Analysis of ‘Population density according to different spatial characteristics, since 2009 ’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from http://data.europa.eu/88u/dataset/0367311d-d9fd-45c0-8ef4-8f42d1fe44eb-stadt-zurich on 11 January 2022.

--- Dataset description provided by original source is as follows ---

In this data set different forms of population density (persons per ha) are offered as a time series since 2009, i.e. by city district, by Stadquartier, by entire city, per total area, per land area without forest and per settlement area.

--- Original source retains full ownership of the source dataset ---

This dataset presents a fine-grained population map of Tanzania with a resolution of 100 meters for 2020, generated using the POMELO super-resolution technique that is based on deep learning. Please refer to our Nature Scientific Reports publication for more details.

Background: Traditionally, many countries, including those in sub-Saharan Africa, rely on aggregated census data over expansive spatial units, which are not always timely or accurate. The need for detailed population maps is paramount in several sectors, including urban development, environmental supervision, public health, and humanitarian initiatives. Addressing this gap, the POMELO methodology leverages coarse census data in conjunction with open geodata to produce high precision population maps.

Key Features: Resolution: The map offers a granular view with a 100m ground sampling distance, providing intricate details about population distributions in Tanzania. Data Sources: Utilizing a combination of projected admisistrative census data (UN), and supplementing it with open geodata. Reliability: In comparative experiments conducted in sub-Saharan Africa, POMELO's ability to disaggregate coarse census counts achieved R2 values of 85-89%. Furthermore, its potential to predict population numbers without any census data reached accuracy levels of 48-69%.

In this data set, the area by land cover type is offered as a time series, namely by city district, by urban district, by entire city, per total area, per land area without forest and per settlement area. The data on the number of persons by land cover type can be found in the dataset «Population density according to different spatial characteristics» at https://data.stadt-zuerich.ch/dataset/bev_bestand_jahr_bevoelkerungsdichten_od5802

This dataset presents a fine-grained population map of Rwanda with a resolution of 100 meters for 2020, generated using the POMELO super-resolution technique that is based on deep learning. Please refer to our Nature Scientific Reports publication for more details.

Background: Traditionally, many countries, including those in sub-Saharan Africa, rely on aggregated census data over expansive spatial units, which are not always timely or accurate. The need for detailed population maps is paramount in several sectors, including urban development, environmental supervision, public health, and humanitarian initiatives. Addressing this gap, the POMELO methodology leverages coarse census data in conjunction with open geodata to produce high precision population maps.

Key Features: Resolution: The map offers a granular view with a 100m ground sampling distance, providing intricate details about population distributions in Rwanda. Data Sources: Utilizing a combination of projected admisistrative census data (UN), and supplementing it with open geodata. Reliability: In comparative experiments conducted in sub-Saharan Africa, POMELO's ability to disaggregate coarse census counts achieved R2 values of 85-89%. Furthermore, its potential to predict population numbers without any census data reached accuracy levels of 48-69%.

This dataset presents a fine-grained population map of Zambiawith a resolution of 100 meters for 2020, generated using the POMELO super-resolution technique that is based on deep learning. Please refer to our Nature Scientific Reports publication for more details.

Background: Traditionally, many countries, including those in sub-Saharan Africa, rely on aggregated census data over expansive spatial units, which are not always timely or accurate. The need for detailed population maps is paramount in several sectors, including urban development, environmental supervision, public health, and humanitarian initiatives. Addressing this gap, the POMELO methodology leverages coarse census data in conjunction with open geodata to produce high precision population maps.

Key Features: Resolution: The map offers a granular view with a 100m ground sampling distance, providing intricate details about population distributions in Zambia. Data Sources: Utilizing a combination of projected admisistrative census data (UN), and supplementing it with open geodata. Reliability: In comparative experiments conducted in sub-Saharan Africa, POMELO's ability to disaggregate coarse census counts achieved R2 values of 85-89%. Furthermore, its potential to predict population numbers without any census data reached accuracy levels of 48-69%.

The technical planning heat reduction formulates conceptual statements on heat reduction and provides impulses for the future development of the city of Zurich. The focus is on the human need for pleasant living quality in the outdoor space. Depending on the bioclimatic stress for day and night (cf. Climate Analysis: Plan information maps, Canton Zurich Building Directorate, 2018) were derived from three areas of action with different needs for action. Action area 1 requires measures to improve the day and night situation. Action area 2 requires measures to improve the day-to-day situation. Action area 3 recommends conservation or improvement measures. Areas with high population density and sensitive uses such as schools, nursing centres or age centres (dataset: Hotspots) wants to relieve the city of Zurich in a targeted manner. In these areas, the aim is to provide the population with better opportunities for regeneration, including by upgrading existing green spaces (dataset: Upgrading). Purpose: Planning basis