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Spatial systems
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<a id="navigation-link" href="/postnet.html">РУ</a>
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<div class="fullpage">
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<div class="section gradient-text">
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<p>Postnet – geoinformation AutoML system devoted to development and operation monitoring of Moscow
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automatic post office chain
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</p>
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<p>Moscow automatic post office chain is now being developed by Moscow city administration. Automatic post
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offices are placed not only in various infrastructure facilities, but also right inside the entrances of
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residential buildings. Now, by using automatic post offices, you can receive orders from online stores
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and marketplaces, and other services will be available soon. To monitor operations of the chain and its
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development, our team has developed the Postnet system</p>
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</div>
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<div class="section">
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<figure>
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<img src="/images/postnet/cover.jpg" />
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<figcaption>System interface</figcaption>
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</figure>
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</div>
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<div class="section">
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<p>The forecast for number of sales in all potential locations for object placement is based on the data
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collected from already operating retail chain outlets, more than 50 factors influencing the city space
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and algorithms of machine learning (more than 100 000 locations throughout the city).</p>
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<p>Once a day our system gets updated data on total sales from all operating points of sales. Due to the
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application of automated machine learning, the model undergoes additional training every day with
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consideration of the updated data, becoming more accurate and remaining relevant over time.</p>
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<p>Beside existing location points in the system, you can import your own into it - the system will
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automatically collect the necessary data on the loaded location points, implement the model and give a
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forecast for the number of orders.</p>
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</div>
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<div class="section">
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<figure>
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<img src="/images/postnet/location.jpg" />
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<figcaption>The projected monthly number of sales in the selected location is 257</figcaption>
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</figure>
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</div>
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<div class="section">
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<p>In forecasting, we use complex models that allow, on the one hand, to identify non-linear patterns
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between the value of the number of sales and many factors affecting it, and on the other, to interpret
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this pattern in a way that is clear to the user.
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</p>
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<p>
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So, the forecast at each location can be decomposed into contribution level of all factors involved in
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the model and understand better which features of the surrounding space turn out to be the most
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significant - which increase the number of orders, and which, on the contrary, decrease it.
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</p>
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</div>
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<div class="section">
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<figure>
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<img src="/images/postnet/diagram.jpg" />
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<figcaption>Interpretation of the prognosis – visualization of the contribution of various factors to
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the order quantity forecast
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</figcaption>
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</figure>
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</div>
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<div class="section">
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<p>The system interface gives an opportunity for the user to select any location from the entire set to
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simulate the placement of new point of sales in the chain. In the process of such an online simulation,
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the model will automatically reassemble all data and recalculate forecasts taking into account that new
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automatic post offices will appear in the selected ones.
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</p>
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<p>If the automatic post office is successfully installed, this location will begin to accumulate data on
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the number of orders and after a certain time will be included in the training set.</p>
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</div>
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<div class="section">
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<p>The system is equipped with many filters that simplify the task of finding locations for placing new
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automatic post offices. With the help of filters, you can quickly select locations with a certain
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forecast number of sales located in certain areas of the city and in objects of a certain category
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(supermarket, residential building entrance, library, etc.).
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</p>
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</div>
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<div class="section">
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<figure>
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<img src="/images/postnet/konkovo.jpg" />
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<figcaption>Living residencies and retail district Konkovo with a monthly order quantity forecast of
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more than 200
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</figcaption>
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</figure>
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</div>
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<div class="section">
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<p>Using advanced filters, you can select locations depending on their position relative to infrastructure
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objects, for example, locations of other automatic post offices/pick up points or having certain
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statistics on the neighborhood, for example, on the number of apartments.</p>
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</div>
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<div class="section">
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<figure>
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<img src="/images/postnet/zones.jpg" />
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<figcaption>Locations positioned in areas with low competition, but with a large number of people
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</figcaption>
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</figure>
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</div>
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<div class="section">
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<p class="gradient-text">All this makes the developed system an innovative and convenient tool for managing
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and developing the Moscow Automatic Post Office chain. In general, such an application can be used for
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any other chains of any commercial or socio-economic facilities with a certain number of already
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operating objects.
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</p>
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<a href="/en/contacts.html" class="contacts-link">Contact us if you are interested in a system like
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