Population: Female: NW: City of St Petersburg data was reported at 3,074,658.000 Person in 2023. This records an increase from the previous number of 3,074,589.000 Person for 2022. Population: Female: NW: City of St Petersburg data is updated yearly, averaging 2,701,170.000 Person from Dec 1989 (Median) to 2023, with 35 observations. The data reached an all-time high of 3,078,359.000 Person in 2021 and a record low of 2,563,704.000 Person in 2002. Population: Female: NW: City of St Petersburg data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Russia Premium Database’s Demographic and Labour Market – Table RU.GA010: Population: Female: by Region.

Population: Urban: NW: City of St Petersburg data was reported at 5,645,943.000 Person in 2024. This records an increase from the previous number of 5,597,763.000 Person for 2023. Population: Urban: NW: City of St Petersburg data is updated yearly, averaging 4,921,117.500 Person from Dec 1989 (Median) to 2024, with 36 observations. The data reached an all-time high of 5,645,943.000 Person in 2024 and a record low of 4,656,474.000 Person in 2002. Population: Urban: NW: City of St Petersburg data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Global Database’s Russian Federation – Table RU.GA011: Population: Urban: by Region.

Russia's capital, Moscow, was the largest city in the country with over **** million residents as of January 1, 2024. Less than a half of Moscow's population resided in Saint Petersburg, the second-most populous city in the country. The third-largest city, Novosibirsk, was located in the Siberian Federal District, being the highest-populated city in the Asian part of Russia. Why is Moscow so populated? The Russian capital is the center of political, industrial, business, and cultural life in Russia. Despite being one of the most expensive cities worldwide, it continues to attract people from Russia and abroad, with its resident population following a generally upward trend over the past decade. Wages in Moscow are higher than in Russia on average, and more opportunities for employment and investment are available in the capital. Furthermore, the number of people living in Moscow was forecast to continue rising, exceeding **** million by 2035. Urbanization in Russia In 2024, around *** million Russian residents lived in cities. That was approximately three-quarters of the country’s population. The urbanization rate increased steadily over the 20th century, leading to a decline in the rural population. Among the country’s regions, the Northwestern Federal District had the highest share of residents in urban areas, measured at ** percent. In the Central Federal District, the tendency was that more people moved to Moscow and cities in the Moscow Oblast.

Population: Male: NW: City of St Petersburg在2023达2,523,105.000人口，相较于2022的2,525,455.000人口有所下降。Population: Male: NW: City of St Petersburg数据按每年更新，1989至2023期间平均值为2,207,668.000人口，共35份观测结果。该数据的历史最高值出现于2021，达2,529,557.000人口，而历史最低值则出现于2003，为2,091,874.000人口。CEIC提供的Population: Male: NW: City of St Petersburg数据处于定期更新的状态，数据来源于Federal State Statistics Service，数据归类于Russia Premium Database的Demographic and Labour Market – Table RU.GA009: Population: Male: by Region。

Population: NW: St Petersburg City: Above Working Age data was reported at 1,424.600 Person th in 2019. This records a decrease from the previous number of 1,467.500 Person th for 2018. Population: NW: St Petersburg City: Above Working Age data is updated yearly, averaging 1,282.500 Person th from Dec 2003 (Median) to 2019, with 16 observations. The data reached an all-time high of 1,467.500 Person th in 2018 and a record low of 1,087.800 Person th in 2003. Population: NW: St Petersburg City: Above Working Age data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Russia Premium Database’s Demographic and Labour Market – Table RU.GA017: Population: by City: North Western Federal District.

In terms of the geographical spread of active Twitter users across Russia, Saint Petersburg was leading with 1.74 percent of its total residents as of November 2019. In aggregate terms, roughly 0.5 percent of the Russian population regularly posted on Twitter over the period under consideration.

In terms of the geographical spread of Vk.com active users across Russia, Saint Petersburg was leading with over half of its total residents being active users of named platform as of November 2019. In aggregate terms, roughly 20 percent of the Russian population regularly posted on Vk.com over the period under consideration.

3,103 (Thousand persons) in 2020.

Population: Working Age: Urban: NW: City of St Petersburg data was reported at 3,365,469.000 Person in 2022. This records an increase from the previous number of 3,292,937.000 Person for 2021. Population: Working Age: Urban: NW: City of St Petersburg data is updated yearly, averaging 2,999,320.000 Person from Dec 1989 (Median) to 2022, with 34 observations. The data reached an all-time high of 3,365,469.000 Person in 2022 and a record low of 2,824,544.000 Person in 1995. Population: Working Age: Urban: NW: City of St Petersburg data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Russia Premium Database’s Demographic and Labour Market – Table RU.GA013: Population: Working Age: by Region.

