Where is Everyone? The Importance of Population Density Data: A Data Artefact Study of the Facebook Population Density Map

2021 ◽  
Author(s):  
Stefaan Verhulst ◽  
Aditi Ramesh ◽  
Andrew Young ◽  
Andrew Zahuranec
Keyword(s):  
Population Density ◽  
Density Data ◽  
Density Map

Classification Method of Population Density Map Based on Lorenz Curve

2010 ◽  
Vol 11 (6) ◽  
pp. 833-838 ◽  
Author(s):  
Jiafu HAN ◽  
Hongsheng LI ◽  
Zhong ZHANG
Keyword(s):  
Population Density ◽  
Lorenz Curve ◽  
Classification Method ◽  
Density Map

scholarly journals Mapping current and future European public water withdrawals and consumption

2014 ◽  
Vol 18 (2) ◽  
pp. 407-416 ◽  
Author(s):  
I. Vandecasteele ◽  
A. Bianchi ◽  
F. Batista e Silva ◽  
C. Lavalle ◽  
O. Batelaan
Keyword(s):  
Population Density ◽  
Temporal Trends ◽  
Absolute Error ◽  
Reference Scenario ◽  
Total Water ◽  
Country Level ◽  
European Public ◽  
Density Map ◽  
Per Capita ◽  
Water Withdrawals

Abstract. In Europe, public water withdrawals make up on average 30% and in some cases up to 60% of total water withdrawals. These withdrawals are becoming increasingly important with growing population density; hence there is a need to understand the spatial and temporal trends involved. Pan-European public/municipal water withdrawals and consumption were mapped for 2006 and forecasted for 2030. Population and tourism density were assumed to be the main driving factors for withdrawals. Country-level statistics on public water withdrawals were disaggregated to a combined population and tourism density map (the "user" density map) computed for 2006. The methodology was validated using actual regional withdrawal statistics from France for 2006. The total absolute error (TAE) calculated was proven to be reduced by taking into account the tourism density in addition to the population density. In order to forecast the map to 2030 we considered a reference scenario where per capita withdrawals were kept constant in time. Although there are large variations from region to region, this resulted in a European average increase of water withdrawals of 16%. If we extrapolate the average reduction in per capita withdrawals seen between 2000 and 2008, we forecast a reduction in average total water withdrawals of 4%. Considering a scenario where all countries converge to an optimal water use efficiency, we see an average decrease of 28%.

GIS Based Spatialization of Population Data in Meijiang River Basin

2013 ◽  
Vol 295-298 ◽  
pp. 2378-2383 ◽  
Author(s):  
Xiang Gui Zeng ◽  
Ge Ying Lai ◽  
Fa Zhao Yi ◽  
Ling Ling Zhang
Keyword(s):  
Land Use ◽  
Population Density ◽  
River Basin ◽  
Population Distribution ◽  
Population Data ◽  
Fusion Technology ◽  
Processing Technologies ◽  
Source Data ◽  
Density Map ◽  
Main Factors

This paper used GIS spatial analysis and data processing technologies and multi-source data fusion technology to spatialize the population data of Meijiang river basin. Land use was selected as the index factor and the settlements as the indicative factor. Selected terrain, roads and rivers were the main influencing factors and were further classified into several sub-factors. During the simulation, we first calculated the weight indexes of sub-factors on the settlements distribution and then fused the indexes to calculate the weight indexes of the main factors. Second we calculated the weight indexes of settlements on the population distribution. Last we fused the weight indexes of the main factors and the weight indexes of settlements to obtain the population density indexes of whole region and then generated the 100m×100m resolution raster population density map.

A Population Density Map of European Russia

1926 ◽  
Vol 16 (2) ◽  
pp. 341 ◽  
Author(s):  
Sten de Geer ◽  
Benjamin Semenov-Tian-Shansky
Keyword(s):  
Population Density ◽  
European Russia ◽  
Density Map

scholarly journals Pedotransfer functions for Irish soils – estimation of bulk density (<i>ρ</i><sub>b</sub>) per horizon type

SOIL ◽  
2016 ◽  
Vol 2 (1) ◽  
pp. 25-39 ◽  
Author(s):  
B. Reidy ◽  
I. Simo ◽  
P. Sills ◽  
R. E. Creamer
Keyword(s):  
Bulk Density ◽  
Soil Bulk Density ◽  
Pedotransfer Functions ◽  
Data Sets ◽  
Density Data ◽  
Soil Carbon Stock ◽  
Packing Structure ◽  
Core Method ◽  
International Data ◽  
Density Map

Abstract. Soil bulk density is a key property in defining soil characteristics. It describes the packing structure of the soil and is also essential for the measurement of soil carbon stock and nutrient assessment. In many older surveys this property was neglected and in many modern surveys this property is omitted due to cost both in laboratory and labour and in cases where the core method cannot be applied. To overcome these oversights pedotransfer functions are applied using other known soil properties to estimate bulk density. Pedotransfer functions have been derived from large international data sets across many studies, with their own inherent biases, many ignoring horizonation and depth variances. Initially pedotransfer functions from the literature were used to predict different horizon type bulk densities using local known bulk density data sets. Then the best performing of the pedotransfer functions were selected to recalibrate and then were validated again using the known data. The predicted co-efficient of determination was 0.5 or greater in 12 of the 17 horizon types studied. These new equations allowed gap filling where bulk density data were missing in part or whole soil profiles. This then allowed the development of an indicative soil bulk density map for Ireland at 0–30 and 30–50 cm horizon depths. In general the horizons with the largest known data sets had the best predictions, using the recalibrated and validated pedotransfer functions.

scholarly journals Significant locations in auxiliary data as seeds for typical use cases of point clustering

2018 ◽  
Vol 1 ◽  
pp. 1-6
Author(s):  
Johannes Kröger
Keyword(s):  
Population Density ◽  
Data Clustering ◽  
Use Cases ◽  
Auxiliary Data ◽  
Density Data ◽  
Local Maxima ◽  
Point Data ◽  
Seed Points ◽  
Apparent Distribution ◽  
Grid Based

Random greedy clustering and grid-based clustering are highly susceptible by their initial parameters. When used for point data clustering in maps they often change the apparent distribution of the underlying data. We propose a process that uses precomputed weighted seed points for the initialization of clusters, for example from local maxima in population density data. Exemplary results from the clustering of a dataset of petrol stations are presented.

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