Before answering how valuable Point of Interest (POI) data is, we need to take a step back for a brief overview: What is POI? How to collect POI data? What factors influence POI data quality?
A point of interest (POI) is a specific point that someone may find useful or interesting. The term “of interest” is not to be taken literally: it means of interest to a specific user in a given context. It might be a hotel, a restaurant, or a tourist attraction, as well as ordinary places like post offices, schools, grocery stores, or parks.
Points of interest are the basis for most of the data supporting location-based applications. POI data feeds most digital maps and navigation systems and can support various applications, including real estate, finance, and healthcare-related.
Anyone with a mobile device can be provided with a geo-located, time-aware POI service to get recommendations on the nearest places. Consumers or end-users, for instance, can use POI data to locate a shopping place, an accommodation, the nearest bus stop, or emergency service.
So, how is POI data generated? Some of the most common methods are data collection from open sources or social media platforms, which produced a massive amount of data every day
Many methods have the flaw of providing an inaccurate or inconsistent dataset. Therefore, through a team of experts, companies or government entities can actively, accurately, and consistently collect and update data for a reliable and efficient data flow. However, which elements should they consider when choosing a data method or a provider to obtain quality POI data?
Governments, or enterprises, can leverage POI data to identify patterns and trends and make more targeted data-driven decisions.
Essentially high-quality POI data is defined by update frequency, coverage, ease of use, and consistency.
Our world is changing so rapidly every day. In order to provide users with the most up-to-date information, it is necessary to keep POI data constantly updated.
Good coverage means that the data provider can offer a wide variety of POIs, ideally on a national or even global scale, using both local and global providers. This way, users can rely on POI location data in an app to find the place they are looking for.
Speaking of consistency, there must be a match between the real world and the POI location data in the database. For example, to avoid a situation when, while searching for a specific place, users discover that an app made them go elsewhere or that the place has been closed for months. Thus, we need updated, accurate, and consistent POI data.
Any industry can leverage POI data differently, although the most common application, which plays a central role for companies or government agencies, includes competitive mapping and site selection.
For instance, in the telecommunication industry, POI data can help determine where to build new infrastructure, expand the customer base, or quality of services. It can allow to study the competition and understand user needs by geography.
In the retail world, POI data can help answer questions such as: What is the market share of a business? How much is the spending capacity of people who live or work in a specific area? What is the demand for that service? Which are the nearby competitors? Knowing the answer to these questions enables marketers to make targeted decisions, such as whether and where to open a new store or when to close one for lack of profit.
The public sector can use POIs to achieve many goals. By mapping a given area, it is possible to analyze and understand if existing services or products are effective, which services need to be improved (e.g., public transport, hospitals, etc.), and where those services are missing.
POI data is also used in the real estate, consulting, and financial sectors.
Location is a key element in real estate. For example, providing consumers who want to rent or buy a property with useful information about the area around that location? POI data can provide all those insights about nearby facilities, stores, schools, etc. Its role has further grown with the pandemic: online platforms for customers are now used, not only to locate the nearest services but also to visit places virtually from home.
Marketing and advertising companies are also leveraging POI data. With location-based advertisements and more targeted campaigns, businesses can improve their strategy, deliver the most appropriate message to customers, attract new ones, and increase their revenues and brand value.
The transportation and logistic industries are also using valuable POI data. POI data enables fleet movement tracking, route management, transportation infrastructure status assessment, and much more.
In addition, as e-commerce has grown, delivery services have become crucial to provide all sorts of goods. So, to maintain profitability, provide reliable and secure services, and keep customers highly satisfied, postal and delivery companies need to get high-quality POI data.
What benefits does it bring? Among the many, we will highlight a few:
Gaining POI data also enables reducing errors, delays, or loss of goods, minimizing last-mile cost with quick deliveries or by offering alternative pick-up points; mapping drop-off and pick-up points, that help to create a general but accurate picture of the served areas and plan additional future points, where required.
Whether you're an enthusiastic developer, a tech aficionado, or a marketer working with mobile app-generated Big Data, it's important at least to understand the technical side to facilitate our activities. Among the basic knowledge, it is crucial to understand what an SDK is.
In this article, we will try to explain in simple and clear words what SDK is and what role it plays as a data source, its benefits, and its relevance in the world of data science and business data analysis.
