Hierarchical models facilitate spatial analysis of large data sets: a case study on invasive plant species in the northeastern United States

2009 ◽  
Vol 12 (2) ◽  
pp. 144-154 ◽  
Author(s):  
A. M. Latimer ◽  
S. Banerjee ◽  
H. Sang Jr ◽  
E. S. Mosher ◽  
J. A. Silander Jr
Keyword(s):  
United States ◽  
Spatial Analysis ◽  
Hierarchical Models ◽  
Invasive Plant ◽  
Large Data ◽  
Large Data Sets ◽  
Data Sets ◽  
Invasive Plant Species ◽  
Northeastern United States

Data-driven campaigns in public sensemaking: Discursive positions, contextualization, and maneuvers in American, British, and German debates around computational politics

2020 ◽  
Vol 45 (s1) ◽  
pp. 535-559
Author(s):  
Christian Pentzold ◽  
Lena Fölsche
Keyword(s):  
United States ◽  
United Kingdom ◽  
Presidential Election ◽  
Data Analytics ◽  
Large Data ◽  
Large Data Sets ◽  
Data Driven ◽  
Data Sets ◽  
Federal Election ◽  
Online Comments

AbstractOur article examines how journalistic reports and online comments have made sense of computational politics. It treats the discourse around data-driven campaigns as its object of analysis and codifies four main perspectives that have structured the debates about the use of large data sets and data analytics in elections. We study American, British, and German sources on the 2016 United States presidential election, the 2017 United Kingdom general election, and the 2017 German federal election. There, groups of speakers maneuvered between enthusiastic, skeptical, agnostic, or admonitory stances and so cannot be clearly mapped onto these four discursive positions. Coming along with the inconsistent accounts, public sensemaking was marked by an atmosphere of speculation about the substance and effects of computational politics. We conclude that this equivocality helped journalists and commentators to sideline prior reporting on the issue in order to repeatedly rediscover the practices they had already covered.

scholarly journals Using Large Data Sets to Forecast House Prices: A Case Study of Twenty U.S. States

2011 ◽  
Vol 20 (2) ◽  
pp. 161-190 ◽  
Author(s):  
Rangan Gupta ◽  
Alain Kabundi ◽  
Stephen Miller
Keyword(s):  
House Prices ◽  
Large Data ◽  
Large Data Sets ◽  
Data Sets

scholarly journals Comparative Performance of Supertree Algorithms in Large Data Sets Using the Soapberry Family (Sapindaceae) as a Case Study

2010 ◽  
Vol 60 (1) ◽  
pp. 32-44 ◽  
Author(s):  
Sven Buerki ◽  
Félix Forest ◽  
Nicolas Salamin ◽  
Nadir Alvarez
Keyword(s):  
Large Data ◽  
Large Data Sets ◽  
Data Sets ◽  
Comparative Performance

The Victim's Experience and Fear of Crime

1998 ◽  
Vol 5 (2) ◽  
pp. 93-140 ◽  
Author(s):  
Helmut Kury ◽  
Theodore Ferdinand
Keyword(s):  
United States ◽  
Fear Of Crime ◽  
Rapid Development ◽  
Large Data ◽  
The United States ◽  
Fundamental Concept ◽  
Large Data Sets ◽  
Data Sets ◽  
Free Society ◽  
Public Concern

With the rapid development of sophisticated victim surveys, the fear of crime has emerged as a fundamental concept in theoretical and practical discourse. Since publication of the Report of the President's Commission The Challenge of Crime in a Free Society (1967), the fear of offenders has become a major public concern in the United States alongside the mounting problem of crime itself. The flourishing of national crime surveys in the United States and in Europe has in turn led to large data sets examining carefully not only the knowledge and experience of the victims regarding criminality but also the fear of offenders and its causes ( cf. Herbert and Darwood, 1992; p. 145). We shall offer first, a review of research on these issues in Europe and the United States, and then we shall report our research that has probed these issues in a focused manner.

scholarly journals To the New World and Back Again: Return Migrants in the Age of Mass Migration