1117 Russian cities with city name, region, geographic coordinates and 2020 population estimate. How to use from pathlib import Path import requests import pandas as pd url = ("https://raw.githubusercontent.com/" "epogrebnyak/ru-cities/main/assets/towns.csv") # save file locally p = Path("towns.csv") if not p.exists(): content = requests.get(url).text p.write_text(content, encoding="utf-8") # read as dataframe df = pd.read_csv("towns.csv") print(df.sample(5)) Files: towns.csv - city information regions.csv - list of Russian Federation regions alt_city_names.json - alternative city names Сolumns (towns.csv): Basic info: city - city name (several cities have alternative names marked in alt_city_names.json) population - city population, thousand people, Rosstat estimate as of 1.1.2020 lat,lon - city geographic coordinates Region: region_name - subnational region (oblast, republic, krai or AO) region_iso_code - ISO 3166 code, eg RU-VLD federal_district, eg Центральный City codes: okato oktmo fias_id kladr_id Data sources City list and city population collected from Rosstat publication Регионы России. Основные социально-экономические показатели городов and parsed from publication Microsoft Word files. City list corresponds to this Wikipedia article. Alternative dataset is wiki-based Dadata city dataset (no population data). Comments City groups Ханты-Мансийский and Ямало-Ненецкий autonomous regions excluded to avoid duplication as parts of Тюменская область. Several notable towns are classified as administrative part of larger cities (Сестрорецк is a municpality at Saint-Petersburg, Щербинка part of Moscow). They are not and not reported in this dataset. By individual city Белоозерский not found in Rosstat publication, but should be considered a city as of 1.1.2020 Alternative city names We suppressed letter "ё" city columns in towns.csv - we have Орел, but not Орёл. This affected: Белоозёрский Королёв Ликино-Дулёво Озёры Щёлково Орёл Дмитриев and Дмитриев-Льговский are the same city. assets/alt_city_names.json contains these names. Tests poetry install poetry run python -m pytest How to replicate dataset 1. Base dataset Run: download data stro rar/get.sh convert Саратовская область.doc to docx run make.py Creates: _towns.csv assets/regions.csv 2. API calls Note: do not attempt if you do not have to - this runs a while and loads third-party API access. You have the resulting files in repo, so probably does not need to these scripts. Run: cd geocoding run coord_dadata.py (needs token) run coord_osm.py Creates: coord_dadata.csv coord_osm.csv 3. Merge data Run: run merge.py Creates: assets/towns.csv See code at Github: https://github.com/epogrebnyak/ru-cities

Moscow had nearly ** CCTV cameras per 1,000 inhabitants in 2023. In total, ******* such devices were recorded in the Russian capital. The second-largest city of the country, Saint Petersburg, recorded a density of approximately **** surveillance cameras per 1,000 population.

Population: NW: St Petersburg City: Non Working Age: Age 0 to 15 data was reported at 864.800 Person th in 2019. This records an increase from the previous number of 846.200 Person th for 2018. Population: NW: St Petersburg City: Non Working Age: Age 0 to 15 data is updated yearly, averaging 627.600 Person th from Dec 2003 (Median) to 2019, with 16 observations. The data reached an all-time high of 864.800 Person th in 2019 and a record low of 563.000 Person th in 2007. Population: NW: St Petersburg City: Non Working Age: Age 0 to 15 data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Russia Premium Database’s Demographic and Labour Market – Table RU.GA017: Population: by City: North Western Federal District.

Abstract

The Russia Longitudinal Monitoring Survey (RLMS) is a household-based survey designed to measure the effects of Russian reforms on the economic well-being of households and individuals. In particular, determining the impact of reforms on household consumption and individual health is essential, as most of the subsidies provided to protect food production and health care have been or will be reduced, eliminated, or at least dramatically changed. These effects are measured by a variety of means: detailed monitoring of individuals' health status and dietary intake, precise measurement of household-level expenditures and service utilization, and collection of relevant community-level data, including region-specific prices and community infrastructure data. Data have been collected since 1992.