The acronym SDK refers to "Software Development Kit." An SDK, basically, is a set of tools that allows a team of developers to create a mobile application, which can be connected to other software.
SDK tools include all the basic technical elements that developers can use and integrate into their apps, such as an editor, libraries, run-time and development environments, compilers or debuggers, or other analysis or testing tools.
Using an example in the real world, consider the SDK as a toolbox containing nails, a hammer, drill, gloves, and other work tools. Using these tools, you can build small things (like a piece of furniture) or more complex projects (like a house). An SDK essentially operates as a toolbox for software developers. Instead of hammers and nails, it contains APIs, IDEs, and other essential tools. SDKs are designed to be used for specific platforms or programming languages but also to improve developer-created mobile app functionality: to show advertisements in apps or manage push notifications or as an analytics system.
We've seen what elements SDKs provide to developers to create software applications on a specific program. This allows programmers and developers to avoid reinventing the wheel every time they want to code standard application features such as data storage, location, user authorization, and geofencing, among others.
An SDK will likely be used outside of the company where it was produced, so it should provide value to other companies and the developers who use it. The SDK's value depends on its features, which in general should be the following:
The SDK can also be considered a data source. SDKs themselves are not trackers, but they are tools through which most tracking occurs through a mobile app installed on our smartphone.
Developers can use SDKs to encode location capabilities directly into an app, thus ensuring that the app collects GPS and location data (if the user gives permission for the app to collect this location information). That makes the SDK a great source of data about people's movements and behavior.
Therefore, a location SDK collects users location data through mobile apps installed on their mobile phones. These are the main characteristics of the data:
However, it is worth mentioning that, since the location data collected via SDKs have a higher quality and accuracy, it is available in a smaller volume than other types of data sources, such as bidstream data obtained on a large scale. It is also considerably more expensive, as it requires direct integration of the SDK into mobile apps. This means more human resources, time, and money spent on partnership negotiations with app development companies, data assessment, and integrations.
SDK is a set of tools needed to provide unique features to improve the user experience of mobile apps. SDK components not only allow developers to add features visible to the end-users but also collect different types of data, such as location data, usage data, behavioral data, etc., that help improve user experience.
Accuracy and security are some of the strongest advantages of SDKs as a data source. Still, considering the downsides, it is a relatively expensive, smaller scale tool. Many apps, which use SDKs to track location data, require a significant amount of power, reducing the performance and battery life of a mobile phone.
Bidstream data is data that is passed with a bid request. A bid request is a set of information sent by ad exchanges to the advertisers containing inventory details like:
In addition, for marketing purposes, a publisher may also share other data with advertisers, as a bid request:
So basically, we refer to bidstream as data collected by ad servers when ads are served on a website or mobile app.
Think about it, every time we open a website or a mobile app, in which ads pop up, the ad publisher gets a bunch of information, including our location, and then this information is passed to the ad firm that provides that ad.
One advantage of this type of data source over other sources is that it is both easier to scale and easier to obtain.
But consequently, there are several disadvantages like
Also, considering, for instance, that everyone is constantly using their phones, it results in a huge number of data points that may or may not be attributed correctly.
Thus, quite often, bidstream data is inaccurate and unreliable.
For this reason, nowadays, there is an increasing focus on data quality over quantity: marketers are demanding more transparency about how the data is created or where exactly it is from.
As we mentioned above, the problem with bidstream location data is that it doesn’t provide the exact device location in many cases. Without accurate data, it is tough to understand the context of the device and get meaningful insights that consequently provide marketers with enduring ROI.
Bidstream data is easy to obtain and scale, but it is often inaccurate and unreliable. Of course, there are a lot more different sources that provide location data, but certainly, an important aspect to consider is transparency.
Understanding the strengths or weaknesses of different data sources makes it easier to decide how and where best to use them. Follow our blog to learn more about other data sources that provide more detailed and accurate information about the location and people movements.
We have been facing many changes in different areas of our lives. The retail industry has been forced to adapt and respond to the impact of the pandemic. Surely, many of the changes made to react to this situation will become permanent, creating what is lately being called “a new normal”.
One of the keys to success in the new normal of commerce is Location Intelligence. It is recognized that the core element of retail has always been location, and even from a different perspective, it will continue to be so.
