The population density in Peru stood at 26.15 people in 2022. In a steady upward trend, the population density rose by 17.97 people from 1961.

Historical dataset showing Peru population density by year from 1961 to 2022.

Peru PE: Population Density: People per Square Km data was reported at 25.129 Person/sq km in 2017. This records an increase from the previous number of 24.823 Person/sq km for 2016. Peru PE: Population Density: People per Square Km data is updated yearly, averaging 16.694 Person/sq km from Dec 1961 (Median) to 2017, with 57 observations. The data reached an all-time high of 25.129 Person/sq km in 2017 and a record low of 8.086 Person/sq km in 1961. Peru PE: Population Density: People per Square Km data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Peru – Table PE.World Bank.WDI: Population and Urbanization Statistics. Population density is midyear population divided by land area in square kilometers. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship--except for refugees not permanently settled in the country of asylum, who are generally considered part of the population of their country of origin. Land area is a country's total area, excluding area under inland water bodies, national claims to continental shelf, and exclusive economic zones. In most cases the definition of inland water bodies includes major rivers and lakes.; ; Food and Agriculture Organization and World Bank population estimates.; Weighted average;

Actual value and historical data chart for Peru Population Density People Per Sq Km

View yearly updates and historical trends for Peru Population Density. Source: World Bank. Track economic data with YCharts analytics.

Peru PE: Population Density: Inhabitants per sq km data was reported at 26.150 Person in 2022. This records an increase from the previous number of 25.900 Person for 2021. Peru PE: Population Density: Inhabitants per sq km data is updated yearly, averaging 22.130 Person from Dec 1990 (Median) to 2022, with 33 observations. The data reached an all-time high of 26.150 Person in 2022 and a record low of 17.200 Person in 1990. Peru PE: Population Density: Inhabitants per sq km data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Peru – Table PE.OECD.GGI: Social: Demography: Non OECD Member: Annual.

Comprehensive socio-economic dataset for Peru including population demographics, economic indicators, geographic data, and social statistics. This dataset covers key metrics such as GDP, population density, area, capital city, and regional classifications.

PE：人口密度：每平方公里人口在12-01-2017达25.129Person/sq km，相较于12-01-2016的24.823Person/sq km有所增长。PE：人口密度：每平方公里人口数据按年更新，12-01-1961至12-01-2017期间平均值为16.694Person/sq km，共57份观测结果。该数据的历史最高值出现于12-01-2017，达25.129Person/sq km，而历史最低值则出现于12-01-1961，为8.086Person/sq km。CEIC提供的PE：人口密度：每平方公里人口数据处于定期更新的状态，数据来源于World Bank，数据归类于全球数据库的秘鲁 – 表 PE.世行.WDI：人口和城市化进程统计。

ABSTRACT The black-headed night monkey, Aotus nigriceps, has one of the largest distribution ranges of the 11 night monkey species found across Central and South America. Yet, only three studies have focused on their ecology, describing considerable variation in habitat, group composition, and population density. Therefore, we analyzed habitat use, group composition, population density, and diet of 14 groups at two field sites in southeastern Peru. All sampled groups were found in secondary tropical rainforest, often dominated by native bamboo species. Half of the observed sleeping sites were in bamboo stands, though groups also emerged from cane thickets and lianas. This contrasts with other Aotus studies which have found groups living in tree cavities and lianas. Population density estimates for both sites were 19 and 50 individuals per km2, outside the range previously reported for A. nigriceps (31−34 individuals per km2). We recovered seeds of 12 species from fecal samples over the course of two field seasons, belonging mainly to Cecropiaceae, Piperaceae and Moraceae. Our results suggest that the black-headed night monkey in Peru can survive and even thrive in secondary forest, feeding extensively on pioneer species, occupying a range of forest types, all while living near human settlements.

1.6 (number per thousand population) in 2022.