The repeated cross-section design is far and away the simplest alternative for the RLMS. The sampling is cost efficient, easy to maintain, and easy to update when needed. The design supports both efficient cross-sectional and aggregate longitudinal analyses of change in the Russian household population. Updates to the sample, including a full replenishment of the probability sample of dwelling units, will not seriously disrupt the longitudinal data series.

Geographic coverage

National

Analysis unit

Households and individuals.

Kind of data

Sample survey data [ssd]

Sampling procedure

In Phase II (Rounds V - XX) of the RLMS, a multi-stage probability sample was employed. Please refer to the March 1997 review of the Phase II sample. First, a list of 2,029 consolidated regions was created to serve as PSUs. These were allocated into 38 strata based largely on geographical factors and level of urbanization but also based on ethnicity where there was salient variability. As in many national surveys involving face-to-face interviews, some remote areas were eliminated to contain costs; also, Chechnya was eliminated because of armed conflict. From among the remaining 1,850 regions (containing 95.6 percent of the population), three very large population units were selected with certainty: Moscow city, Moscow Oblast, and St. Petersburg city constituted self-representing (SR) strata. The remaining non-self-representing regions (NSR) were allocated to 35 equal-sized strata. One region was then selected from each NSR stratum using the method "probability proportional to size" (PPS). That is, the probability that a region in a given NSR stratum was selected was directly proportional to its measure of population size.

The NSR strata were designed to have approximately equal sizes to improve the efficiency of estimates. The target population (omitting the deliberate exclusions described above) totaled over 140 million inhabitants. Ideally, one would use the population of eligible households, not the population of individuals. As is often the case, we were obliged to use figures on the population of individuals as a surrogate because of the unavailability of household figures in various regions.

Although the target sample size was set at 4,000, the number of households drawn into the sample was inflated to 4,718 to allow for a nonresponse rate of approximately 15 percent. The number of households drawn from each of the NSR strata was approximately equal (averaging 108), since the strata were of approximately equal size and PPS was employed to draw the PSUs in each one. However, because response rates were expected to be higher in urban areas than in rural areas, the extent of over-sampling varied. This variation accounted for the differences in households drawn across the NSR PSUs. It also accounted for the fact that 940 households were drawn in the three SR strata--more than the 14.6 percent (i.e. 689) that would have been allotted based on strict proportionality.

Since there was no consolidated list of households or dwellings in any of the 38 selected PSUs, an intermediate stage of selection was then introduced, as usual. Professional samplers will recognize that this is actually the first stage of selection in the three SR strata, since those units were selected with certainty. That is, technically, in Moscow, St. Petersburg, and Moscow oblast, the census enumeration districts were the PSUs. However, it was cumbersome to keep making this distinction throughout the description, and researchers followed the normal practice of using the terms "PSU" and "SSU" loosely. Needless to say, in the calculation of design effects, where the distinction is critical, the proper distinction was maintained. The selection of second-stage units (SSUs) differed depending on whether the population was urban (located in cities and "villages of the city type," known as "PGTs") or rural (located in villages). That is, within each selected PSU the population was stratified into urban and rural substrata, and the target sample size was allocated proportionately to the two substrata. For example, if 40 percent of the population in a given region was rural, 40 of the 100 households allotted to the stratum were drawn from villages.

In rural areas of the selected PSUs, a list of all villages was compiled to serve as SSUs. The list was ordered by size and (where salient) by ethnic composition. PPS was employed to select one village for each 10 households allocated to the rural substratum. Again, under the standard principles of PPS, once the required number of villages was selected, an equal number of households in the sample (10) were allocated to each village. Since villages maintain very reliable lists of households, in each selected village the 10 households were selected systematically from the household list. In a few cases, villages were judged to be too small to sustain independent interviews with 10 households; in such cases, three or four tiny villages were treated as a single SSU for sampling purposes.

In urban areas, SSUs were defined by the boundaries of 1989 census enumeration districts, if possible. If the necessary information was not available, 1994 microcensus enumeration districts, voting districts, or residential postal zones were employed--in decreasing order of preference. Since census enumeration districts were originally designed to be roughly equal in population size, one district was selected systematically without using PPS for each 10 households required in the sample. In the few cases where postal zones were used, one zone was likewise selected systematically for each 10 households. However, where voting districts were used, to compensate for the marked variation in population size, PPS was employed to select one voting district for each 10 households required in the urban sub-stratum.

