Mais

Criação de localização aleatória dentro de uma rede a partir de um determinado local e uma determinada distância


Para um estudo de seleção de habitat, estou tentando identificar a disponibilidade. Minha espécie vive ao longo de corpos d'água e eu poderia avaliar distâncias movidas entre dois locais consecutivos usando o analista de rede (Calculando distâncias entre pontos consecutivos ao longo de rios (polígono) usando ArcGIS Desktop?).

Gostaria de obter uma localização potencial que o animal poderia ter alcançado dentro da mesma distância.

Para melhor compreensão: tenho a localização A e a distância d da localização A a B tenho uma rede de rios e outros corpos d'água

Agora quero obter a localização C usando A como localização inicial e distância d como a distância ao longo da rede.

Isso é possível fazer e, em caso afirmativo, como?


Encontrei a (ou uma) solução: Precisa-se da Ferramenta "Área de Serviço" no Analista de Rede. Primeiro, você precisa ter alguns pré-requisitos necessários:

a) Configure uma rede no analista de rede. Você precisa ter o comprimento das estradas / rios / etc. como um atributo adicionado durante a configuração do Analista, nomeie-o, por exemplo, "Distância"

b) Você precisa de uma coluna em seu arquivo de localização com a distância a ser calculada para cada localização (então, se você tiver várias distâncias por localização, elas precisam estar cada uma em uma nova linha), nomeie-a, por exemplo, "New_Dist")

a) Abra Analista de Rede e escolha "Área de serviço", abra-o como uma nova barra

b) Clique no ícone à direita da área de serviço. Abra "Acumulação", você verá o nome do atributo de Comprimento que você configurou no Analista de Rede (por exemplo, "Distância"). Confira

c) Abra "Geral", desmarque "Gerar polígonos" (leva séculos e faz você sair do caminho)

d) Abra "Geração de linha", clique em "Gerar linha"

c) Feche.

e) Clique em "Instalações" e carregue seus locais nele. Usei o ID do local (não OBJECTID, mas o meu próprio para que não fosse confundido com mais nada), e adicionei no campo "Breaks_Distance" o campo com suas Distâncias (por exemplo, "New_Dist")

f) Carregue-o e deixe-o funcionar. Será lento, mas não muito lento. O carregamento dos locais deve ser bastante rápido. Caso contrário, verifique se você tem um índice espacial (vá para Catálogo, abra-o e verifique as propriedades. Se nenhum índice espacial for construído, construa-o agora. Ele vai muito mais rápido).

g) Exportar as linhas

h) Junte-se ao novo arquivo usando "Facility_ID" com a "Área de serviço Facility" usando "OBJECT_ID". Isso é necessário porque as próprias linhas têm pouca informação, então você precisa juntá-las.

i) Adicione as novas colunas X e Y

j) Calcule para X = "X Fim da linha" em "Calcular Geometria" da tabela e o Y = "Y Fim da Linha"

k) Exportar a tabela como tal

l) importe a tabela novamente para o gis com "Display X e Y", a seguir exporte-a como novo arquivo.

m) Como a distância real é feita em segmentos ao longo das diferentes possibilidades ao longo da rede, mas você quer apenas saber a localização final, você precisa agora adicionar outra coluna à tabela do novo arquivo, chame-a como quiser, mas pegue como "Float". Use a calculadora de campo usando "Break_Dis" - "ToCumul" e selecione apenas aqueles que são = <0. Esses são os locais com a distância aproximada que você queria ter.


Como encontrar todos os vizinhos de um determinado ponto em uma triangulação delaunay usando scipy.spatial.Delaunay?

Tenho procurado uma resposta para esta pergunta, mas não consigo encontrar nada útil.

Estou trabalhando com a pilha de computação científica python (scipy, numpy, matplotlib) e tenho um conjunto de pontos 2 dimensionais, para os quais calculo a traingulação de Delaunay (wiki) usando scipy.spatial.Delaunay.

Preciso escrever uma função que, dado qualquer ponto a, retornará todos os outros pontos que são vértices de qualquer simplex (ou seja, triângulo) do qual a também é um vértice (os vizinhos de a na triangulação). No entanto, a documentação para scipy.spatial.Delaunay (aqui) é muito ruim, e eu não consigo entender como as coisas simples estão sendo especificadas ou eu faria isso. Mesmo apenas uma explicação de como os vizinhos, vértices e matrizes vertex_to_simplex na saída de Delaunay são organizados já seria o suficiente para me levar adiante.


Você pode usar, por exemplo, r.nextInt (101)

Para um "entre dois números" mais genérico, use:

Isso dá a você um número aleatório entre 10 (inclusivo) e 100 (exclusivo)

Supondo que o superior seja o limite superior e o inferior seja o limite inferior, você pode fazer um número aleatório, r, entre os dois limites com:

se você precisar gerar mais de um valor, basta usar o loop for para esse

Se você quiser especificar um intervalo mais decente, como de 10 a 100 (ambos estão no intervalo)

No seu caso, seria algo assim:

Java não tem um gerador aleatório entre dois valores da mesma forma que o Python. Na verdade, leva apenas um valor para gerar o Random. O que você precisa fazer, então, é adicionar UM CERTO NÚMERO ao número gerado, o que fará com que o número fique dentro de um intervalo. Por exemplo:


Introdução

Serviço baseado em localização (LBS) é um tipo de serviço de informação que fornece aos usuários posições geográficas localizadas por dispositivos móveis e rede sem fio. Existem muitas informações nos dados de localização, como interesses do usuário, hobbies do usuário e padrão de comportamento do usuário. O LBS pode ser empregado em uma série de aplicações, incluindo: publicidade baseada em localização [1], serviços meteorológicos personalizados, entretenimento [2], vida pessoal e assim por diante. Uma previsão ou recomendação de localização eficaz pode fazer com que os usuários tenham uma boa experiência.

Os avanços nas tecnologias de aquisição de localização e comunicação móvel capacitam as pessoas a usar os dados de localização com as redes sociais online existentes de várias maneiras. As pessoas podem compartilhar sua localização atual, registrar rotas de viagem com GPS para compartilhar experiências de viagem no GeoLife [3]. Zheng [4] dá uma visão geral da mineração de dados de trajetória, incluindo o pré-processamento de dados de trajetória, mineração de padrão e classificação, explora as conexões, correlações e diferenças entre essas técnicas existentes e também alguns conjuntos de dados de trajetória públicos são apresentados. Zheng [5] apresenta a abordagem para encontrar as viagens do candidato top-k dentro dos dados de trajetória incerta. Os dados históricos são usados ​​para inferir a viagem de viagem e reduzem a incerteza da trajetória do usuário.

Para melhorar a experiência do serviço de localização, é necessário saber a localização do usuário com antecedência. Por exemplo, se for possível prever que o usuário aparecerá no local B às 18h com base nos locais visitados anteriormente, o provedor LBS pode enviar as informações de recomendação ou anúncios para restaurantes no local B para o usuário com antecedência. Xue [6] apresenta o algoritmo SubSyn para previsão de localização. Os dados de trajetória histórica do usuário são decompostos no conjunto de subtrajetória, o que aumenta o número de trilhas e o tamanho dos dados de treinamento, e o desempenho de previsão é aprimorado. Quanto à predição de trilhas, o método do Modelo de Markov [7,8] é amplamente utilizado e sua ideia central é construir a cadeia de Markov para especulação. O algoritmo do SMLP (algoritmo de predição de localização do usuário móvel com reconhecimento social) é proposto por Yu [9]. Ele integra a correlação do usuário com o modelo de Markov para previsão de localização. Embora o algoritmo exija menos espaço do que o modelo de Markov, os resultados da previsão são fortemente afetados pela divisão da região. Lian [10] propõe o algoritmo CEPR (Collaborative Exploration and Periodically Returning) que adota a técnica de filtragem colaborativa e o comportamento histórico do usuário é usado para previsão e recomendação de localização. Eles também fornecem a análise de correlação [11] entre as informações estatísticas do usuário e a previsibilidade de localização nos dados de Gowalla (https://snap.stanford.edu/data/loc-gowalla.html). A árvore de prefixos e a estratégia de busca heurística são adotadas para implementar a recomendação de viagem personalizada de Zhang [12]. Wu [13] usa Markov Random Field para prever a anotação dos registros de localização e o destino do usuário, o melhor desempenho é alcançado quando há mais registros do usuário. Nghia [14] usa fatoração de matriz para selecionar recursos e predizer a localização do usuário. Embora o algoritmo possa prever a localização geográfica em tempo real, seu conjunto de dados é composto de tweets contendo muitas informações semânticas. Os resultados são influenciados pelas emoções e expressões subjetivas das pessoas.

