Urban Analytics. Shared City Photos
We are back with another material about “Urban Analytics” module. This article will describe the “Shared City Photos” tool. It can be used to assess the attractiveness of the urban environment through the analysis of digital footprints, which are the geolocations of city street photos taken and posted on social networks.
How does it work?
The most beautiful and attractive space is the one with most photos.
Separation of street photos from those taken indoors is done by a specially developed neural network that classifies them and adds only those photos to the map that were taken on the street.
There are two parts to the “Urban Analytics” module. The first is that each image is translated to a point, and the coordinates are assigned to each point based on geolocation, allows users to directly accumulate data and display it on a map.
The second is that the date of the photo is used for building line graphs and visualizing trends in photo frequency.
Map with geolocation points itself is enough to say a lot about the city and
the popularity of urban public spaces. To a certain extent, it is like a tourist map of the city. With this map, users can come up with tourist routes and support their feasibility with the data represented in the map layer. Another use for the data is for planning continuous sidewalks, and assessing the level of events and other activity. The use cases of “Shared City Photos” tool are not limited to displaying coordinates of city street photos posted on social networks. Explore further uses:
- Run calculation and get as a result the density map of city street photos over intervals of time, identifying hotspots for photo activity and giving it a quantitative appraisal.
- Build line graphs of street photo frequency over intervals of time to visualize peaks of photo activity, seasonality and other trends. To make a good assessment of a digital footprint, knowing distribution of photos over certain area is not enough. How it changes over time is important too, so the tool lets users plot the number of photos on the timeline for the whole city or a certain territory.
- Outline the target territory for analysis and build a photo activity line graph.
- Visualize the trends in changes in the number of photos and compare how many were posted online in a single city area for the two specified time periods.
- Estimate the dynamics of public spaces popularity over time.
The tool is implemented in Kazan as part of Complex Municipal Geographic Information System. Consider some of its real-life usage examples:
Example No. 1.
According to the graph below, just a couple years ago, the period of most photographic activity were the New Year holidays, peaking during the period from 25th of December, 2019 to 10th of January, 2020. Yet the overall tendency is the decrease of the number of New Year’s street photos. This year, most of them were taken around the time of City Anniversary Day or “Sabantuy” summer fest, and their number exceeded the ones taken during winter.
It is fair to say that the tool helps evaluate the attractiveness and popularity of certain urban spaces in absolute numerical values.
Example No. 2.
Restricting search area to a popular family centre called “Kazan” shows how New Year holidays cause surge in photo activity. Yet search in the same area for different time period revealed that people paid more attention to this spot on the Independence Day than around the New Year’s eve.
Example No. 3.
Since 2018, the Kaban Lake embankment has been undergoing renovation. The project turned out to be very successful and is widely known among architects, judging by how many times it was tagged online and mentioned in posts. The place has become more popular among city tourists and residents too, as evidenced by the street photos dynamics. Before 2018, pictures were made only 11 times along the entire stretch of the embankment. After the 1st stage of the project was delivered, the number of photos increased to 1000. In 2019, we count 1,350 photos. This trend continues each year except for the year 2020 which was affected by Covid-19. It is interesting how the location of the photos themselves changes as the renovated territory expands.
Example No. 4.
In October 2019 the capital of Tatarstan hosted World Urban Parks Congress, and the Kazan forest park «Swan Harbour» was nominated for the International Urban Parks Award in the Eurasian Park Awards category. The landscaping of the Swan Lakes is in fact only the final touch of a large renovation project. Once this wonderful gift of nature got lost, and now it is reclaimed, although at a considerable cost. The renovation was completed in 2018. The bottom of the lakes was restored, as well as the communication with the Emerald Lake. The animation shows how the number and location of photographs change as the pieces of work are completed and the facilities are put into operation.