In both urban and rural substrata, interviewers were required to visit each selected dwelling up to three times to secure the interviews. They were not allowed to make substitutions of any sort. The interviewers' first task was to identify households at the designated dwellings. "Household" was defined as a group of people who live together in a given domicile, and who share common income and expenditures. Households were also defined to include unmarried children, 18 years of age or younger, who were temporarily residing outside the domicile at the time of the survey. If perchance the interviewer identified more than one household in the dwelling, he or she was obliged to select one using a procedure outlined in the technical report. The interviewer then administered a household questionnaire to the most knowledgeable and willing member of the household.

The interviewer then conducted interviews with as many adults as possible, acquiring data about their individual activities and health. Data for the children's questionnaires were obtained from adults in the household. By virtue of the fact that an attempt was made to obtain individual questionnaires for all members of households, the sample constitutes a proper probability sample of individuals as well as of households, without any special weighting. Actually, the fact that we did not interview unmarried minors living temporarily outside the domicile slightly diminished the representativeness of the sample of individuals in that age group.

The multivariate distribution of the sample by sex, age, and urban-rural location compared quite well with the corresponding multivariate distribution of the 1989 census. Of course, because of random sampling error and changes in the distribution since the 1989 census, we did not expect perfect correspondence. Nevertheless, there was usually a difference of only one percentage point or less between the two distributions.

Another way to evaluate the adequacy (or efficiency) of the sample was to examine design effects. An important factor in determining the precision of estimates in multi-stage samples was the mean ultimate cluster (PSU) size. All else being equal, the larger the size the less precise the measure is. In Rounds I through IV of the RLMS, the average cluster size approached 360--a large number dictated by constraints imposed by our collaborators. Thus, although the sample size covered around 6,000 households, precision was less than we would have liked for a sample of that size. In Rounds I and III of the RLMS, the 95 percent confidence interval for household income was about ?±13 percent.

In the Phase II (Rounds V - XX) sample, the situation was considerably better. Although there were only 4,000 households, the mean size of clusters was much smaller than in Phase I. There were 35 PSUs with about 100 households each; even this result was an improvement over the average of 360 in the design of the RLMS Rounds I through IV. However, in the three self-representing areas, the respondents were drawn from 61 PSUs. Recall that Moscow city and oblast, as well as St. Petersburg

Approximately 6.9 million people received at least one dose of COVID-19 vaccine in Moscow as of January 23, 2022. The Russian capital also had the highest number of COVID-19 cases nationwide. The second- and third-leading regions by vaccinated population were the Moscow Oblast and Saint Petersburg, respectively.

Abstract

The Russia Longitudinal Monitoring Survey (RLMS) is a household-based survey designed to measure the effects of Russian reforms on the economic well-being of households and individuals. In particular, determining the impact of reforms on household consumption and individual health is essential, as most of the subsidies provided to protect food production and health care have been or will be reduced, eliminated, or at least dramatically changed. These effects are measured by a variety of means: detailed monitoring of individuals' health status and dietary intake, precise measurement of household-level expenditures and service utilization, and collection of relevant community-level data, including region-specific prices and community infrastructure data. Data have been collected since 1992.

Geographic coverage

National

Analysis unit

Households and individuals.

Kind of data

Sample survey data [ssd]

Sampling procedure

In Phase II (Rounds V - XX) of the RLMS, a multi-stage probability sample was employed. Please refer to the March 1997 review of the Phase II sample. First, a list of 2,029 consolidated regions was created to serve as PSUs. These were allocated into 38 strata based largely on geographical factors and level of urbanization but also based on ethnicity where there was salient variability. As in many national surveys involving face-to-face interviews, some remote areas were eliminated to contain costs; also, Chechnya was eliminated because of armed conflict. From among the remaining 1,850 regions (containing 95.6 percent of the population), three very large population units were selected with certainty: Moscow city, Moscow Oblast, and St. Petersburg city constituted self-representing (SR) strata. The remaining non-self-representing regions (NSR) were allocated to 35 equal-sized strata. One region was then selected from each NSR stratum using the method "probability proportional to size" (PPS). That is, the probability that a region in a given NSR stratum was selected was directly proportional to its measure of population size.

The NSR strata were designed to have approximately equal sizes to improve the efficiency of estimates. The target population (omitting the deliberate exclusions described above) totaled over 140 million inhabitants. Ideally, one would use the population of eligible households, not the population of individuals. As is often the case, we were obliged to use figures on the population of individuals as a surrogate because of the unavailability of household figures in various regions.