Gambs [15] estende um modelo de mobilidade chamado Mobility Markov Chain (MMC) para incorporar o n locais visitados anteriormente para a próxima previsão de local. No entanto, ele não pode prever a localização em qualquer intervalo de tempo. Mathew [16] apresenta um método híbrido para predizer a mobilidade humana com base em Modelos Ocultos de Markov (HMMs). Eles usam o algoritmo direto para calcular a probabilidade de possíveis sequências e retornar o próximo lugar da sequência com a maior probabilidade. Mas os resultados experimentais no GeoLife não estão satisfeitos com a maior precisão @ 5 de 26,40. Qiao [17] propõe um modelo de predição híbrido baseado em Markov que contém três estágios: descoberta de padrão de mobilidade, preditor de Markov de ordem variável e cálculo de similaridade de usuários baseado em padrão de mobilidade. Os dados da trajetória humana são extraídos do tráfego de dados de uma rede LTE (Long Term Evolution). As extensas avaliações experimentais devem ser conduzidas para comparar com outros trabalhos relacionados em diferentes conjuntos de dados. Huang [18] propõe um modelo preditivo levando em consideração mudanças de atividades. Ele é implementado para dois usuários selecionados do conjunto de dados GeoLife e obtém melhorias de desempenho. Os resultados do estudo são limitados pela cobertura espacial e temporal do conjunto de dados usado e deve ser aplicado para prever o movimento humano em diferentes dias da semana com dados de melhor qualidade.

Além disso, muitas outras abordagens também são usadas para construir o modelo de predição, como o método baseado em regras de associação [19] e assim por diante. No entanto, todas essas estratégias existentes não podem fornecer a previsão com base em tempo real.

GeoLife (https://www.microsoft.com/en-us/download/details.aspx?id=52367) são os dados comumente usados ​​para serviço baseado em localização, que registra uma ampla gama de movimentos ao ar livre dos usuários, incluindo não apenas rotinas de vida, mas também alguns entretenimentos e atividades esportivas. Este conjunto de dados de trajetória pode ser usado em muitos campos de pesquisa, como mineração de padrões de mobilidade, reconhecimento de atividade do usuário, redes sociais baseadas em localização e recomendação de localização.

Neste artigo, abordamos a questão de prever a localização do usuário na série de tempo contínua com base nos dados de trajetória histórica e dar a melhoria ao modelo de Markov original. A sequência de tempo discreta é simulada para a sequência contínua pelo modelo de mistura gaussiana.


A ANÁLISE DA FORMA ESPACIAL E SUA RELAÇÃO COM A TEORIA GEOGRÁFICA

W. Bunge, “Theoretical Geography,”Lund Studies in Geography, Series C, No. 1 (1962), pág. 171

P. Haggett, Análise Locacional em Geografia Humana (Londres: E. Arnold Ltd., 1965), pp. 15-16.

D. Harvey, "Behavioral Postulates and the Construction of Theory in Human Geography",Artigo do Seminário Série A, No. 6 (Bristol, Inglaterra: Departamento de Geografia, University of Bristol, 1967), pp. 7–8.

F. Lukermann, Geografia entre as Ciências (Mimeographed, Minneapolis / Kalamata, 1964), p. 26

Essas abordagens mais convencionais para a análise estatística são revisadas em L. J. King, Análise Estatística em Geografia (Englewood Cliffs: Prentice-Hall Inc., 1969).

As introduções à geometria euclidiana são dadas em textos como, H. S. M. Coxeter, Introdução à Geometria (Nova York: John Wiley and Sons, Inc., 1961) H. G. Forder, Geometria (Londres: Hutchinson's University Library, 1950).

Bachi, R., “Standard Distance Measures and Related Methods for Spatial Analysis,” Papers, Regional Science Association, Vol. 10 (1963), pp. 83 - 132.

Veja, D. M. Y. Somerville, Uma introdução à geometria de N dimensões (Nova York: Dover Publications, 1958) também, H. H. Harman, Análise Fatorial Moderna (Chicago: The University of Chicago Press, 1962), Capítulo 4.

K. J. Dueker, "Spatial Data Systems",Relatórios Técnicos Nos. 4, 5, 6, Sistemas de Informação Urbana e de Transporte (Evanston, Ill .: Departamento de Geografia, Northwestern University, 1966).

Para outras discussões sobre dados espaciais e sistemas de coordenadas, consulte W. R. Tobler, "Geographical Coordinate Computations: Part I: General Considerations",Relatório Técnico No. 2, Tarefa ONR nº 389–137, Contrato Nonr 1224 (48) (Ann Arbor: Departamento de Geografia, Universidade de Michigan, 1964) M. B. Teitz, Informações sobre o uso do solo para o governo da Califórnia: classificação e inventário (Berkeley: Instituto de Desenvolvimento Urbano e Regional, 1965) D. F. Cooke e W. H. Maxfield, O desenvolvimento de um arquivo de base geográfica e seus usos para mapeamento (Artigo apresentado na reunião anual da Urban and Regional Information Systems Association, Nova York, 1967) T. Hägerstrand, “The Computer and the Geographer,”Transações e documentos, Institute of British Geographers, No. 42 (1967), pp. 1-19.

J. S. Bendat e A. G. Piersol, Medição e análise de dados aleatórios (Nova York: John Wiley and Sons, Inc., 1966), Capítulo 1.

J. W. Harbaugh e F. W. Preston, "Fourier Series Analysis in Geology", em B. J. L. Berry e D. F. Marble (Eds.), Análise espacial (Englewood Cliffs, N. J .: Prentice-Hall, Inc., 1968), pp. 218-38.

Harbaugh e Preston, op. cit., nota de rodapé 12, p. 233.

Horn, L. H. e Bryson, R. A., "Análise Harmônica da Marcha Anual de Precipitação", Anais, Associação de Geógrafos Americanos, Vol. 50 (1960), pp. 157-71 Sabbagh, M. E. e Bryson, R. A., "Aspectos da Climatologia de Precipitação do Canadá Investigados pelo Método de Análise Harmônica", Annals, Association of American Geographers, Vol. 52 (1962), pp. 426-40 Peixoto, J. P., Saltzman, B., e Teweles, S., "Análise Harmônica da Topografia ao Longo dos Paralelos da Terra", Journal Geophysical Research, Vol. 69 (1964), pp. 1501-05 ver também, Stone, R. O. e Dugundji, J., "A Study of Microrelief - Its Mapping, Classification, and Quantification by Means of a Fourier Analysis," Engineering Geology, Vol. 1 (1965), pp. 89-187.

Casetti, E., "Analysis of Spatial Association by Trigonometric Polynomials", Canadian Geographer, Vol. 10 (1966), pp. 199-204.

W. R. Tobler, "Numerical Map Generalization and Notes on the Analysis of Geographical Distributions",Documento de discussão nº 8 (Ann Arbor: Comunidade Interuniversitária de Geógrafos Matemáticos de Michigan, 1966).

Warntz, W. e Neft, D. S., "Contributions to a Statistical Methodology for Areal Distributions", Journal of Regional Science, Vol. 2 (1960), pp. 47-66 D. S. Neft, "Statistical Analysis for Areal Distributions",Série de Monografias, No. 2 (Philadelphia: Regional Science Research Institute, 1967).

W. Warntz, Rumo a uma geografia de preço: um estudo em geoeconometria (Filadélfia: University of Pennsylvania Press, 1959) W. Warntz, “Macrogeography and Income Fronts,”Série de Monografias, No. 3 (Philadelphia: Regional Science Research Institute, 1965).

Este é o caso, por exemplo, de Clark, C., “Urban Population Densities,” Journal Royal Statistical Society, Series A, Vol. 114 (1951), pp. 490-96 Berry, B. J. L., Simmons, J. W., e Tennant, R. J., "Urban Population Densities: Structure and Change," Geographical Review, Vol. 53 (1963), pp. 389-405.

