Résumé : The slides were taken on collecting trips sponsored by the William L. Bryant Foundation, where books, music and art indigenous to the regions were gathered. The are organized by geographical location.
Archaeologist Ripley Pierce Bullen and his wife, bioanthropologist Adelaide Kendall Bullen exiting caves in New Providence, Bahamas. Ripley Bullen was the curator emeritus at the Florida State Museum, currently the Florida Museum of Natural History, at the University of Florida. Adelaide Bullen was the Research Associate in Anthropology at the Florida State Museum. Slide labeled Cave on North Coast N. P. April '65.
Bahamas -- North America -- Nassau, New Providence Island

Droits : All rights to images are held by the respective holding institution. This image is posted publicly for non-profit educational uses, excluding printed publication. For permission to reproduce images and/or for copyright information contact Special Collections & University Archives, University of Central Florida Libraries, Orlando, FL 32816 phone (407) 823-2576, email: speccoll@mail.ucf.edu
CFM1972_01a
Sheet 13:13
http://ufdc.ufl.edu/AA00029234/00001

Résumé : The slides were taken on collecting trips sponsored by the William L. Bryant Foundation, where books, music and art indigenous to the regions were gathered. The are organized by geographical location.
In the foreground are archaeologist Ripley Pierce Bullen and his wife, bioanthropologist Adelaide Kendall Bullen. Ripley Bullen was the curator emeritus at the Florida State Museum, currently the Florida Museum of Natural History, at the University of Florida. Adelaide Bullen was the Research Associate in Anthropology at the Florida State Museum. In the background of the restaurant is a painting of a Bahamian market scene which includes fruit, fish, straw vendors, shoppers, a police officer, and surrey carriage. Slide labeled Bullens at Nassau Restaurant, April '65.
Bahamas -- North America -- New Providence Island

Droits : All rights to images are held by the respective holding institution. This image is posted publicly for non-profit educational uses, excluding printed publication. For permission to reproduce images and/or for copyright information contact Special Collections & University Archives, University of Central Florida Libraries, Orlando, FL 32816 phone (407) 823-2576, email: speccoll@mail.ucf.edu
CFM1972_01a
Sheet 17:18
http://ufdc.ufl.edu/AA00029537/00001

Éditeur(s) : HAL CCSD
Résumé : JEL : C40 ; C60 ; R12 ; L60
This study introduces two new measures of spatial concentration. The proposed M functions constitute an extension to Ripley's functions (Ripley, 1976, 1977). They allow the evaluation of the relative geographic concentration and co-location of industries in a non-homogeneous spatial framework. Some rigorous comparisons with similar recently developed tools prove the relevance of the M functions in the field of spatial economics.
https://halshs.archives-ouvertes.fr/halshs-00372617

halshs-00372617
https://halshs.archives-ouvertes.fr/halshs-00372617
https://halshs.archives-ouvertes.fr/halshs-00372617/document
https://halshs.archives-ouvertes.fr/halshs-00372617/file/HAL_Marcon_Puech_-_MeasuresOfTheGeographicConcentrationOfIndustriesImprovingDistanceBasedMethods.pdf

Éditeur(s) : HAL CCSD Elsevier
Résumé : International audience
Voting rules relying on k-nearest neighbors (k-NN) are an effective tool in countless many machine learning techniques. Thanks to its simplicity, k-NN classification is very attractive to practitioners, as it enables very good performances in several practical applications. However, it suffers from various drawbacks, like sensitivity to "noisy" instances and poor generalization properties when dealing with sparse high-dimensional data. In this paper, we tackle the k-NN classification problem at its core by providing a novel k-NN boosting approach. Namely, we propose a supervised learning algorithm, called Universal Nearest Neighbors (UNN), that induces a leveraged k-NN rule by globally minimizing a surrogate risk upper bounding the empirical misclassification rate over training data. Interestingly, this surrogate risk can be arbitrary chosen from a class of Bregman loss functions, including the familiar exponential, logistic and squared losses. Furthermore, we show that UNN allows to efficiently filter a dataset of instances by keeping only a small fraction of data. Experimental results on the synthetic Ripley's dataset show that such a filtering strategy is able to reject "noisy" examples, and yields a classification error close to the optimal Bayes error. Experiments on standard UCI datasets show significant improvements over the current state of the art.
ISSN: 0925-2312