To recover after the crisis, grow revenues, and acquire new customers again, marketers started to leverage location intelligence to understand who their audience is, what they are interested in, where they come from, and go after. This also helps manage logistics operations, orders and shipments, production, inventory, and control social distancing compliance efficiently. The data gathered using location intelligence will enable business leaders to develop customized marketing and sales strategies.
The success of these activities will not simply depend on “going digital”.
So why does location remain an essential element for retailers? Let’s see some of the major benefits of location data analysis here below.
Location intelligence is the key component that helps retailers get closer to their customers and attract new ones.
By collecting data, retailers can examine customer demographics, movement patterns, purchase history, and location to develop effective and personalized relationships. In addition, implementing heat maps within the store premises to collect data such as the time a consumer spends in a given area; areas of the store with the most footfalls, etc., allows
Starbucks is an example of brands that effectively use location analytics. To enrich and personalize the customer experience, Starbucks leverages data as what, where, and when customers buy their products and then combines them with other data such as weather, holidays, time of the day, or special promotions.
For instance, on the GeoCTRL dashboard below, several parameters are considered: GPS tracking, foot traffic, revenue, the volume of visits, etc. We can observe how the frequency of visitors is strongly correlated with the weather, sales, advertising (online and offline) and obviously fluctuates depending on the day of the week and time slot.
Moreover, by exploiting data collected from the loyalty mobile app, Starbucks personalizes its customer experience and improves customer satisfaction. This is how it works. When a customer visits a new Starbucks store, the point of sale system reads that person’s order patterns from a mobile app and gives indications to the barista, for example, on the preferred order of that customer and suggest new products and drinks available in that specific location.
Besides the study of consumer behaviors that provide more targeted services, location intelligence can improve the customer experience by sending up-to-date information on store openings and closings in different geographic areas.
So, why is location intelligence so important for retailers today?
By better analyzing and understanding our customers, or potential customers, we can create a more personal relationship with them and better adapt our products to their demand, apply more targeted strategies and improve the customer experience.
This past year has led us to reflect on how fast the world is changing. Geospatial analytics is not an exception. “The global geospatial analytics market size was valued at $58.35 billion in 2019 and is projected to reach $158.84 billion by 2027, growing at a CAGR of 14.2% from 2020 to 2027.” – Valuates Reports.
Many industries started to focus on spatial data analysis as part of their data science research and development. Therefore the requirements for data completeness and validation are growing. The main requirements of data scientists towards the geospatial data are its continuity, real or near real-time flow, and availability of multiple alternative sources. However, there are other criteria of data assessment. Let’s explore some of the trends that will drive geospatial analytics in 2021.
Geospatial data is generated in massive volumes from millions of cell phones, sensors, and other sources every day. At the same time, raw data doesn’t have any value. The data has to be cleaned, analyzed, and visualized faster than ever before. In many cases, this process requires the development of complex methodologies and constant monitoring by experienced data scientists.
Data democratization makes big data accessible not only to data analysts and engineers but to any sales, marketing, or executive professional with no data science background. Thus, data democratization allows easy access to thousands of datasets from controlled sources, simplifying their licensing process, and provides simple and clear visualization to the end-user. It provides unbeatable benefits to different industries, allowing them to work with high quality and always up-to-date location data.
Managing and storing the enormous amount of data that is produced daily can quickly become complex and costly for any company. It is a common situation that working with Big Data rapidly increases the demand for financial and human resources to analyze and extrapolate usable information. That’s why many companies consider or are already in the process of adopting cloud computing solutions.
An entirely new architecture of cloud computing solutions has been created for geospatial applications, taking full advantage of the web services and elastic computing and data storage capabilities.
Cloud computing SaaS (Software as a Service) is one of the models existing in the market. Such platform-based solutions provide hundreds of analytical and visualization capabilities combined into easy-to-use applications for various implementations. A modern geospatial cloud solution offers ready-to-use demographic datasets and map/image layers that allow users to gain immediate context for applications of all types. Among the leading companies providing these services on the market are Amazon, Google, and Microsoft.
The biggest benefit is that engineers can run applications and services without managing and operating expensive and complex server infrastructures. The expansion of cloud computing services will definitely continue to evolve and grow throughout 2021.