This is the way for the city to give feedback, and Geometa and the “Shared City Photos” tool allow you to collect it and translate into measurable data. Thanks to this, city administrations have now the power to:
— evaluate and measure the success of renovation accomplishments;
— plan the opening of a new public space where such a space is clearly lacking, like in residential areas, for example;
— transform the existing public space if the results of shared photos analytics say that people do not consider it beautiful enough, photo-worthy, or due to a simple reason that it is rarely visited.
For the information about all the capabilities of Geometa please read the “Solutions” section.
Urban Analytics. Number of Residents
We are back with the series of articles about the “Urban analytics” module. The topics of our last posts were “Private cars map” and “Road accidents” tools.
Today’s article is about a tool that helps distribute city benefits among its residents. When managing the development of a city, it is important to have demographic data available. Urban planners need to know the city’s population density, as well as the number of inhabitants in each house or apartment building.
Getting hold of this information can solve some city-wide issues like designing the best possible layout of public transport, grid street plans, landscaping of parks, squares and avenues, building of educational, sports and cultural facilities. Local-scale issues are also easier to solve as this information can guide the placement of household waste containers or relocation of parking spots.
The tool we developed allows visualization of the number of inhabitants in each residential building on a map. The data originates from a variety of sources. Most open to the public is the data coming from “Housing and Utilities Reform” automated system. Data resulting from calculations is also considered open source.
Automated system “Housing and Utilities Reform” is a system used by the employees of property management companies, Housing Reform Fund, local governments and state authorities, as well as the State Housing Inspection. The system holds data on the number of people living in all apartment buildings in the city.
There are regular updates of data stored in the “Housing and Utilities Reform” automated system. When data in the “Housing and Utilities Reform” system is brought up to date and imported into our system, map layer showing number of city residents is synchronized with it.
The tool generates estimated number of residents directly in the Geometa system. This estimate derives from data on buildings and structures, as well as statistical data: the average living space per person and the average family size for a certain city area. So we derived the number of residents in apartment buildings from the total building area, and in case of private housing buildings — the average family size, based on the assumption that only one family lives in one private housing building.
You can get a general idea of how densely the city is populated in different areas from reading the medium-scale map.
It makes sense to combine both sources. This means using the data of the “Housing and Communal Services Reform” for multi-apartment buildings, but taking calculated values of the average family size for private housing buildings. This way you will get more reliable information, since it’s common for luxury housing to provide more square meters per person than economy class apartments. Typically, data on housing classes in municipalities is not maintained, so the calculated values for some houses will differ significantly from the real situation.
Next article will be about “Availability of social facilities” tool.
For the information about all the capabilities of Geometa please read the “Solutions” section.
Urban Аnalytics. Private Cars Map
The topic of our last posts was “Urban Analytics module” and the tools it includes. One of the tools is “Road accidents” for revealing the road accidents hotspots.
Now let us introduce “Private cars map” tool. It is characterized by its main purpose: visualization of car density and their location in the city. Knowing the detailed information of this kind can be useful in many processes like planning the layout of parking spots or evaluating car-dependency level of the city.
When a polygonal object representing real-life objects like green spaces or lawns is stored in the system, it is possible to reveal areas where lawn parking violations take place.
But such information is not easy to obtain. The proposed method for solving the problem is based on computer vision technology. The neural network takes orthophotomaps or high-resolution satellite images and distinguishes vehicles on it, classifying each as a car or a truck. A centroid is then assigned to each vehicle as its representation in the system.
As a result, thematic data layer is created that can be interpreted by GIS tools. Use cases include comparing with other objects of the system to solve real-life issues.
The accumulation of historical data in the system with new relevant orthophotomaps becoming available will help to see the dynamics of the situation.
The tool also allows you to calculate the density of cars in each point of the city and display the results of the calculation on the map. Following map shows distribution of cars and cars clusters around the city.
Another developed feature is identification of residential areas with overcrowded parking lots.
Announcing the topic of next time: the “Number of residents” tool.
You can read about all the features of the Urban Analytics module in our article.