Although the target sample size was set at 4,000, the number of households drawn into the sample was inflated to 4,718 to allow for a nonresponse rate of approximately 15 percent. The number of households drawn from each of the NSR strata was approximately equal (averaging 108), since the strata were of approximately equal size and PPS was employed to draw the PSUs in each one. However, because response rates were expected to be higher in urban areas than in rural areas, the extent of over-sampling varied. This variation accounted for the differences in households drawn across the NSR PSUs. It also accounted for the fact that 940 households were drawn in the three SR strata--more than the 14.6 percent (i.e. 689) that would have been allotted based on strict proportionality.

Since there was no consolidated list of households or dwellings in any of the 38 selected PSUs, an intermediate stage of selection was then introduced, as usual. Professional samplers will recognize that this is actually the first stage of selection in the three SR strata, since those units were selected with certainty. That is, technically, in Moscow, St. Petersburg, and Moscow oblast, the census enumeration districts were the PSUs. However, it was cumbersome to keep making this distinction throughout the description, and researchers followed the normal practice of using the terms "PSU" and "SSU" loosely. Needless to say, in the calculation of design effects, where the distinction is critical, the proper distinction was maintained. The selection of second-stage units (SSUs) differed depending on whether the population was urban (located in cities and "villages of the city type," known as "PGTs") or rural (located in villages). That is, within each selected PSU the population was stratified into urban and rural substrata, and the target sample size was allocated proportionately to the two substrata. For example, if 40 percent of the population in a given region was rural, 40 of the 100 households allotted to the stratum were drawn from villages.

In rural areas of the selected PSUs, a list of all villages was compiled to serve as SSUs. The list was ordered by size and (where salient) by ethnic composition. PPS was employed to select one village for each 10 households allocated to the rural substratum. Again, under the standard principles of PPS, once the required number of villages was selected, an equal number of households in the sample (10) were allocated to each village. Since villages maintain very reliable lists of households, in each selected village the 10 households were selected systematically from the household list. In a few cases, villages were judged to be too small to sustain independent interviews with 10 households; in such cases, three or four tiny villages were treated as a single SSU for sampling purposes.

In urban areas, SSUs were defined by the boundaries of 1989 census enumeration districts, if possible. If the necessary information was not available, 1994 microcensus enumeration districts, voting districts, or residential postal zones were employed--in decreasing order of preference. Since census enumeration districts were originally designed to be roughly equal in population size, one district was selected systematically without using PPS for each 10 households required in the sample. In the few cases where postal zones were used, one zone was likewise selected systematically for each 10 households. However, where voting districts were used, to compensate for the marked variation in population size, PPS was employed to select one voting district for each 10 households required in the urban sub-stratum.

In both urban and rural substrata, interviewers were required to visit each selected dwelling up to three times to secure the interviews. They were not allowed to make substitutions of any sort. The interviewers' first task was to identify households at the designated dwellings. "Household" was defined as a group of people who live together in a given domicile, and who share common income and expenditures. Households were also defined to include unmarried children, 18 years of age or younger, who were temporarily residing outside the domicile at the time of the survey. If perchance the interviewer identified more than one household in the dwelling, he or she was obliged to select one using a procedure outlined in the technical report. The interviewer then administered a household questionnaire to the most knowledgeable and willing member of the household.

The interviewer then conducted interviews with as many adults as possible, acquiring data about their individual activities and health. Data for the children's questionnaires were obtained from adults in the household. By virtue of the fact that an attempt was made to obtain individual questionnaires for all members of households, the sample constitutes a proper probability sample of individuals as well as of households, without any special weighting. Actually, the fact that we did not interview unmarried minors living temporarily outside the domicile slightly diminished the representativeness of the sample of individuals in that age group.

The multivariate distribution of the sample by sex, age, and urban-rural location compared quite well with the corresponding multivariate distribution of the 1989 census. Of course, because of random sampling error and changes in the distribution since the 1989 census, we did not expect perfect correspondence. Nevertheless, there was usually a difference of only one percentage point or less between the two distributions.

Another way to evaluate the adequacy (or efficiency) of the sample was to examine design effects. An important factor in determining the precision of estimates in multi-stage samples was the mean ultimate cluster (PSU) size. All else being equal, the larger the size the less precise the measure is. In Rounds I through IV of the RLMS, the average cluster size approached 360--a large number dictated by constraints imposed by our collaborators. Thus, although the sample size covered around 6,000 households, precision was less than we would have liked for a sample of that size. In Rounds I and III of the RLMS, the 95 percent confidence interval for household income was about ?±13 percent.