Efficiently managing Big Data merged from different sources to gain business benefits is crucial for company competitiveness. Data warehousing is a key component of this process.
Data warehouse is a combination of technologies and components that provides an environment separate from the operational systems and is completely designed for decision-support, analytical-reporting, ad-hoc queries, and data mining. It is a process of turning data into information and making it available to users in a timely manner.
So, why do we include it among the most relevant trends for spatial analysis? Because businesses that have to manage massive amounts of data can do it quickly and safely with data warehousing.
Data warehouses can ensure data security by using encryption and specific security setups that are intended to protect confidential data. It also allows to look back at historical data to analyze the evolution of trends, derive accurate insights, and, more importantly, make smarter decisions.
Light detection and ranging (LiDAR) technology is a growing source of detailed 3D elevation data that can provide meaningful context over geographic areas.
In synthesis, LiDAR enables collecting a wide range of information for numerous applications and delivers several advantages over traditional aerial mapping methods. LiDAR is changing the paradigm of terrain mapping and attaining popularity in several applications such as floodplain mapping, forest inventory, geomorphology, hydrology, landscape ecology, urban planning, survey assessments, volumetric calculations, and coastal engineering.
In the past, the common way to generate topographic maps was using traditional stereophotogrammetry. It was a manual method that obviously cannot match the quality or accuracy of a lidar-derived digital terrain model.
In fact, the current technology provides users with a complete dataset and a precise 3D visualization of geographic areas, allowing users to create survey maps, update a GIS system, or create digital models of surface, terrain, etc., without having to travel to the site, thus saving time and resources.
These are some trends in Spatial Data Science that will definitely play a key role in 2021 and bring huge benefits to many industries, such as Transportation and Logistics, Retail, Energy, and Agriculture.
Today, retailers can benefit from technologies and tools that help to understand what drives their business. People movement tracking technologies quantify the activities in a physical store. Retailers can measure foot traffic, common customer routes through the store, “hot spots”, demographics of store visitors, and the impact of promotions and marketing campaigns on store visitation.
This article gives an overview of the most trivial to the most efficient in-store technologies for retailers. Let’s dive in!
Break-beam sensors work by detecting motion. They are installed at the entrance to a store and send out a beam of light invisible to the human eye. The sensor counts each time the beam of light is passed through and understands that the person will pass through the beam of light twice: when he enters and when he leaves.
This technology is best suited for small stores or boutiques that need a simple way to measure customer visitation, providing anonymous data to their business.
Level of insights: Low
Token-based analytics is one of the latest technologies, which involves installing low-energy electronic devices (tokens) on the shopping tools available in the store (e.g., shopping carts, rolling baskets, hand baskets, etc.). The tokens then transmit their location in real-time and collect information about each customer’s entire journey. The tracking is anonymous, as there is no way to identify a specific person holding a shopping tool.
This option is more valuable for the bigger stores, as they don’t count visitors who don’t use carts and buskets.
Level of insights: Low
Camera-based people counting detects people movement inside the store with a high level of accuracy.
Typically, those cameras cover the entire store and provide detailed data on in-store activity, generating accurate heat maps that analyze customer movements, dwell time, and high and low traffic areas at different times of the day. This type of counting also collects anonymized information avoiding issues related to customer privacy.
Level of insights: Medium
Imagine passing by your favorite store at the mall and promptly receiving a push notification on your phone with a 20% off on that same dress! This is an example of “proximity marketing”.
Some stores purchase expensive location data based on cellphone data or GPS. However, there is also a lower cost, accurate and discreet tracking tool: Bluetooth Beacons.
A beacon is a small hardware device that enables data transmission to mobile devices within a specific range. Typically, beacons can only work with Bluetooth signals. However, more advanced ones, like GeoCTRL sensors, can also communicate via Wi-Fi signal. Need to mention, beacons do not require customers to connect their Bluetooth or Wi-Fi to a specific beacon. They only need to be switched on.
Beacons can count footfall traffic, dwell time, the proximity of the devices to the cashier, and even identify new and regular customers. When choosing your provider, make sure they take privacy seriously and ensure the anonymization of signals.
Level of insights: High
We all know, Wi-Fi allows smart devices to connect to the internet wirelessly. However, some retailers give this free solution connection “not for free”.