M. F. Dacey, "A Stochastic Model of Economic Regions",Documento de Discussão No. 4 (Evanston, Ill .: Departamento de Geografia, Northwestern University, 1965).

Veja A. Tribunal, Distribuições populacionais e autopotencial (Northridge, Califórnia: Departamento de Geografia, San Fernando Valley State College, 1966, mimeo.) Gurevich, B. L., "The Density of Population of a City and the Density of Probability of a Random Magnitude," Soviet Geography, Vol. 8 (1967), pp. 722-30.

Dacey, M. F., "Two-Dimensional Random Point Patterns: a Review and an Interpretation," Papers, Regional Science Association, Vol. 13 (1964), pp. 41-55.

Dacey, M. F., “Modified Poisson Probability Law for Point Pattern More Regular than Random,” Annals, Association of American Geographers, Vol. 54 (1964), pp. 559-65.

Dacey, M. F., "Order Distance in an Inhomogeneous Random Point Pattern", Canadian Geographer, Vol. 9 (1965), pp. 144-153.

Dacey, M. F., "A Compound Probability Law for a Pattern More Dispersed than Random and with Areal Inhomogeneity," Economic Geography, Vol. 42 (1966), pp. 172-79.

D. Harvey, "Alguns problemas metodológicos no uso do tipo A de Neyman e as distribuições de probabilidade binomial negativa para a análise de padrões de pontos espaciais",Transações e documentos, Institute of British Geographers, No. 44 (1968), pp. 85-99 M. F. Dacey, Um estudo empírico da distribuição regional de casas em Porto Rico (Evanston, Ill .: Departamento de Geografia, Northwestern University, mimeo. 1967).

Por exemplo, Kulldorff, G., “Migration Probabilities,” Lund Studies in Geography, Series B, Vol. 14 (1955), 45 pp Morrill, R. L. e Pitts, F. R., "Casamento, Migração e o Campo de Informação Média: Um Estudo em Unicidade e Generalidade", Anais, Associação de Geógrafos Americanos, Vol. 57 (1967), pp. 401-22.

Para os resultados relativos à distribuição gama de distâncias, consulte, Dacey, M. F., “Order Neighbor Statistics for a Class of Random Patterns in Multidimensional Space,” Annals, Association of American Geographers, Vol. 53 (1963), pp. 505-15 os outros resultados são dados em Relatórios Técnicos, Nos. 3, 5, 6, Geographical Information Systems Project (Evanston, III .: Departamento de Geografia, Northwestern University, 1965).

Veja P. Greig-Smith, Ecologia Quantitativa de Plantas (London: Butterworths, 1964, 2ª edição.) Holgate, P., “Testes de Randomness Baseado em Métodos de Distância,” Biometrika, Vol. 52 (1965), pp. 345-53 O. Persson, "Distance Methods",Studia Forestalia Suecica, No. 15, 1964, 68 pp Burns, M. A., “On the Spatial Distribution of Foraminifera,” Contributions from the Cushman Foundation for Foraminiferal Research, Vol. 19 (1968), pp. 1-11 W. G. Warren, Contribuições para o estudo dos processos de pontos espaciais (Dissertação de Ph.D. não publicada, University of North Carolina, 1962) Fairthrone, D., "The Distances Between Random Points in Two Concentric Circles", Biometrika, Vol. 51 (1964), pp. 275-77.

Ver, por exemplo, Goodall, D. W., “Plotless Tests of Interspecific Association,” Journal of Ecology, Vol. 53 (1965), pp. 197-210.

Ver, por exemplo, Berg, W. F., “Aggregates in One and Two-Dimensional Random Distributions,” Philosophical Magazine Series 7, Vol. 36 (1945), p. 337 Mack, C., "The Expected Number of Aggregates in a Random Distribution of n Points," Proceedings Cambridge Philosophical Society, Vol. 46 (1950), pp. 285-92 Naus, J., “Clustering of Random Points in Two Dimensions,” Biometrika, Vol. 52 (1965), pp. 263-67.

Bendat e Piersol, op. cit., nota de rodapé 11, p. 19. A função dada aqui provavelmente deve ser referida, em sentido estrito, como uma função de autocovariância, ver os comentários sobre este ponto por Curry, L., “Central Places in the Random Spatial Economy,” Journal of Regional Science, Vol. 7 (1967), pág. 220

Matern, B., “Spatial Variation,” Meddelanden Fran Statens Skogsforskningsinstitut, Vol. 49 (1960), 144 pp.

W. R. Tobler, O Espectro dos EUA 40 (Ann Arbor: Departamento de Geografia, Universidade de Michigan, 1967, mimeo.) R. A. Bryson e J. A. Dutton, "The Variance Spectra of Certain Natural Series", em W. L. Garrison e D. F. Marble (Eds.), Geografia Quantitativa (Evanston, Ill .: Northwestern University, Department of Geography, 1967), pp. 1-24.

Whittle, P., "On the Variation of Yield Variance with Plot Size", Biometrika, Vol. 43 (1956), pp. 337-43 Whittle, P., "Topographic Correlation, Power-law Covariance Functions, and Diffusion", Biometrika, Vol. 49 (1962), pp. 305-14.

Bendat e Piersol, op. cit., nota de rodapé, 11, p. 22

Ver, por exemplo, D. R. Cox e P. A. W. Lewis, A Análise Estatística de Série de Eventos (Londres: Methuen and Co. Ltd., 1966) G. M. Jenkins e D. G. Watts, Análise espectral e suas aplicações (São Francisco: Holden-Day, Inc., 1968).

Pincus, M. J. e Dobrin, M. B. Geological Application of Optical Data Processing, ”Journal of Geophysical Research, Vol. 71 (1966), pp. 4861-69 Bauer, A., Fontanel, A., e Grau, G., "The Application of Optical Filtering in Coherent Light to the Study of AerialPhotos of Greenland Glaciers," Journal of Glaciology, Vol. 6 (1967), pp. 781-93.

Tobler, op. cit., nota de rodapé 16. Uma discussão mais recente das funções de ponderação linear como filtros de frequência discretos é descrita em W. R. Tobler, “Of Maps and Matrices,”Journal of Regional Science, Vol. 7, No. 2, Suplemento (1967), pp. 275–80.

Dougherty, E. L. e Smith, S. T. The Use of Linear Programming to Filter Digitized Map Data, ”Geophysics, Vol. 31 (1966), pp. 253-59.

Darby, E. K. e Davies, E. B. The Analysis and Design of Two-Dimensional Filters for Two-Dimensional Data, ”Geophysical Prospecting, Vol. 15 (1967), pp. 383-406.

Veja, por exemplo, suas declarações: Curry, L., “A Note on Spatial Association,” The Professional Geographer, Vol. 18 (1966), pp. 97-99 Curry, L., “Quantitative Geography,” The Canadian Geographer, Vol. 2 (1967), pp. 265 - 79 J. N. Rayner, "Correlation Between Surfaces by Spectral Methods", em D. F. Merriam e N. C. Cooke (Eds.), "Computer Applications in the Earth Sciences: Colloquium on Trend Analysis,"Contribuição de computador 12 (Kansas Geological Survey, 1967), pp. 31-37 W. R. Tobler, "Spectral Analysis of Spatial Series",Processos, Quarta Conferência Anual sobre Sistemas e Programas de Informação de Planejamento Urbano (Berkeley, 1966), pp. 179-85.

R. A. Bryson e J. A. Dutton, "The Variance Spectra of Certain Natural Series",op. cit., nota de rodapé 34 Rayner, J. N., “Um Modelo Estatístico para a Descrição Explanatória do Tempo em Grande Escala e Clima Espacial,” Canadian Geographer, Vol. 11 (1967), pp. 67-86 Tobler, op. cit., nota de rodapé 34.

