distinct locations. Users can leverage insights about foot traffic to make critical business decisions such as site selection and to inform omnichannel efforts such as proximity marketing via sms to target demographics.

Article first appeared on the Carto Blog.

 

Lava is an authorised Partner of Carto in Malaysia and with more than a decade of experience in the industry, we’re proud to say we’re one of the leading cloud consultants and service providers in the Asia Pacific region.

social media engagement

How You Can Map the Social Media Engagement of Your Business

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By Steve Isaac, Content Marketing Producer, Carto

We wanted to see how maps can be used to assess the level of social media engagement among users. So, we decided to zoom into a recent Twitter project. Here’s the story:

On 8 January 2018, CJ Marple, a 3rd grade teacher at Osage City Elementary School, Kansas, US, began a lesson exploring the speed and distance that information can travel using social media.

Marple, an active twitter user, worked with his students to compose a tweet, with the intention of mapping the social media engagement as the message spread. It isn’t hard to imagine CJ and his students’ surprise as the tweet began to pick up steam, go viral, and circle the globe.

With over a million and a half total engagements, retweets from several celebrities (including Modern Family’s Eric Stonestreet, former White House Press Secretary Dana Perino, and US Senator Jerry Moran), and responses from across the world, CJ’s tweet proved shockingly effective.

He had initially set up a map in his classroom to flag retweets using post-it notes, but as the tweet continued to go viral and the data points continued to pour in, that solution likely proved untenable. To help CJ out, Twitter stepped in:

social media engagement

Twitter used CARTO to visualise the location of retweets, mapping out the spread of the data and in doing so, were able to create an extremely valuable teaching tool for CJ. Not only did their map represent the locations of each retweet, but it took Twitter’s temporal data into account.

RelatedThis is How Location Data Can Resolve the Global Refugee Crisis

social media engagement

By creating an animated time-scale map, Twitter was able to fully visualise CJ’s lesson. The time-scale map provides a more complete view to CJ’s students, not only showing them where their information had spread, but also when and how, presenting the pattern of retweets using a histogram.

When discussing what led them to put together this map, Elaine Filadelfo, Data Editor at Twitter noted:

“This story was the perfect fit for using CARTO. We could clearly visualise the original tweet being posted in Kansas, and then watch as it spread regionally, nationally, and globally over the following days.”

At its core, visualising our location data empowers us to further understand the world around us and the systems that we interact with every day. CJ noted that “looking at the data, the thing that was really crazy for them to see (and their big takeaway) was where it was getting retweeted.”

With retweets from Chile to China, location intelligence helped a class of 3rd graders from Kansas see just how connected to the wider world they truly are.

Article first appeared on the CARTO Blog.

 

Lava is an authorised Partner of Carto in Malaysia and with more than a decade of experience in the industry, we’re proud to say we’re one of the leading cloud consultants and service providers in the Asia Pacific region.

civic mapping

A Civic Mapping Project to Determine Neighbourhood Needs

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By Tyler Bird, Community Lead, Carto 

Citizen-led organisations are working in cities across the world to better understand the intricacies of housing markets, housing policy, tenant rights, as well as the socio-economic and demographic evolution of neighbourhoods.

Geospatial technology and location intelligence have become fundamental tools for analysis, communication, storytelling, monitoring and evaluation.

CARTO’s Grants For Good Programme connects academics and organisations to the technology they need to create impactful projects. In this article, we’d like to talk about an influential civic mapping project in New York City.

New York City

Rising rents, gentrification, crime reduction and immigration trends are changing the demographic make-up of neighborhoods across New York City. The Citizens Housing and Planning Council (CHPCNYC) is a non-profit organisation and a community of people that share ideas and shape practical solutions to help the NYC government and housing industry ensure residents’ housing needs are met.

CHPC’s mission, since 1937, is to develop and advance practical public policies to support the housing stock of the city. They do this by understanding New York’s most pressing housing and neighbourhood needs.

Much of CHPC’s recent work focuses on aggregating and segmenting demographic data that demonstrates the demographic and socio-economic shifts taking place in neighbourhoods over a ten year period.

CHPC released an extensive report, Making Neighborhoods, as well as an insightful interactive map allowing users to explore the rich temporal, qualitative, and geographic data. Explore the map for yourself to see how neighbourhoods in NYC have changed over a ten year period!

civic mapping

The civic mapping project used ‘cluster analysis’, as a way of parsing large amounts of data into groups with shared traits. Populations clusters were identified in 2000 and tracked again in 2010. Dillon Massey, Housing Informatics Designer, played a leading role in the project and managed the entire data visualisation and mapping.

“Using 16 demographic variables to measure race, age, foreign birth, household type, education level, and poverty, the model formed 14 ‘clusters’ of census tracts where populations share these characteristics.”

Citizen-led initiatives are complemented by innovative approaches by the public sector for better citizen-centric and participatory governance. The New York City Mayor’s Office is doing just that with new real-time data dashboard powered by CARTO. The dashboard shows indicators which include up-to-date crime statistics, service provision performance, health figures, infrastructure project updates, public works, 311 data, environmental indicators, housing and homelessness statistics.

It is crucial to fully comprehend how a city is performing, how neighbourhoods are changing and who is being affected by policy. This understanding is achieved by analysing, monitoring and evaluating the constantly shifting social, economic, and political tides of a city.

Article first appeared on the Carto blog.

 

Lava Labs brings together innovation and technology, combined with expertise and deep understanding of businesses and their needs by engaging with industry leaders to empower organizations. We specialize in building custom web and mobile applications in Malaysia and around the APAC region.