Wi-Fi enables track customer visits, capturing data from their smartphones with Wi-Fi enabled and connected to a network. The advantage is a low-cost and simplicity for tracking unstructured movement in large locations such as a shopping mall, stadium, or airport.
The disadvantages its low accuracy and the need for a direct network connection. On average only 10% of customers actually use the internet in-store.
Level of insights: Medium
Mobile app data can provide unique and valuable insights. Mobile GPS data, for instance, which is generated by capturing satellite pings and then transmitted through mobile device apps, allows to identify exactly where the owners of the mobile devices are located (point of interest or PoI), what they do and how they move. In this way retailers can obtain, amongst many, useful socio-economic and demographic insights, understanding customers interests, flow and dwell time, location of origin or peak hours that they can leverage to boost their business and to improve their marketing strategies.
Level of insights: High
These are a few examples of the tracking technologies existing today. Certainly, retailers can opt among a wide range of solutions and devices, utilizing only one of them or multiple combined. Measuring and analyzing in-store traffic and customer behavior is a key element in optimizing customer experience, achieving rapid growth, and increasing productivity.
Advanced location analytics can help determine where to place specific product types, price the most profitable retail spaces, organize store layouts, optimize marketing campaigns, customize offers and discounts, and constantly track performances. Furthermore, by knowing peak days and hours, retailers can better manage staff and shifts as needed.
GeoCTRL Footfall Sensors use Bluetooth and Wi-Fi connection to track the audience around a store, enabling businesses to get unique insights about regular, new customers or just people walking around a location.
When we talk about tracking, we are not referring to a single technology. Rather, we are talking about a convergence of different technologies that can be used to track the location and movement of inventory, people devices, objects, fleets, etc. This article presents the most used tracking technologies in a nutshell.
The Global Positioning System (GPS) is a network of orbiting satellites. A GPS tracking system uses microwave signals from the Global Navigation Satellite System (GNSS) to track the device's position, speed, time, and direction. GPS is a core of many tracking, navigation, and mapping services.
There is no need to list the GPS application fields. From emergency management to military, automotive, transportation, and logistics, GPS became a foundation to many software and hardware.
BLE beacon is a small hardware device that enables data transmission to mobile devices within a specific range. In most cases, recipients must have active Bluetooth (that allows tracking) and opt-in to accept the sender's transmissions (for communication purpose).
BLE beacons have countless applications. In retail, they can enhance guest experience by providing virtual maps, sharing tips and relevant facts, and offering promotions and discounts. In hospitality, beacons are used to send welcome greetings to hotel guests and communicate route maps. In transportation, specifically at airports and train stations, travelers can receive important information on their trips. In hospitals, manufacturing sites, and warehouses, beacons allow track and locate assets.
In simple terms, Wi-Fi allows wirelessly connecting smart devices at high speed to the internet. It is ideal for unstructured movements in large venues such as airports, stadiums, or shopping centers.
For location intelligence purposes, Wi-Fi can capture smart device signals and thus count footfall, generate density heatmaps, track device movements and flow, identify new and returned visitors and calculate dwell time.
But Wi-Fi applications go beyond the wireless internet connection and tracking. Wi-Fi creates a foundation for IoT devices networks, for example, smart home systems.
Looking to the healthcare sector, Wi-Fi can be used to transmit data from connected medical equipment directly to mobile devices or workstations. This allows healthcare providers to access real-time patient information from anywhere, eliminating the need for physical patient records and improving patient well-being accuracy and level of information.
QR is a data-encoding system via a small, digitally encoded squares and dots pattern, designed to be read via scanners or cameras. In essence, this is one of the basic applications of computer vision with a wide range of commercial applications.
QR codes can store many different types of data and can be used for many purposes. It can contain text data, weblinks, images, and even bank account or credit card information to process payments. The list could continue.
RFID, or Radio-frequency identification, stores data using electronic tags with radio signals that can be scanned and read by special RFID readers on a short distance.
There are two types of RFID tracking: passive and active RFID. Unlike active RFID, passive systems do not actively track movement in real-time. Active tags are mainly used for storing specific object data, monitoring physical parameters (such as temperature, humidity, motion) and location. They are applied to various industries, such as construction, public works, security, and home automation.
Near-field communication (NFC) is a wireless technology that provides two-way short-range (maximum of 10 cm) contactless connectivity.