Por exemplo, Bartlett, M. S., "The Spectral Analysis of Two-Dimensional Point Processes", Biometrika, Vol. 51 (1964), pp. 299 - 311 Gudmundsson, G., "Spectral Analysis of Magnetic Surveys", The Geophysical Journal, Vol. 13 (1967), pp. 325-37 Hannan, E. J., “Spectral Analysis for Geophysical Data,” The Geophysical Journal, Vol. 11 (1966), pp. 225-36 Leese, J. A. e Epstein, E. S. Aplicação da Análise Espectral Bidimensional para a Quantificação de Fotografias de Nuvem de Satélite, ”Journal of Applied Meteorology, Vol. 2 (1963), pp. 629-44 Pierson, W. F. et al, "O espectro de direção de um mar gerado pelo vento conforme determinado a partir de dados obtidos pelo projeto de observação de ondas estéreo", New York University Meteorological Papers, vol. 2 (1960) F. W. Preston, "Two-Dimensional Power Spectra for Classification of Land Forms", em D. F. Merriam (Ed.), "Computer Applications in the Earth Sciences Colloquium on Classification Procedures,"Contribuição de computador 7 (Kansas Geological Survey, 1966), pp. 64-69 Priestely, M. B., "The Analysis of Two-Dimensional Stationary Processes with Discontinuous Spectra", Biometrika, Vol. 51 (1964), pp. 195-217.

Tobler, op. cit., nota de rodapé 34.

Clark, C., “Urban Population Densities,” Journal Royal Statistical Society Series A, Vol. 114 (1951), pp. 490-96 Berry, B. J. L. et al, “Urban Population Densities: Structure and Change,” Geographical Review, Vol. 53 (1963), pp. 389-405.

B. E. Newling, População urbana: a matemática da estrutura e dos processos (Artigo apresentado na reunião da Association of American Geographers, St. Louis, 1967).

W. C. Krumbein e F. A. Graybill, Uma introdução aos modelos estatísticos em geologia (Nova York: McGraw-Hill Book Co., 1965), pp. 319-57.

F. K. Hare, "A Representação Quantitativa dos Campos de Pressão Polar Norte", em Simpósio da Atmosfera Polar: Parte I, Meteorologia (Nova York: Pergamon Press, 1958).

R. J. Chorley e P. Haggett, "Trend Surface Mapping in Geographical Research,"Transações e documentos, Institute of British Geographers, No. 37 (1965), pp. 47-67.

P. Gould, “On Mental Maps,”Documento de Discussão No. 9 (Ann Arbor: MICMAG, 1966) K. J. Fairbairn e G. Robinson, Uma aplicação de mapeamento de superfície de tendência para a distribuição de resíduos de uma regressão (Melbourne, Austrália: Monash University, 1967 mimeo.) P. Haggett, Mapeamento de superfície de tendência em comparações inter-regionais (Artigo apresentado em European R. S. A., The Hague, 1967).

W. R. Tobler, Comentários sobre a análise de tendências geográficas (Ann Arbor: Departamento de Geografia, Universidade de Michigan, 1968, mimeo.).

E. Casetti e R. K. Semple, "A Method for the Stepwise Separation of Spatial Trends",Documento de Discussão No. 11 (Ann Arbor: MICMAG, 1968).

Casetti e Semple, op. cit., nota de rodapé 53, p. 4

W. Warntz, "Distance and Land Values ​​as Data for Introducing Problems Associated with Spatially Continuous Fields of Correlation Coefficients,"Artigos de Harvard em Geografia Teórica, No. 10 (1968).

Orloci, L., "Modelos geométricos em Ecologia I," Journal of Ecology, Vol. 54 (1966), pp. 193-2015.

J. Imbrie, "Factor and Vector Analysis Programs for Analyzing Geologic Data,"Relatório Técnico No. 6, Computer Applications in the Earth Sciences Project (Evanston, Ill .: Departamento de Geografia, Northwestern University, 1963).

P. R. Gould, "On the Geographical Interpretation of Eigenvalues",Transações e documentos, Institute of British Geographers, No. 42 (1967), pp. 53-86.

Kutzbach, J. E., "Empirical Eigenvectors of Sea-Level Pressure, Surface Temperature and Precipitation Complexes over North America," Journal of Applied Meteorology, Vol. 6 (1967), pp. 791-802.

R. N. Shepard e J. D. Carroll, "Parametric Representation of Nonlinear Data Structures", em P. R. Krishnaiah (Ed.), Análise multivariada (New York: Academic Press, 1966), pp. 561–92.

A. Rosenfeld, “Image Processing,”Proc. Terceira Conferência Anual sobre Sistemas e Programas de Informação de Planejamento Urbano (Chicago, 1965), pp. 49-52 ver também, G. C. Cheng et al (Eds.), Reconhecimento pictórico de padrões (Washington, D. C .: Thompson Book Co., 1968) A. G. Arkadev e E. M. Braverman, Computadores e reconhecimento de padrões (Washington, D. C .: Thompson Book Co., 1967).

P. Haggett, "Network Models in Geography", em R. J. Chorley e P. Haggett (Eds.), Modelos em Geografia (London: Methuen and Co. Ltd., 1967), pp. 656–62.

Tobler, W., "Geographical Filters and their Inverses," Geographical Analysis, Vol. 1 (1969) J. Rayner, A Aplicação das Técnicas de Fourier em Geografia (Artigo apresentado na reunião anual, Association of American Geographers, Washington, D. C., 1968).

ver Ward, J. H., "Hierarchical Grouping to Optimize an Objective Function", Journal American Statistical Association, Vol. 58 (1963), pp. 236-44 Friedman, H. P. e Rubin, J. On Some Invariant Criteria for Grouping Data, ”Journal American Statistical Association, Vol. 62 (1967), pp. 1159-78 B. J. L. Berry, "The Mathematics of Economic Regionalization,"Processos, Conferência de Brno sobre Regionalização Econômica (Brno, 1967) Lankford, P. M., “Regionalização: Teoria e Algoritmos Alternativos,” Análise Geográfica, Vol. 1 (1969).

T. W. Anderson, Introdução à análise estatística multivariada (Nova York: John Wiley and Sons, Inc., 1958), cap. 6

G. S. Sebestyen, Processos de tomada de decisão no reconhecimento de padrões (Nova York: The Macmillan Co., 1962) W. H. Highleyman, "Linear Decision Functions with Application to Pattern Recognition",Processos IRE (1962), pp. 1501-14 E. Casetti, "Classificatory and Regional Analysis by Discriminant Iterations",Relatório Técnico No. 12, Computer Applications in the Earth Sciences Project (Evanston, Ill .: Dept. of Geography, Northwestern University, 1964).

M. F. Dacey, "The Geometry of Central Place Theory",Geografiska Annaler, Vol. 47B (1965), pp. 111–24.

Dacey, op. cit., nota de rodapé 67, p. 113

M. J. Woldenberg, "Spatial Order in Fluvial Systems: Horton's Laws Derived from Mixed Hexagonal Hierarchies of Drainage Basin Areas",Artigos de Harvard em Geografia Teórica, No. 13 (1968) Woldenberg, M. J., “Energy Flow and Spatial Order. Mixed Hexagonal Hierarchies of Central Places ”, Geographical Review, Vol. 58 (1968), pp. 552-74.

Renyi, A., "On a One-dimensional Problem Concerning Random Space-Filling", Selected Translations in Mathematical Statistics and Probability, Vol. 4 (1963) Capobianco, M. F., "Using Probabilities to Compare Parking Disciplines", Traffic Quarterly, Vol. 22 (1968), pp. 137-44.

Gilbert, E. N., "The Probability of Covering a Sphere with N Circular Caps", Biometrika, Vol. 52 (1965), pp. 323-30 Marcus, A. H., "A Multivariate Immigration with Multiple Death Process and Applications to Lunar Craters", Biometrika, Vol. 54 (1967), pp. 251-61 Marcus, A. H., “A Stochastic Model of the Formation and Survival of Lunar Craters, I. The Distribution of Diameter of Clean Craters,” Icarus, Vol. 3 (1964), pp. 460-72 problemas de empacotamento também foram discutidos em Moran, P. A. P., “A Note on Recent Research in Geometrical Probability,” Journal Applied Probability, Vol. 3 (1966), pp. 453-63.

Por exemplo, Marcus, A. H., “A Stochastic Model of the Formation and Survival of Lunar Craters, II. Approximate Distribution of Diameter of All Observable Craters, ”Icarus, Vol. 5 (1966), pp. 165-77.

