In previous articles, we discussed how to maximize the benefits of using high-quality databases. However, if you don’t have your own address or location data, but you still want to develop your business by conducting spatial analyses and making accurate business decisions based on geographical criteria – nothing lost!
AlgoMaps provides a wide range of spatial data concerning all apartments, buildings, postal codes, towns, and streets.
AlgoMaps provides detailed address databases of buildings and apartments. In addition to a complete standardized address, the application provides geographic coordinates – which in turn makes them successfully analyzable using Location Intelligence technology. AlgoMaps is a complete collection of other location data as well, including information about:
In the case of apartments, the database also contains data on the number of inhabitants. In the case of buildings – all the information we already wrote about in the previous articles of the New Data Dimension series. Additionally, the data on buildings is enriched with information from the Broadband Internet Access market, including:
Data from the database of buildings and apartments can therefore be filtered according to specific parameters, including location. The filters can be used depending on your business needs. Examples?
What is important is that the data made available as part of AlgoMaps does not contain any personal data (allowing for the identification of a specific person). Using them – you don’t have to worry about GDPR!
As you can see from the examples above, many business decisions will be made using zip code information as well. This is the case, for example, in insurance companies, where the postal code (and, for example, the permanent place where the car is parked) plays a significant role in the calculated motor insurance premium. Also, in trade – distribution ranges are most often determined precisely based on the postal code. The same situation occurs in logistics – the assignment of delivery areas to transshipment terminals is made on the same basis.
However, it should be noted that storing the postal code in the database is not sufficient to analyze important business phenomena in geographical terms. It is possible only when we add spatial data in a form of an area that covers a given postal code to textual information (number of characters that make up a postal code). This is where AlgoMaps comes in, providing not only maps of postal codes but also areas of districts, zones, and code sectors. It is important to note that this data can be supplemented with a range of economic and demographic information, analogous to the data on buildings and dwellings. This makes it so that models and processes that have worked before do not need to be adapted to the new type of data (e.g., address points), but can be supplemented with additional information with little time and effort. An example of zip code data is shown in the figure below.
Map of postal codes made with AlgoMaps data
If you intend to conduct analyses using Location Intelligence, but the level of individual apartments and buildings is too detailed, AlgoMaps is also a source of data aggregated to the street, township, or postal code level. The advantage of this database is the standardized and, above all, up-to-date names of address components: including names of provinces, counties, municipalities, and unique identifiers from the TERYT registry. This data, at every possible level of aggregation, can be supplemented with demographic data, data about the character of the area, and an indication of the location potential of any business venture.
Data in spatial form facilitates, among other things:
AlgoMaps data can be used to make a wide range of business decisions – from day-to-day operational activities to decisions with long-term lead times. An example is identifying the optimal location for a new outlet or partner to distribute our products. If we know what the target group of our products is, and then we put additional filters on this information (for example, the distance of this place from the warehouse), using LI technology we can easily determine a new location that will be the most profitable. We can also take the opposite approach to assess whether current locations are appropriate. AlgoMaps data also allows us to answer the question: how many people from the target group of my business live within a given distance from my establishment?
Another application of this data is direct marketing campaigns. AlgoMaps data allows you to answer the question: where does my potential customer live? If you are, for example, an Internet provider, the market data are in our resource. Based on the data, you can assess the nature of the local market and the competition, and then decide to expand your network by specific address points – selected, of course, in terms of the return on investment. If you operate in the mortgage or insurance market, AlgoMaps will help you locate, for example, a new housing estate where people with mortgages live. The mortgage data is provided by one of our trusted partners.
…is easy. Building information can be downloaded for a selected spatial unit (province, county, municipality, zip code) through the AlgoMaps online application. Simply create a free account and then select the appropriate parameters from the menu. The download process is described in detail in the documentation.
In case of more advanced filters, e.g., for targeting marketing campaigns, please contact us – together we will determine what you need and what data we can provide you with.
Whether you are considering improving your scoring by adding a geographic criterion or planning to improve UX by autocomplete web forms – the AlgoMaps application provides a whole new dimension of data for your business. This article ends the series discussing the effective use of databases, but let the presented solutions become an incentive to support the development of your business using Location Intelligence technology.
If you have any questions, we can help – you can find us at firstname.lastname@example.org.
You can also take a look at a selected article from the New Data Dimension series at any time – all linked below.