NFC is an upgrade to the existing proximity card (RFID) standard that combines the interface of a smartcard and a reader into a single device. It allows users to seamlessly share content between digital devices, interact with contactless infrastructures, such as payment, ticketing, and access systems.
Ultra-wideband (UWB) is a radio technology that uses a very low energy level for short-range, high-bandwidth communications over a large portion of the radio spectrum.
UWB is traditionally applied in non-cooperative radar imaging. Most recently, it is applied for sensor data collection, location, or accuracy tracking. Moreover, since September 2019, UWB support has been included in higher-range smartphones.
But how it works? If a smartphone with UWB, like the latest iPhone, is near another UWB device, the two devices start ranging or measuring their exact distance. For example, an airport or mall, with a beacon network installed, can monitor a pedestrian's progress through the building and offer directions to a destination in real-time.
Deep learning is a branch of artificial intelligence (AI) that has become a foundation for computer vision development. Computer vision is now one of the most prominent research areas in AI and Computer Science since it has application potential in many industries.
The key element of computer vision is designing computer systems that can capture, understand, and interpret visual information from images and videos. Once data is obtained, it is converted using contextual knowledge into insights that guide the decision process.
Computer vision is widely used in retail. It helps analyze customer behavior, detect theft, and count visitors, which is especially useful for complying with the occupancy restrictions opposed due to the pandemic. Autonomous and transportation industries use computer vision for autonomous driving, traffic analytics, and smart public transportation development. In healthcare, computer vision is applied to support skin disease diagnostic, including different types of cancer.
Lidar (an acronym standing for light imaging, detection, and ranging) is a method used to measure distances (ranging) by illuminating the target with laser light and measuring the time it takes for the light to reflect back to the sensor.
Lidar is most commonly used in the location intelligence ecosystem to create high-resolution maps and train autonomous vehicle software to navigate through the environment safely.
Let's imagine framing any object with a tablet or a smartphone to see additional information on the display: text, images, real, animated movies, etc. This is possible with Augmented Reality (AR).
One of the principles of AR is adding digital layers of data on real objects. AR allows simulating reality or a context different from the one in which the subject is physically located and to observe the context around it enriched by additional data. This is how it works: a camera identifies the object in the frame, the system recognizes it and activates a new communication level that overlaps digital data related to that object.
There are many possible applications of AR as part of location intelligence. AR is implemented in the industrial sector, improving productivity and assets management. Location-based AR is used in marketing and advertising via Digital Out of Home (DooH), mobile ads, or mobile apps. This is one of the most popular choices for high-budget campaigns in the entertainment, fashion, art, consumer goods, food & beverage, tourism, and hospitality industries.
Stereo sensors consist of high-resolution cameras to capture a three-dimensional image of objects. The tracking is highly accurate and enables monitoring high volume traffic, such as queue management or customer engagement.
3D Stereo sensors allow counting footfall and tracking people movement, analyzing the input, and providing accurate information about gender, age, visual attention, and visitors behavior. It is not that privacy-friendly; therefore is used mainly for surveillance and security reasons in airports, banks, and political events.
Thermal imaging is a method that uses infrared radiation and thermal energy to gather information from objects and formulate images of them. It is based on the science of infrared energy, the "heat" that all objects emit. Since it does not rely on visible light, it is effective in the dark, smoke, fog, and haze.
At first, it was used for military purposes, for instance, in the Korean War. Since then, thermal imaging has been improved over the years to be applied for law enforcement, disaster relief management, or emergency management. In location intelligence, thermal imaging is used to accurately and precisely visualize density heat patterns. It is also used in healthcare to detect temperature abnormalities and in navigation, supporting night travels. These are the most common location intelligence technologies. Their rapid development opens new opportunities for increasing the quality and quantity of collected data. Both, data quality and quantity, are extremely important to for in-depth analysis and data-driven decisions for business growth and success.
This week, we present an overview of the Geospatial analytics market for the Middle East, Africa, and South America.
The geospatial analytics market is expected to grow at an annual compound growth rate of 16.84% during 2019-2027.
The Middle East and Africa region is the second-largest market for cell phones. For example, the United Arab Emirates and Saudi Arabia have one of the highest smartphone adoption rates. The booming of the geospatial analytics market in this region also directly reflects the economic situation and actions taken by some governments.