P. R. Gould, "Space Searching Procedures in Geography and the Social Sciences",Documentos de Trabalho No. 1 (Honolulu: Social Science Research Institute, Univ. Of Hawaii, 1966).

Guter, R. S., "On the Probability of Detecting a Region by a Linear Search," Theory of Probability and its Applications, Vol. 9 (1964), pp. 331-33.

Wolpert chamou a atenção em alguns de seus escritos sobre conflitos e decisões de localização para problemas como o impacto diferenciado sobre grupos residenciais de localizações de vias expressas J. Wolpert, Saídas do Ambiente Usual na Análise Locacional (Artigo apresentado no Kansas Center for Regional Studies, Universidade de Kansas, 1967) questões de busca e probabilidades de contato podem ser relevantes neste contexto.

J. Neyman e E. L. Scott, "A Stochastic Model of Epidemics", em J. Gurland (Ed.), Modelos estocásticos em medicina e biologia (Madison: Univ. Of Wisconsin Press, 1964), pp. 45-83.

L. Brown, "Models for Spatial Diffusion Research - A Review",Relatório Técnico No. 3, Spatial Diffusion Study (Evanston, Ill.: Dept. of Geography, Northwestern University, 1965).

Haggett, op. cit., footnote 62.

P. Haggett, “An Extension of the Horton Combinatorial Model to Regional Highway Networks,”Journal of Regional Science, Vol. 7, No. 2, Supplement (1967), pp. 281–90.

Werner, C. , “ The Law of Refraction in Transportation Geography: Its Multivariate Extension ,” Canadian Geographer , Vol. 12 ( 1968 ), pp. 28 – 40 .

M. F. Dacey, “Description of Line Patterns,” in Garrison and Marble, op. cit., footnote 34, pp. 277–87.

Gauthier, H. L. , “ Transportation and the Growth of the São Paulo Economy ,” Journal of Regional Science , Vol. 8 ( 1968 ), pp. 77 – 94 .

See for example, Smith, T. E. , “ Spatial Stochastic Process Models—A Method of Analyzing Spatial Point Phenomena ,” Papers and Proceedings Japan Section R. S. A. , Vol. 2 ( 1967 ), pp. 19 – 30 .

J. C. Hudson, Maps and Spatial Processes Describable with Markov Chains (Grand Forks: Dept. of Geography, Univ. of North Dakota, 1966, mimeo.).

D. W. Harvey, “Geographical Processes and the Analysis of Point Patterns,”Transactions and Papers, Institute of British Geographers, No. 40 (1966), p. 93

Morgan, R. W. and Welsh, D. J. A. , “ A Two-Dimensional Poisson Growth Process ,” Journal Royal Statistical Society Ser. B , Vol. 27 ( 1965 ), pp. 497 – 504 .

M. A. Geisler, W. W. Haythorn, and W. A. Steger, “Simulation and the Logistics Systems Laboratory,”Rand Memorandum, RM-3281-PR (1962), p. 6

T. Hägerstrand, Innovationsförloppet ur Korologisk Synpunkt (Lund, Sweden: C. W. K. Gleerup, 1953) W. L. Garrison, “Toward Simulation Models of Urban Growth and Development,”Lund Studies in Geography B, No. 24 (1962), pp. 91–108.

For example, Morrill, R. L. , “ The Development of Spatial Distributions of Towns in Sweden: An Historical-Predictive Approach ,” Annals , Association of American Geographers, Vol. 53 ( 1963 ), pp. 1 – 14 E. J. Taaffe et al, The Peripheral Journey to Work: A Geographic Consideration (Evanston, Ill.: North-western University Press, 1963).

Skellam, J. G. , “ Random Dispersal in Theoretical Populations ,” Biometrika , Vol. 38 ( 1951 ), pp. 196 – 218 .

See for example, A. E. Scheidegger, Theoretical Geomorphology (Berlin: Springer-Verlag, 1961).

K. E. F. Watt, Ecology and Resource Management (New York: McGraw-Hill Book Co., 1968), p. 261.

For an example of the use of such systems in a spatial in a problem, see, J. S. de Cani, “On the Construction of Stochastic Models of Population Growth and Migration,”Journal of Regional Science, Vol. 3, No. 2 (1961), pp. 1–13.

Newling, op. cit., footnote 47.

E. Casetti and G. J. Demko, “A Diffusion Model of Fertility Decline: An Application to Selected Soviet Data, 1940–65,” forthcoming in Demografia. An alternative approach in which the coefficients of a logistic growth model are written as functions of distance is illustrated in, Casetti, E. and Semple, R. K. , “ Concerning the Testing of Spatial Diffusion Hypotheses ,” Geographical Analysis , Vol. 1 ( 1969 ).

Hyperbolic geometry is associated mainly with the work of Bolyai and Lobachevsky elliptic geometry with that of Gauss, Riemann, and Klein. H. S. M. Coxeter, Non-Euclidean Geometry (Toronto: University of Toronto Press, 3rd ed., 1967).

See, Coxeter, op. cit., footnote 96 H. Meschkowski, Noneuclidean Geometry (New York: Academic Press, Inc., 1964) S. Kulczycki, Non-Euclidean Geometry (New York: Pergamon Press, Inc., 1961).

P. G. Bergmann, The Riddle of Gravitation (New York: Charles Scribner's Sons, 1968), p. 68.

Bergmann, op. cit., footnote 98, p. 66, notes that in the Minkowski universe, “Lorentz frames are the analogs of the Cartesian coordinate systems of ordinary geometry. Their axes, both space and time, are all straight lines. …”

See J. L. Coolidge, A History of Geometrical Methods (Oxford: Clarendon Press, 1940, and New York: Dover Publication, Inc., 1963), pp. 355–56.

Coolidge, op. cit., footnote 100, pp. 355–87.

Coolidge, op. cit., footnote 100, pp. 410–16.

R. K. Luneburg, Mathematical Analysis of Binocular Vision (Princeton: Princeton University Press, 1947).

Luneburg, op. cit., footnote 103.

Blank, A. A. , “ Axiomatics of Binocular Vision. The Foundations of Metric Geometry in Relation to Space Perception,” and “Analysis of Experiments in Binocular Space Perception ,” Journal Optical Society of America , Vol. 48 ( 1958 ), pp. 328 – 34 , 911–28 Roberts, F. and Suppes, P. Some Problems in the Geometry of Visual Perception ,” Synthese , Vol. 17 ( 1967 ), pp. 173 – 201 Hoffman, W. C. , “ The Lie Algebra of Visual Perception ,” Journal of Mathematical Psychology , Vol. 3 ( 1966 ), pp. 65 – 98 .

M. D. Vernon (Ed.), Experiments in Visual Perception (Harmondsworth, England: Penguin Books, Ltd., 1966).

G. F. White (Ed.), “Papers on Flood Problems,”Research Paper No. 70 (Chicago: Department of Geography, University of Chicago, 1961) R. W. Kates, “Stimulus and Symbol: The View from the Bridge,”Journal of Social Issues, Vol. 22, No. 4 (1966), pp. 21–28 T. F. Saarinen, “Perception of the Drought Hazard on the Great Plains,”Research Paper No. 106 (Chicago: Department of Geography, University of Chicago, 1966) J. Wolpert, “Migration as an Adjustment to Environmental Stress,”Journal of Social Issues, Vol. 22, No. 4 (1966), pp. 92–102 D. Lowenthal (Ed.), “Environmental Perception and Behavior,”Research Paper No. 109 (Chicago: Department of Geography, University of Chicago, 1967).

K. Lynch, The Image of The City (Cambridge: M.I.T. Press, 1960) Gould, op. cit., footnote 51.

Tobler, W. R. , “ Geographic Area and Map Projections ,” Geographical Review , Vol. 53 ( 1963 ), pp. 59 – 78 .