ILR Review ◽  
2017 ◽  
Vol 72 (2) ◽  
pp. 300-322 ◽  
Author(s):  
Ran Abramitzky ◽  
Leah Boustan ◽  
Katherine Eriksson
Keyword(s):  
United States ◽  
Return Migration ◽  
New World ◽  
Large Data ◽  
The United States ◽  
Large Data Sets ◽  
Data Sets ◽  
Return Migrants ◽  
Mass Migration ◽  
Before And After

The authors compile large data sets from Norwegian and US historical censuses to study return migration during the Age of Mass Migration (1850–1913). Norwegian immigrants who returned to Norway held lower-paid occupations than did Norwegian immigrants who stayed in the United States, both before and after their first transatlantic migration, suggesting they were negatively selected from the migrant pool. Upon returning to Norway, return migrants held higher-paid occupations relative to Norwegians who never moved, despite hailing from poorer backgrounds. These patterns suggest that despite being negatively selected, return migrants had been able to accumulate savings and could improve their economic circumstances once they returned home.

scholarly journals Penerapan Metode Clustering Dengan Algoritma K-Means Tindak Kejahatan Pencurian di Kabupaten Asahan

2021 ◽  
Vol 1 (1) ◽  
pp. 7-14
Author(s):  
Nur Afni Syahpitri Damanik ◽  
Irianto Irianto ◽  
Dahriansah Dahriansah
Keyword(s):  
Data Mining ◽  
Knowledge Discovery ◽  
Historical Data ◽  
Large Data ◽  
Large Data Sets ◽  
Annual Data ◽  
Data Sets ◽  
Process Data ◽  
Different Characteristics

Abstract:Theft is the illegal taking of property or belongings of another person without the permission of the owner. The most common crime problem in Asahan District is theft, so that the POLRES is still having trouble determining which areas are often the crime of theft. With this problem, we need to do a grouping for areas where theft often occurs, so the process used  is the data mining process. Data mining is one of the processes of Knowledge Discovery from Databases (KDD). KDD is an activity that includes collecting, using historical data to find regularities, patterns or relationships in large data sets. One of the techniques known in data mining is clustering technique. The K-Means method is a method for clustering techniques, K- Means is a method that partitions data into groups so that data with the same characteristics are entered into the same set of groups and data with different characteristics are grouped into other groups. The attributes used in grouping this data are annual data, namely 2015, 2016, 2017, 2018, 2019. A case study of 9 POLSEK in the Asahan. Keywords: Data Mining, Clustering, K-Means Algorithm, Theft Crimes Grouping.  Abstrak: Pencurian merupakan pengambilan properti atau barang milik orang lain secara tidak sah tanpa ijin dari pemilik. Masalah tindak kejahatan yang paling banyak terjadi di Kabupaten Asahan adalah tindak kejahatan pencurian sehingga pihak POLRES masih kesulitan untuk menentukan daerah mana saja yang sering terjadi tindak kejahatan pencuriaan. Dengan adanya masalah ini kita perlu melakukan pengelompokan untuk daerah mana saja yang sering terjadi tindak pencurian maka proses yang digunakan adalah proses data mining. Data mining adalah salah satu proses dari Knowledge Discovery from Databases (KDD). KDD adalah kegiatan yang meliputi pengumpulan, pemakaian data, historis untuk menemukan keteraturan, pola atau hubungan dalam set data besar. Salah satu teknik yang di kenal dalam data mining adalah teknik clustering. Metode K-Means merupakan metode untuk teknik clustering, K-Means adalah metode yang mempartisi data kedalam kelompok sehingga data berkarakteristik sama dimasukan kedalam set kelompok yang sama dan data yang berkerakteristik berbeda dikelompokkan ke dalam kelompok yang lain. Atribut yang di gunakan dalam pengelomokan data ini adalah data pertahun yaitu tahun 2015, 2016, 2017, 2018, 2019. Studi kasus pada 9 POLSEK yang ada di daerah kabupaten Asahan. Kata kunci: Data Mining, Clustering, Algoritma K-Means, Pengelompokan Tindak Kejahatan  Pencurian.

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