人口密度：每平方公里的居民在12-01-2022达26.150人，相较于12-01-2021的25.900人有所增长。人口密度：每平方公里的居民数据按年更新，12-01-1990至12-01-2022期间平均值为22.130人，共33份观测结果。该数据的历史最高值出现于12-01-2022，达26.150人，而历史最低值则出现于12-01-1990，为17.200人。CEIC提供的人口密度：每平方公里的居民数据处于定期更新的状态，数据来源于Organisation for Economic Co-operation and Development，数据归类于全球数据库的秘鲁 – Table PE.OECD.GGI: Social: Demography: Non OECD Member: Annual。

This repository contains the Python scripts used to visualize the inferred movements of respiratory virus lineages at the departmental level in Peru and internationally between countries. The visualizations were generated using the Cartopy, GeoPandas, Matplotlib, Basemap, and Shapely libraries.Figure 3. Inferred movements of SARS-CoV-2 infections between the 25 administrative regions of Peru. The map is projected in Plate Carrée at a scale of 200 km. Circles represent departments and are scaled to the number of inferred outgoing viral movements. Arrows represent the direction and intensity of viral transitions between regions, with width indicating the number of events. Population density is displayed as a blue choropleth background, based on open data from the Ministry of Health of Peru.Shapefiles from INEI were used for departmental boundaries.Nodes represent departments, and their size is proportional to the number of inferred outgoing transitions.Arrows indicate origin–destination routes, with thickness and transparency proportional to the number of events.Random grey circles (~10,000 inhabitants) were added to represent population density, based on MINSA data.Figure 4. International spread of four SARS-CoV-2 sub-lineages of Peruvian origin. Movements of Lambda C.37, Gamma P.1.12, Omicron XBB.2.6, and Omicron DJ.1 lineages inferred from phylogeographic analysis. Arcs represent transitions between countries of sample collection, projected in Plate Carrée at a scale of 1000 km. The width of each arrow indicates the number of inferred transitions, and the color denotes the viral lineage.Base maps from Natural Earth Data were used.Geodesic arrows represent inferred transitions between countries.The thickness of each arrow indicates the number of inferred transitions on that route.The color of each arrow represents the corresponding viral lineage.Github repository available at: https://github.com/renatocava/inferred_dates_animationUploaded November 13, 2025.

Abstract

The Government of Peru with the assistance of the World Bank and the Inter-American Development Bank launched a Public Expenditure Tracking Survey (PETS) to study weaknesses of the budget execution system in education and health sectors. The study also aimed to analyze effects of these weaknesses on service delivery and to assist in the generation of policy recommendations.

Documented here is the Public Expenditure Tracking Survey conducted in Peru health sector. The study focused on Vaso de Leche (Glass of Milk) program, one of the largest food assistance program in Peru. By law, the intended primary beneficiaries of the program are children six years old or less and pregnant and breastfeeding mothers. Priority is given to those showing clear signs of malnutrition or tuberculosis. The products distributed can be milk in any form and/or milk substitutes, and/or other products such as soybean, oatmeal, quinoa, kiwicha or other. The funds for the program are transferred from central to local governments. Unfortunately, organizational hurdles, inefficiencies, leakages, and sometimes low nutritional value of the products chosen for distribution, limit the effectiveness of the Vaso de Leche (VdL) program to accomplish its goals.

This study analyzed the leakages of funds for Vaso de Leche program from the central government to the municipalities, within municipalities, from municipality to VdL committees, from VdL committees to beneficiaries/households, and inside the household. One hundred twenty municipalities out of 1828 were surveyed. The fieldwork was carried out from February 3, 2002, to February 17, 2002.

Geographic coverage

Ancash, Arequipa, Cajamarca, Cusco, Lima, Loreto and Piura regions.

Kind of data

Sample survey data [ssd]

Sampling procedure

The following regions were chosen for the study: Ancash, Arequipa, Cajamarca, Cusco, Loreto, and Piura. These regions have the broadest range of geography, population density and poverty distribution.

One hundred municipalities were selected in these regions. Municipalities were chosen based on poverty as a central stratification variable. Investigators employed the following steps:

• A database consisting of the entire universe of districts in Peru excluding Lima & Callao (total of 1,651 districts) was used as a starting point.

• The Ministry of Economy and Finance's continuous index of poverty FGT24 was used to calculate poverty population deciles.

• The deciles were arranged into three groups such that group 1 consisted of deciles 1-3, group 2 contained deciles 4-7 and group 3 had deciles 8-10. These three groups approximate the categories of "not poor", "poor" and "extreme poor" and were used to stratify the districts of our sub-population (Ancash and Piura) into three strata.

• The three strata represented 14 percent, 41 percent, and 45 percent of the districts in Peru (excluding Lima and Callao).

• In order for the sample to be self-weighted, 14, 41, and 45 municipalities (total of 100) were chosen from each stratum respectively, (from the sub-population of six departments). The selection within each stratum was done using Probability Proportional to Size (PPS) relative to district population.

Once the above procedures were carried out, individual municipalities were selected according to PPS criteria, using a complete listing of all districts selected that were ordered within the stratums by geographic order to allow a systematic selection that ensured geographic heterogeneity.