See for example, Kruskal, J. B. , “ Multidimensional Scaling by Optimizing Goodness of Fit to a Nonmetric Hypothesis ,” Psychometrika , Vol. 29 ( 1964 ), pp. 1 – 27 Shepard, R. N. , “ The Analysis of Proximities: Multidimensional Scaling with an Unknown Distance Function ,” Psychometrika , Vol. 27 ( 1962 ), pp. 125 – 40 , 219–46 Shepard, R. N. , “ Metric Structures in Ordinal Data ,” Journal of Mathematical Psychology , Vol. 3 ( 1966 ), pp. 287 – 315 W. S. Torgerson, Theory and Methods of Scaling (New York: John Wiley and Sons, Inc., 1958) Torgerson, W. S. , “ Multidimensional Scaling of Similarity ,” Psychometrika , Vol. 30 ( 1965 ), pp. 379 – 93 see also, Attneave, F. , “ Dimensions of Similarity ,” American Journal of Psychology , Vol. 63 ( 1950 ), pp. 516 – 56 Krantz, D. H. , “ Rational Distance Functions for Multidimensional Scaling ,” Journal of Mathematical Psychology , Vol. 4 ( 1967 ), pp. 226 – 45 .

See G. H. Hardy et al, Desigualdades (Cambridge, England: The University Press, 1952).

Shepard and Carroll, op. cit., footnote 60.

Hare, op. cit., footnote 49.

Hannan, op. cit., footnote 44 Lee, W. H. K. and Kaula, W. M. , “ A Spherical Harmonic Analysis of the Earth's Topography ,” Journal of Geophysical Research , Vol. 72 ( 1967 ), pp. 753 – 58 .

Kaula, W. M. , “ Theory of Statistical Analysis of Data Distributed over a Sphere ,” Reviews of Geophysics , Vol. 5 ( 1967 ), pp. 83 – 107 .

Tobler, op. cit., footnote 109, p. 59.

Tobler, op. cit., footnote 109, p. 59.

W. R. Tobler, The Geometry of Geography (Presentation at Faculty-Graduate Seminar, Univ. of Michigan, Winter 1968).

A. Delachet, Contemporary Geometry (New York: Dover Publications, Inc., 1962), p. 47

Delachet, op. cit., footnote 119, pp. 63–66.

Bunge, op. cit., footnote 1, pp. 183–95.

G. Olsson, “Geography 1984,”Bristol Seminar Paper Series, No. 7A (1967).

Dacey, M. F. , “ A Probability Model for Central Place Locations ,” Annals , Association of American Geographers, Vol. 56 ( 1966 ), pp. 550 – 68 .

Harvey, op. cit., footnote 3, p. 8

Dacey, M. F. , “ A County-Seat Model for the Areal Pattern of an Urban System ,” Geographical Review , Vol. 56 ( 1966 ), p. 542 .

L. Curry, “Central Places in the Random Spatial Economy,”Journal of Regional Science, Vol. 7, No. 2, Supplement (1967), p. 219.

See for example, R. G. Golledge, The Geographical Relevance of Some Learning Theories (Paper presented at Association of American Geographers, annual meeting, Washington, D. C., 1968) Wolpert, op. cit., footnote 75.

For example, Casetti, E. , “ Urban Population Density Patterns: An Alternate Explanation ,” Canadian Geographer , Vol. 11 ( 1967 ), pp. 96 – 100 R. G. Golledge, “Conceptualizing the Market Decision Process,”Journal of Regional Science, Vol. 7, No. 2, Supplement (1967), pp. 239–58.

Curry, op. cit., footnote 126, p. 236.

L. Curry, Geographical Dynamics and the Central Place Problem (Paper given at NSF Conference on Urban Geography Models, Cincinnati, 1968).

T. Hägerstrand, Innovation Diffusion as a Spatial Process, translated by A. Pred (Chicago: University of Chicago Press, 1967).

Dacey, op. cit., footnote 125, and op. cit., footnote 123.

A. Pred, “Behavior and Location,”Lund Studies in Geography, No. 27B (1967).

Pred, op. cit., footnote 133, p. 31

It is informative to review some of the more recent work in ecology in which there are analogous attempts, although somewhat greater progress, at theorizing about animal behavior and spatial patterns. See, R. H. MacArthur and E. O. Wilson, “The Theory of Island Biogeography,”Monographs in Population Biology, No. 1 (Princeton, N. J.: Princeton University Press, 1967) R. Levins, “Evolutions in Changing Environments, Some Theoretical Explorations,”Monographs in Population Biology, No. 2 (Princeton, N. J.: Princeton University Press, 1968).

Abstrato

ABSTRACT An interest in the relations between geography and geometry has been a continuing theme in geographic studies. A review of current research which is consistent with this theme reveals a greater emphasis now on the mathematics of spatial patterns and the properties of the geometries which are assumed. A challenge for geography is to combine this interest in geometries with the work on human behavior over space and to develop process theories from which the spatial patterns can be deduced.


The Geography of Logistics Firm Location: The Role of Accessibility

The organization of modern economies is built upon an efficient transport system and an increasing role is played by the logistics sector in overcoming the constraints of time and distance in modern supply chains. While a large body of literature is dedicated to the spatial distribution of firms and firm location choice in general, surprisingly little is still known about the location patterns of logistics firms, and more specifically about the role of accessibility in their location decisions. We use geo-referenced firm level data along with detailed information on transport infrastructure in order to investigate the geography of logistics firms in Spain. We place specific attention to the relationship between logistics firm location, accessibility, and urban structure. Our results show that these firms are located closer to highways and other transport infrastructure compared to other sectors and that the logistics sector is highly urbanized. Yet, they are also locating increasingly in suburban locations and to some extent in extra-urban locations with good accessibility while central cities of urban areas have experienced a declining share of logistics firms.

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Data residency in Azure

Azure has more global regions than any other cloud provider—offering the scale and data residency options you need to bring your apps closer to your users around the world.

As a customer, you maintain ownership of customer data—the content, personal and other data you provide for storing and hosting in Azure services. You are also in control of any additional geographies where you decide to deploy your solutions or replicate your data.

Where a service's functionality requires global data replication, details are available below.

Microsoft secures your data using multiple layers of security and encryption protocols. Get an overview of how Microsoft uses encryption to secure your data.

By default, Microsoft Managed Keys protect your data, and customer data that persists on any physical media is always encrypted using FIPS 140-2 compliant encryption protocols. Customers can also employ customer-managed keys (CMK), double encryption and/or hardware security modules (HSM) for increased data protection.

All data traffic moving between datacenters is protected using IEEE 802.1AE MAC Security Standards, preventing physical "man-in-the-middle" attacks. To maintain resiliency, Microsoft uses variable network paths that sometimes cross Geo boundaries but replication of customer data between regions is always transmitted over encrypted network connections.

Additionally, to minimize privacy risk, Microsoft generates pseudonymous identifiers that enable Microsoft to offer a global cloud service (including operating and improving services, billing, and fraud protection). In all cases, pseudonymous identifiers cannot be used to directly identify an individual, and access to the customer data that identifies individuals is always protected as described above.

All Azure services can be used in compliance with the GDPR. If customers using Azure services choose to transfer content containing personal data across borders, they will need to consider the legal requirements that apply to such transfers. Microsoft provides customers with services and resources to help them comply with GDPR requirements that may apply to their operations.

Some Microsoft online services share data with third parties acting as its subprocessors. The publicly disclosed Microsoft Online Services Subprocessors List identifies subprocessors authorized to process customer data or personal data. All such subprocessors are contractually obligated to meet or exceed the contractual commitments Microsoft makes to its customers.

Microsoft will not provide any third party (a) direct, blanket, or unfettered access to customers' data (b) platform encryption keys used to secure data or the ability to break such encryption or (c) access to data if Microsoft is aware that the data is to be used for purposes other than those stated in the third party's request. Further information on Microsoft’s approach to legal disclosure of customer data in relation to government demands is available here.

Most Azure services enable you to specify the region where your customer data will be stored and processed. Microsoft may replicate to other regions for data resiliency, but Microsoft will not store or process customer data outside the selected Geo. You and your users may move, copy, or access your customer data from any location globally.

Datacenter region Localização Available to
Ásia leste Hong Kong All customers and partners
Sudeste da Ásia Cingapura All customers and partners

In some cases, data for certain services may be stored outside of specified regions. See Additional information on this page for details.

Stored at rest in the Asia Pacific region. See Additional information for details.

To learn about product availability in the Asia Pacific geo, go to Products available by region and select Asia Pacific from the Regions dropdown menu.