Urban Аnalytics. Road Accidents
This year, we launched the Urban Analytics module, which helps make decisions about the future of the urban environment using measurable data. The module has six tools in total, and today we highlight the one that will save lives, it is called “Road accidents”.
Reducing road traffic fatalities, as well as reducing accident-prone sections on the road network, are important national objectives. They are declared among the priority goals of the Safe and High-Quality Roads project in Russia, which is aimed at creating a comfortable and safe living environment.
We rose to the challenge and we made a cartographic analytical service that would allow us to visualize road accidents, their hotspot locations, the causes and dynamics of their changes over time. This information will help to prioritize when working on the development or reconstruction of the road network.
Another benefit is that it gives valuable insights on the measures taken to improve traffic safety. Imagine city management puts a new road sign, traffic light or a pedestrian island by the road, or maybe reconstructs the road itself. Find out if these measures took the expected effect and reduced the number of accidents
With the help of the “Road accidents” tool, you can easily find the most problematic sections of the city’s road network in terms of safety, focus on them and find effective solutions.
What features go with the tool?
User can choose object and display its record with attributes of each accident: the date of the accident, the category of the accident, the number of injured, the number of deaths, the severity of the accident, the weather conditions at the time of the accident, or whether pedestrians were involved. Filter can be applied by any of listed attributes to show only filtered accidents on the map:
With the help of the tool, it is possible to find the hotspots of accidents for a given period of time (orange symbols) and accidents involving pedestrians (dark gray symbols). The map shows the proportion of accidents involving pedestrians in each of the accident hotspot:
The tool also highlights the concentration of accidents in terms of severity and harm to health: bright pink symbols show severe accidents. Here you can also see the proportion of severe accidents in relation to minor ones:
Another feature is a comparison map showing change in the number of accidents relative to the same time of year in the past. The user provides input of two time periods, and the system shows if number of accidents has increased or decreased in each part of the city. The size of the arrows is proportional to the number of accidents:
Thus, the “Road accidents” tool makes it possible to find out details about specific accidents, their concentration in different parts of the city and compare data with the same time of the year in the past. In the next article, we will talk about the “Private cars density” tool, which helps our users get information about how cars are distrubuted around the city.
You can read about all the features of the Urban Analytics module in our article.
Creation of the Information System for Management of a Million-plus City
The integrated municipal geoinformation system of Kazan city, created on the basis of Geometa, is designed to solve the city problems associated with the processing of spatial data. This is a single source of comprehensive information about the territory.
The system allows the leadership of Kazan city in a convenient and understandable form to receive up-to-date information on the state of the urban environment and on the administration of land resources, visualize the city needs, help to make decisions on infrastructure development and to determine the necessary amount of funding for municipal programs and city projects.
The system is used by investors interested in business development in Kazan. They receive legitimate information about the possibility of implementing their projects. Thus the system contributes to the investment attractiveness of the city.
Moreover, the system contributes to the reduction of mandatory procedures terms in construction, helps to synchronize the plans of conducted works, which leads to savings in investment and budgetary funds. Construction is a high-budget area of activity where the optimization of resources is extremely important.
The system contains full functionality to support urban planning activities: characteristics of all capital construction projects, documents, data on urban planning, zoning, information on land parcels and land rights. To date, 100% of municipal services related to construction are automated through the system.
The development of the system continues, its functionality is expanded by analytical tools that visualize various aspects of managing the processes of city territory modernization. Data on the population number and density is compared with data on the location of social, recreational, and transport infrastructure facilities to identify the points of greatest unmet demand for urban facilities.
Calculable, quantitative indicators allow to reduce managerial errors since decisions are made based on data, not just on experts’ opinion.
In a city with a population of one million, there are a huge number of control objects, they are interdependent. So the effective city management is an extremely non-trivial task. Modern cities compete with one another to attract the best minds, talents, businesses and capital, and it will not be possible to win the competitive race without the help of advanced technologies.