Within each municipality, from the roster of Vaso de Leche committees using systematic sampling technique, researchers selected four committees. If there were less than four Vaso de Leche committees in a municipality, all were included in the sample. A substitute for a committee was used if travel time to the committee exceeded 24 hours. The sample slightly underrepresented remote areas within the neighborhoods of the selected committees.

In each municipality investigators interviewed the mayor and obtained municipal-level data from him/her. They also attained the municipal roster of committees participating in the Vaso de Leche program. By law, Vaso de Leche committees should include a mayor, a municipal employee, a representative from the Ministry of Health, three representatives of the Mother's Associations (elected by the mothers following the rules established in their own statutes), and a representative of the local agriculture/farming association accredited by the Ministry of Agriculture.

Enumerators interviewed at least one committee member. From the respondents, researchers received a list of beneficiary households and interviewed four households in each committee catchments area, using the survey instrument intended for households in Arequipa, Cusco, Cajamarca, and Loreto.

Mode of data collection

Face-to-face [f2f]

This dataset relates to a study exploring off-grid sanitation practices in Kenya, Peru, and South Africa, with a focus on how various user demographics access and utilize sanitation facilities. The study contrasts container-based sanitation with alternative methods. Participants, acting as citizen researchers, gathered confidential information using a specialized mobile application. The primary objective was to uncover obstacles and challenges, with the intention of sharing insights with other municipalities interested in implementing container-based sanitation solutions for off-grid regions.

Over the course of 12 months, participants received incentives for consistent involvement, following a micro-payment for micro-tasks model. Selection of participants was randomized, involving attendance at a training session and, if necessary, provision of a smartphone which they retained at the conclusion of the project. Weekly smartphone surveys were conducted in more than 300 households within informal settlements across the three countries throughout the project duration. These surveys aimed to capture daily routines, well-being, income levels, usage of infrastructure services, livelihood or environmental shocks and other socioeconomic factors on a weekly basis, contributing to more comprehensive analyses and informed decision-making processes.

The smartphone-based methodology offered an efficient and adaptable means of data collection, facilitating broad coverage across diverse geographical areas and subjects, while promoting regular engagement. Open Data Kit (ODK) tools were utilized to support data collection in resource-limited settings with unreliable connectivity.To protect human health and the environment, sanitation systems must separate people from their excreta and treat it. This does not just involve technologies but other aspects like finance, government policies and human behaviours must be considered. Sewers and wastewater treatment plants can assist in providing safe sanitation, but they are expensive and challenging to build, particularly in dense urban areas or where people do not own the land that they live on. In fact, only 45% of the world's urban population have safely managed sanitation; that is where human waste is treated before disposal. Many of these people are instead using off-grid options for sanitation, such as pit latrines and septic tanks. These are physically difficult to empty, especially in areas of high population density, on steep slopes or with a high water table, and pose significant health hazards. The collected waste is often dumped illegally, frequently into water sources. Off grid solutions can only manage waste safely if the waste collection, treatment and disposal is properly considered.

This research will focus on an emerging off-grid sanitation option in the form of container-based sanitation (CBS) across four city contexts where the provision and regulation of CBS is done by different organisations. In Cap Haitien (Haiti), CBS is provided by an NGO, in Lima (Peru) it is provided by a private company, in Cape Town (South Africa) it is provided by the municipality and in Kakuma Refugee camp (Kenya) it is provided by a private company working with an NGO. A refugee camp is included as in the future, refugees driven by climate and other factors will make up a significant proportion of the world's urban population.

Interviews will be conducted with the staff working for the CBS provider as well as other organisations that they work closely with, e.g., local government, water utilities and regulators. To capture the feelings and opinions of the people using the CBS toilets, as well as people using other off-grid sanitation options like pit latrines, a novel smartphone data collection will be used. Participants will complete a short survey several times a week to capture aspects of their mental well-being.

In addition, the data collected will reveal whether current regulations and policies support CBS and whether CBS increases disparities or decreases them. For example, does CBS without a permanent infrastructure make users feel more vulnerable to eviction, and is the collection of the containers a burden? Or does it give access to people who would not otherwise be able to use a toilet?

The project will also look at the links with other sectors, specifically waste, energy, transport and solid waste, as if CBS coverage is increased these services will be impacted. For example CBS relies on road transport and cause additional traffic. But the use of CBS stops the disposal of waste into rivers so water supplies are cleaner.