Datacenter region Localização Available to
Australia Central Canberra All customers and partners
Australia Central 2 Canberra For Australia Central customers requiring in-country disaster recovery
Australia East Nova Gales do Sul All customers and partners
Australia Southeast Victoria All customers and partners

In some cases, data for certain services may be stored outside of specified regions. See Additional information on this page for details.

Stored at rest in Australia. See Additional information for details.

To learn about product availability in the Australia geo, go to Products available by region and select Australia from the Regions dropdown menu.

In some cases, data for certain services may be stored outside of specified regions. See Additional information on this page for details.

Stored at rest in Austria. See Additional information for details.

Datacenter region Localização Available to
Brazil South São Paulo State All customers and partners
Brazil Southeast Rio de Janeiro Reserved for Brazil South customers requiring scenario-based in-country disaster recovery

In some cases, data for certain services may be stored outside of specified regions. See Additional information on this page for details.

Brazil South: Data replication to the US.

Brazil Southeast: Data replication to the Brazil South.

To learn about product availability in the Brazil geo, go to Products available by region and select Brazil from the Regions dropdown menu.

Datacenter region Localização Available to
Canada Central Toronto All customers and partners
Canada East cidade de Quebec All customers and partners

In some cases, data for certain services may be stored outside of specified regions. See Additional information on this page for details.

Stored at rest in Canada. See Additional information for details.

To learn about product availability in the Canada geo, go to Products available by region and select Canada from the Regions dropdown menu.

Datacenter region Localização Available to
Índia Central Pune All customers and partners
Sul da Índia Chennai All customers and partners
Índia Ocidental Mumbai All customers and partners

In some cases, data for certain services may be stored outside of specified regions. See Additional information on this page for details.

Stored at rest in India. See Additional information for details.

To learn about product availability in the India geo, go to Products available by region and select India from the Regions dropdown menu.

In some cases, data for certain services may be stored outside of specified regions. See Additional information on this page for details.

Stored at rest in Chile. See Additional information for details.

Datacenter region Localização Available to
China East Xangai Organizations with a business presence in China
China East 2 Xangai Organizations with a business presence in China
China East 3 Jiangsu Organizations with a business presence in China
China North Pequim Organizations with a business presence in China
China North 2 Pequim Organizations with a business presence in China
China North 3 Hebei Organizations with a business presence in China

In some cases, data for certain services may be stored outside of specified regions.

Azure in China is a separate service sold and operated by 21Vianet. For customers of Azure operated by 21Vianet, this map outlines the locations of datacenters where 21Vianet stores customer data. 21Vianet may replicate customer data in at least two datacenters for data resiliency and availability but always within China. The exceptions below for regional and non-regional services do not apply.

In some cases, data for certain services may be stored outside of specified regions. See Additional information on this page for details.


The 13 Types Of Data

Data types are forking and splintering out as fast as lightening.

Adrian Brophy @ Xtrashot Photographic

Data is a thorny subject. For a start, we’re not sure how we are supposed to refer to it, that is - data is the plural of datum. Strictly speaking we should talk about data that ‘are’ not ‘is’ available to support a theory etc. The Guardian newspaper discussed the debate here and appeared to suggest that (split infinitives and nuances of idiomatic Latin notwithstanding) our day-to-day usage of the term is allowed to remained conveniently grammatically incorrect.

“For what it's worth, I can confidently say that this will probably be the only time I ever write the word ‘datum’ in a [blog] post. Data as a plural term may be the proper usage, but language evolves and we want to write in terms that everyone understands - and that don't seem ridiculous,” wrote Simon Rogers, in 2012, before moving to his position as data editor at Google.

So of the many different instances of individual datum (sorry, data) that exist, can we group them into distinct types, categories, varieties and classifications? In this world of so-called digital transformation and cloud computing that drives our always-on über-connected lifestyles, surely it would be useful to understand the what, when, where and why of data on our journey to then starting to appreciate the how factor.

1 - Big data

A core favorite, big data has arisen to be defined as something like: that amount of data that will not practically fit into a standard (relational) database for analysis and processing caused by the huge volumes of information being created by human and machine-generated processes.

“While definitions of ‘big data’ may differ slightly, at the root of each are very large, diverse data sets that include structured, semi-structured and unstructured data, from different sources and in different volumes, from terabytes to zettabytes. It’s about data sets so large and diverse that it’s difficult, if not impossible, for traditional relational databases to capture, manage, and process them with low-latency,” said Rob Thomas, general manager for IBM Analytics.

Thomas suggests that big data is a big deal because it’s the fuel that drives things like machine learning, which form the building blocks of artificial intelligence (AI). He says that by digging into (and analyzing) big data, people are able to discover patterns to better understand why things happened. They can also then use AI to predict how they may happen in the future and prescribe strategic directions based on these insights.

2 - Structured, unstructured, semi-structured data

All data has structure of some sort. Delineating between structured and unstructured data comes down to whether the data has a pre-defined data model and whether it’s organized in a pre-defined way.

Mat Keep is senior director of products and solutions at MongoDB. Keep explains that, in the past, data structures were pretty simple and often known ahead of data model design -- and so data was typically stored in the tabular row and column format of relational databases.

“However, the advance of modern web, mobile, social, AI, and IoT apps, coupled with modern object-oriented programming, break that paradigm. The data describing an entity (i.e. a customer, product, connected asset) is managed in code as complete objects , containing deeply nested elements . The structure of those objects can vary (polymorphism) – i.e. some customers have a social media profile that is tracked, and some don’t. And, with agile development methodologies, data structures also change rapidly as new application features are built,” said Keep.

As a result of all this polymorphism today, many software developers are looking towards more flexible alternatives to relational databases to accommodate data of any structure.

3 - Time-stamped data

Time-stamped data is a dataset which has a concept of time ordering defining the sequence that each data point was either captured (event time) or collected (processed time).

“This type of data is typically used when collecting behavioral data (for example, user actions on a website) and thus is a true representation of actions over time. Having a dataset such as this is invaluable to data scientists who are working on systems that are tasked with predicting or estimating next best action style models, or performing journey analysis as it is possible to replay a user's steps through a system, learn from changes over time and respond,” said Alex Olivier, product manager at marketing personalization software platform company Qubit.

4 - Machine data

Simply put, machine data is the digital exhaust created by the systems, technologies and infrastructure powering modern businesses.

Matt Davies, head of EMEA marketing at Splunk asks us to paint a picture and imagine your typical day at work, driving to the office in your connected car, logging on to your computer, making phone calls, responding to emails, accessing applications. Davies explains that all this activity creates a wealth of machine data in an array of unpredictable formats that is often ignored.

“Machine data includes data from areas as varied as application programming interfaces (APIs), security endpoints, message queues, change events, cloud applications, call detail records and sensor data from industrial systems,” said Davies. “Yet machine data is valuable because it contains a definitive, real time record of all the activity and behavior of customers, users, transactions, applications, servers, networks and mobile devices.”

If made accessible and usable, machine data is argued to be able to help organizations troubleshoot problems, identify threats and use machine learning to help predict future issues.

5 - Spatiotemporal data

Spatiotemporal data describes both location and time for the same event -- and it can show us how phenomena in a physical location change over time.

“Spatial data is the ‘spatio’ in spatiotemporal. It can describe point locations or more complex lines such as vehicle trajectories, or polygons (plane figures) that make up geographic objects like countries, roads, lakes or building footprints,” explained Todd Mostak, CEO of MapD.

Temporal data contains date and time information in a time stamp. Valid Time is the time period covered in the real world. Transaction Time is the time when a fact stored in the database was known.

“Examples of how analysts can visualize and interact with spatiotemporal data include: tracking moving vehicles, describing the change in populations over time, or identifying anomalies in a telecommunications network. Decision-makers can also run backend database calculations to find distances between objects or summary statistics on objects contained within specified locations,” said MapD’s Mostak.

6 - Open data

Open data is data that is freely available to anyone in terms of its use (the chance to apply analytics to it) and rights to republish without restrictions from copyright, patents or other mechanisms of control. The Open Data Institute states that open data is only useful if it’s shared in ways that people can actually understand. It needs to be shared in a standardized format and easily traced back to where it came from.

“Wouldn’t it be interesting if we could make some private data [shapes, extrapolated trends, aggregate values and analytics] available to the world without giving up the source and owner identification of that data? Some technologies are emerging, like multi-party computation and differential privacy that can help us do this,” said Mike Bursell, chief security architect at Red Hat.