UAE, Saudi Arabia, Turkey, and South Africa are the most advanced countries in the Middle East and Africa's geospatial analytics market. In 2018 Saudi Arabia dominated the geospatial analytics market, followed by Turkey.
GIS and RS technologies have been widely adopted in the UAE, especially in relation to the complex nature of tourism resources in the country. Existing geographic data system data layers have been used to recognize sensitive natural environments and archaeological heritage resources that are endangered because of rapid urbanization, protect them, and redirect growth in other areas.
In Saudi Arabia, IT companies depend on various data, such as social media data, text data for analytical purposes. Geospatial analytics has emerged as a vital data source for project planning and implementation, reducing workload, generating effective business forecasts, and updating sustainable strategies.
Regarding South Africa, looking at the economic growth of the region and the situation of the government (with GDP increasing from 4.7% to 5.2% in the last years and FDI growth of 16%, reaching $43 billion), the market is expected to grow at the highest CAGR during the next five years.
The geospatial analytics market in Latin America is expected to grow in revenue and expand at a CAGR of 17.31% during the forecast years 2019-2027.
The location intelligence market in Latin America supports government policies, investments, and funding from the national governments in this region.
Brazil has the largest manufacturing sector in Latin America. Moreover, increasing the retail market with growing economic conditions leads to a consequent increased demand for geospatial analytics services in the country.
We highlighted the following companies looking at the South American market: Cognatis and Spectro Location Intelligence (Brazil) and Geoint (Ecuador).
This week we analyzed the Asia-Pacific (APAC) region of the Location Intelligence market.
A new report published by KBV Research shows that the APAC Location Analytics market is expected to reach a market size of USD 6.1 billion by 2022, growing at a CAGR of 18.4% during the forecast period. Market expansion will be driven by small and medium-sized enterprises that will increase the adoption of location data analytic services.
In APAC, the market of geospatial analytics is led by China, Japan, India, South Korea, Singapore, and Malaysia. GeoCTRL visual map highlights remarkable location analytics companies from China, Australia, New Zeeland, and Singapore.
We can segment the APAC market into different categories. Based on application: Risk Management, Customer Experience Management, Sales & Marketing Optimization, Emergency Response Management, Predictive Assets Management, Inventory Management, Supply Chain Planning & Optimization, Remote Monitoring, and others.
Based on location type, there are two mains categories: Indoor and Outdoor.
Based on vertical, the APAC market is segmented into Telecom & IT, BFSI, Retail, Government and Defense, Healthcare, Transportation and Logistics, Energy & Utilities, Media & Entertainment, and others.
Based on component type, it is divided into Software (Geocoding and Reverse Geocoding, Data Integration and Extraction, Transformation and Loading, Reporting and Visualization, Thematic Mapping and Spatial Analysis, etc.) and Services (System Integration and Deployment, Consulting, Data maintenance, and creation).
Considering the current landscape, it is certain that the booming of smart devices and investments in IoT technologies are the factors that will drive the APAC Location Analytics Market expansion for the coming years. However, some factors such as lack of network infrastructure, data privacy concerns, lack of awareness, and other operational challenges and risks could delay that growth.
The leading countries in the European Location Intelligence market are UK, France, Germany, Spain, and Italy.
The rapid expansion in business intelligence analytics and geographic information systems technology is partly due to the growth in the retail industry and partly due to the increasing use of smartphones combined with other advanced tools. However, the market is constantly evolving to meet and satisfy customer needs.
Today, many industries use location intelligence solutions to optimize their sales & marketing activities, customer relationship management, risk management, emergency response management, supply chain planning, inventory management, etc.
Based on verticals, the market can be segmented into BFSI, Retail, Manufacturing, Government & Defense, Telecom & IT, Healthcare, Transportation & Logistics, Media & Entertainment, Energy & Utilities, and others. The location intelligence map features both indoor and outdoor location services and solutions
Most of the featured companies offer to help achieving similar business goals but using different solutions. The fundamental difference is in the data sources of such solutions. They vary from mobile network data, GPS systems, Bluetooth and Wi-fi connections, app network exchanges, and even mobility providers.
For more insights check out the Worldwide Location Intelligence Map here.
Download a high-resolution map