In the Phase II (Rounds V - XX) sample, the situation was considerably better. Although there were only 4,000 households, the mean size of clusters was much smaller than in Phase I. There were 35 PSUs with about 100 households each; even this result was an improvement over the average of 360 in the design of the RLMS Rounds I through IV. However, in the three self-representing areas, the respondents were drawn from 61 PSUs. Recall that Moscow city and oblast, as well as St. Petersburg city, were not sampled but were chosen with certainty. Therefore, the first stage of selection in them was the selection of census enumeration districts. Thus the mean cluster size in the entire sample was about 42, i.e., 4,000/(35+61). Given these much smaller cluster sizes, researchers had reason to expect that precision in this survey would be as good as it was in Rounds I through IV despite the smaller sample size, and this expectation, in fact, turned out

Population: Internet Use: Orders of Goods & Services: % of Total: NW: City of St Petersburg data was reported at 73.600 % in 2024. This records an increase from the previous number of 70.300 % for 2023. Population: Internet Use: Orders of Goods & Services: % of Total: NW: City of St Petersburg data is updated yearly, averaging 47.300 % from Dec 2014 (Median) to 2024, with 11 observations. The data reached an all-time high of 73.600 % in 2024 and a record low of 25.600 % in 2015. Population: Internet Use: Orders of Goods & Services: % of Total: NW: City of St Petersburg data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Russia Premium Database’s Transport and Telecommunications Sector – Table RU.TH003: Population: by Internet Use for Orders of Goods and Services: by Region.

Population with Income per Capita below Living Cost: % of Total: NW: City of St Petersburg data was reported at 3.500 % in 2024. This records a decrease from the previous number of 4.400 % for 2023. Population with Income per Capita below Living Cost: % of Total: NW: City of St Petersburg data is updated yearly, averaging 9.250 % from Dec 1995 (Median) to 2024, with 30 observations. The data reached an all-time high of 33.100 % in 1999 and a record low of 3.500 % in 2024. Population with Income per Capita below Living Cost: % of Total: NW: City of St Petersburg data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Russia Premium Database’s Demographic and Labour Market – Table RU.GA015: Population with Income per Capita below Living Cost.

Population: NW: St Petersburg City: Working Age data was reported at 3,108.700 Person th in 2019. This records an increase from the previous number of 3,070.200 Person th for 2018. Population: NW: St Petersburg City: Working Age data is updated yearly, averaging 3,070.300 Person th from Dec 2003 (Median) to 2019, with 16 observations. The data reached an all-time high of 3,135.100 Person th in 2013 and a record low of 2,836.400 Person th in 2009. Population: NW: St Petersburg City: Working Age data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Russia Premium Database’s Demographic and Labour Market – Table RU.GA017: Population: by City: North Western Federal District. Labour Force population includes men aged 16-59 years old and women aged 16-54 years old Население трудоспособного возраста - мужчины 16-59 лет, женщины 16-54 года

Population: NW: St Petersburg City data was reported at 5,398.100 Person th in 2019. This records an increase from the previous number of 5,383.900 Person th for 2018. Population: NW: St Petersburg City data is updated yearly, averaging 4,757.400 Person th from Dec 1992 (Median) to 2019, with 28 observations. The data reached an all-time high of 5,398.100 Person th in 2019 and a record low of 4,568.100 Person th in 2007. Population: NW: St Petersburg City data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Russia Premium Database’s Demographic and Labour Market – Table RU.GA017: Population: by City: North Western Federal District.

Paid Services Rendered to Population: NW: City of St Petersburg data was reported at 722,989,404.500 RUB th in 2022. This records an increase from the previous number of 634,912,742.500 RUB th for 2021. Paid Services Rendered to Population: NW: City of St Petersburg data is updated yearly, averaging 209,438,603.400 RUB th from Dec 1993 (Median) to 2022, with 30 observations. The data reached an all-time high of 722,989,404.500 RUB th in 2022 and a record low of 266,754.000 RUB th in 1993. Paid Services Rendered to Population: NW: City of St Petersburg data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Russia Premium Database’s Household Survey – Table RU.HG006: Paid Services Rendered to Population: by Region: Annual.