Bursell explains that these are still academic techniques at the moment, but over the next ten years he says that people will be thinking about what we mean by open data in different ways. The open source world understands some of those questions and can lead the pack. The Red Hat security man says that it can be difficult for organizations that have built their business around keeping secrets. They now have to look at how they open that up to create opportunities for wealth creation and innovation.

7 - Dark data

Dark data is digital information that is not being used and lies dormant in some form.

Analyst house Gartner Inc. describes dark data as, "Information assets that an organization collects, processes and stores in the course of its regular business activity, but generally fails to use for other purposes."

8 - Real time data

One of the most explosive trends in analytics is the ability to stream and act around real time data. Some people argue that the term itself is something of a misnomer i.e. data can only travel as fast as the speed of communications, which isn’t faster than time itself… so, logically, even real time data is slightly behind the actual passage of time in the real world. However, we can still use the term to refer to instantaneous computing that happens about as fast as a human can perceive.

“Trends like edge computing and the impending rise of 5G are gaining their momentum based upon the opportunities thrown up by real time data. The power of immediacy with data is going to be the catalyst for realizing smart cities,” said Daniel Newman, principal analyst at Chicago-based Futurum Research.

Newman says that real time data can help with everything from deploying emergency resources in a road crash to helping traffic flow more smoothly during a citywide event. He says that real time data can also provide a better link between consumers and brands allowing the most relevant offers to be delivered at precise moments based upon location and preferences. “Real time data is a real powerhouse and its potential will be fully realized in the near term,” added Newman.

9 - Genomics data

Bharath Gowda, vice president for product marketing at Databricks points at genomics data as another area that needs specialist understanding. Genomics data involves analysing the DNA of patients to identify new drugs and improve care with personalized treatments.

He explains, ”The data involved [in genomics] is huge - by 2020 genomic data is expected to be orders of magnitude greater than the data produced by Twitter and YouTube. The first genome took over a decade to assemble. Today, a patient’s genome can be sequenced in a couple of days. However, generating data is the easy part. Turning data into insight is the challenge. The tools used by researchers cannot handle the massive volumes of genomic data.”

What are the issues here? According to Gowda, data processing and downstream analytics are the new bottlenecks that stop us getting more value out of genomic data. So what makes genomic data different?

“It requires significant data processing and needs to be blended with data from hundreds of thousands of patients to generate insights. Furthermore, you need to look at how you can unify analytics workflows across all teams - from the bioinformatics professional prepping data to the clinical specialist treating patients - in order to maximize its value,” said Gowda.

10 - Operational data

Colin Fernandes is product marketing director for EMEA region at Sumo Logic. Fernandes says that companies have big data, they have application logs and metrics, they have event data, and they have information from microservices applications and third parties.

The question is: how can they turn this data into business insights that decision makers and non-technical teams can use, in addition to data scientists and IT specialists?

“This is where operational analytics comes into play,” said Fernandes. “Analyzing operational data turns IT systems data into resources that employees can use in their roles. What’s important here is that we turn data from a specialist resource into assets that can be understood by everyone, from the CEO to line of business workers, whenever they have a decision to make.”

Fernandes points out that in practice, this means looking at new applications and business goals together to reverse engineer what your operational data metrics should be. New customer-facing services can be developed on microservices, but how do we make sure we extract the right data from the start? By putting this ‘operational data” mindset in place, we can arguably look at getting the right information to the right people as they need it.

11 - High-dimensional data

High-dimensional data is a term being popularized in relation to facial recognition technologies. Due to the massively complex number of contours on a human face, we need new expressions of data that are multi-faceted enough to be able to handle computations that are capable of describing all the nuances and individualities that exist across out facial physiognomies. Related to this is the concept of eigenfaces, the name given to a set of eigenvectors when they are used in computing to process human face recognition.

12 - Unverified outdated data

The previously quoted Mike Bursell of Red Hat also points to what he calls unverified outdated data. This is data that has been collected, but nobody has any idea whether it's relevant, accurate or even of the right type. We can suggest that in business terms, if you're trusting data that you haven't verified, then you shouldn't be trusting any decisions that are made on its basis. Bursell says that Garbage In, Garbage Out still holds… and without verification, data is just that: garbage.

“Arguably even worse that unverified data, which may at least have some validity and which you should at least know that you shouldn't trust, data which is out-of-date and used to be relevant. But many of the real-world evidence from which we derive our data changes, and if the data doesn't change to reflect that, then it is positively dangerous to use it in many cases,” said Bursell.

13 - Translytic Data

An amalgam of ‘transact’ and ‘analyze’, translytic data is argued to enable on-demand real-time processing and reporting with new metrics not previously available at the point of action. This is the opinion of Mark Darbyshire, CTO for data and database management at SAP UK.

Darbyshire says that traditionally, analysis has been done on a copy of transactional data. But today, with the availability of in-memory computing, companies can perform ‘transaction window’ analytics. This he says supports tasks that increase business value like intelligent targeting, curated recommendations, alternative diagnosis and instant fraud detection as well as providing subtle but valuable business insights.

According to SAP’s Darbyshire, “Translytic data requires a simplified technology architecture and hybrid transactional analytic database systems, which are enabled by the in-memory technology. This also provides the added benefit of simplicity of architecture – one system to maintain with no data movement. Companies who transact in real time with instant insight into the relevant key metrics that matter while they transact experience increased operational efficiency as well as faster access and improved visibility into its real-time data.”

This list is by no means meant to be exhaustive, such is the nature of information technology and the proliferation of data


Generate Random Numbers

Use rand , randi , randn , and randperm to create arrays of random numbers.

This example shows how to create an array of random floating-point numbers that are drawn from a uniform distribution in a specific interval.

This example shows how to create an array of random integer values that are drawn from a discrete uniform distribution on a specific set of numbers.

This example shows how to create an array of random floating-point numbers that are drawn from a normal distribution having a specified mean and variance.

This example shows how to create random points within the volume of a sphere.

Avoid repetition of random number arrays when MATLAB ® restarts.

Replace Discouraged Syntaxes of rand and randn .

Control Random Number Generation

This example shows how to use the rng function, which provides control over random number generation.

This example shows how to repeat arrays of random numbers by specifying the seed first. Every time you initialize the generator using the same seed, you always get the same result.

This example shows how to avoid repeating the same random number arrays when MATLAB restarts.

Control Multiple Streams or Substreams

This example shows how to use the RandStream class to control random number generation from the global stream.

This example uses RandStream to create multiple, independent random number streams.

This example shows how to use RandStream to create random number streams and substreams.


Spatial pattern analysis of manufacturing industries in Keraniganj, Dhaka, Bangladesh

Understanding industrial clustering and its patterns of development are important steps in linking regional policy development, strategic decision making, business site management, and fostering a country’s economic growth. A considerable variety of common location-based cluster measures are available in practice, including area-based measures and a variety of indicators based on analyses of point data. This study uses the geostatistical approaches kernel density, multi-distance Reply’s-K, and spatial autocorrelation, both global Moran’s-I and local Moran’s-I, to assess the degree of spatial clustering of manufacturing locations in Keranignaj, located at the southern periphery of the urban region of Dhaka, Bangladesh. Results indicated a non-random pattern for all manufacturing locations in the study region. Small-scale industries such as garment manufacturing, metal, and brick making have a strong presence in Keranignaj. Expansion of such industries were highly associated with proximity to a river, while food processing, rubber and plastics manufacturing industries were clustered in relation to road proximity. The spatial association Global Moran’s-I with higher positive coefficient value indicates homogeneity, or spatial auto-correlation, exist in the industrial locations studied here. Local Moran’s-I, which documents regional clustering, has yielded a statistically significant manufacturing cluster (0.05 level) for the manufacturing areas of Zinjira, Kaliganj, Mirerbagh, and Chunkutia. Since cluster-based economic development has recently been an area of increasing interest for both developed and developing nations, the outcomes from this study provide an insight into spatial processes of industrial development in Bangladesh, and the Dhaka area in particular, enabling planners and policymakers to make rational, informed decisions and strengthening the economic growth and capacity for development of micro-industries clusters for the area studied here and the region beyond.

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