hal-00664462
https://hal.inria.fr/hal-00664462

Éditeur(s) : HAL CCSD
Résumé : For ecology scientists, spatial statistics are a set of tools allowing characterization of the structure of a point set, for instance a map of trees in a forest plot. This structure is defined implicitly as a departure from complete spatial randomness (CSR), the result of a Poisson point process. Inhomogeneity of the process and non-independence of points are its causes. As they cannot be disentangled by analyzing the data, so-called "spatial concentration" or "aggregation" (evenness due to repulsion also exists but is actually rare) usually concerns non-independence while intensity is assumed as known. The literature of point processes and measures of spatial structures is reviewed to clarify concepts and choices. The purpose of this thesis was to produce methodological enhancements. Its main results are: * A test for the most used statistic, Ripley's K function, against the null hypothesis of CSR. It can replace Monte-Carlo simulations used until now in the literature. * An extension of K to inhomogeneous processes, within a typology of available statistics (absolute, relative and topographic measures). When data is available as number of objects per zones (say a number of tree in each forest plot) rather than their exact position, information theory is used to define a general framework allowing to characterize spatial structure (of species) and diversity (of plots) as two aspects of a single measure of inequality. This framework is applied to Shannon's index of biodiversity to derive a self-sufficient definition of beta diversity, its calculation independently from the difference between gamma and alpha diversity, and a statistical test of significance against the null hypothesis that plots are samples of the same community. The way is paved for other applications to measures of diversity and spatial structures. As a conclusion, it appears clear that pattern characterization tools are a first step to deal with ecological questionings. Their development is still a matter of research. Yet, they are far from sufficient to address ecological processes as point processes ignore the temporal aspect of object locations.
Les statistiques spatiales sont pour les écologues un ensemble d'outils permettant de caractériser la structure d'un semis de points, par exemple une carte représentant des emplacements des arbres dans une parcelle de forêt. Cette structure est définie implicitement comme un écart à semis complètement aléatoire, résultat d'un processus de Poisson. L'hétérogénéité du processus ponctuel dont le semis de points est une réalisation et la non-indépendance des points en sont les causes indiscernables, ce qui amène généralement à supposer l'intensité du processus connu et nommer " concentration spatiale " ou " agrégation " (la régularité spatiale est possible, mais rare en pratique) la non-indépendance. Une revue de la littérature des processus ponctuels et des mesures de structures spatiales est fournie pour clarifier les concepts et les choix. L'objectif de ce travail de thèse était de produire des améliorations méthodologiques. Ses résultats principaux sont : * L'établissement d'un test pour la principale statistique utilisée, la fonction K de Ripley, permettant de s'affranchir de la méthode de Monte-Carlo utilisée dans la littérature pour rejeter l'hypothèse nulle d'un processus complètement aléatoire. * L'extension de K aux processus hétérogènes, dans le cadre d'une typologie claire des statistiques (absolues, relatives, topographiques). Lorsque la position exacte des objets n'est pas connue, mais que des effectifs par zone sont disponibles (par exemple des nombres d'arbres par parcelle), la théorie de l'information est utilisée pour définir un cadre général permettant de caractériser la structure spatiale (des espèces dans les parcelles) et la diversité (des parcelles, en termes d'espèces) comme deux aspects d'une même mesure d'inégalité. Ce cadre est appliqué à l'indice de biodiversité de Shannon pour définir clairement la mesure de diversité bêta, son calcul direct indépendamment de la différence entre diversités gamma et alpha, et fournir un test statistique de non nullité. La voie est ouverte pour l'application à d'autres mesures de diversité et de structure spatiale. En conclusion, il semble clair que ces outils de caractérisation sont un premier pas pour traiter les questions écologiques, leur développement étant toujours du domaine de la recherche. Ils sont cependant très insuffisants pour répondre à des questions liées aux processus écologiques, assez éloignés des processus ponctuels qui ignorent l'aspect temporel de l'installation des objets.
https://pastel.archives-ouvertes.fr/pastel-00540327

NNT : 2010AGPT0075
pastel-00540327
https://pastel.archives-ouvertes.fr/pastel-00540327
https://pastel.archives-ouvertes.fr/pastel-00540327/document
https://pastel.archives-ouvertes.fr/pastel-00540327/file/13_Marcon2010.pdf

Éditeur(s) : HAL CCSD Hindawi Publishing Corporation
Résumé : International audience
We generalize Ripley's K function to get a new function, M, to characterize the spatial structure of a point pattern relatively to another one. We show that this new approach is pertinent in ecology when space is not homogenous and the size of objects matters. We present how to use the function and test the data against the null hypothesis of independence between points. In a tropical tree data set we detect intraspecific aggregation and interspecific competition.
ISSN: 1687-9708

hal-01502623
https://hal-agroparistech.archives-ouvertes.fr/hal-01502623
DOI : 10.1155/2012/619281

Éditeur(s) : HAL CCSD
Résumé : International audience
Ripley’s K function is the classical tool to characterize the spatial structure of point patterns. It is widely used in vegetation studies. Testing its values against a null hypothesis usually relies on Monte-Carlo simulations since little is known about its distribution.We introduce a statistical test against complete spatial randomness (CSR). The test returns the p-value to reject the null hypothesis of independence between point locations. It is more rigorous and faster than classical Monte-Carlo simulations. We show how to apply it to a tropical forest plot. The necessary R code is provided.
International Scholarly Research Network, ISRN Ecology

hal-01502637
https://hal-agroparistech.archives-ouvertes.fr/hal-01502637
https://hal-agroparistech.archives-ouvertes.fr/hal-01502637/document
https://hal-agroparistech.archives-ouvertes.fr/hal-01502637/file/2013_Marcon_Hindawi%20Publishing%20Corporation_%7B7A602F34-6983-47DE-924B-7CDC3B8D14F7%7D.pdf
DOI : 10.1155/2013/753475

Éditeur(s) : HAL CCSD
Résumé : In spatial statistics, Ripley's K function (Ripley, 1977) is a classical tool to analyse spatial point patterns. Yet, it faces two major limits: it is only pertinent for homogeneous point processes and it does not allow the weighting of points.
We generalize it to get a new function, M, which oversteps these limits and detects spatial structures of inhomogeneous populations of weighted points.
https://halshs.archives-ouvertes.fr/halshs-00372631

halshs-00372631
https://halshs.archives-ouvertes.fr/halshs-00372631
https://halshs.archives-ouvertes.fr/halshs-00372631/document
https://halshs.archives-ouvertes.fr/halshs-00372631/file/HAL_Marcon_Puech_-_GeneralizingRipleysKFunctiontoInhomogeneousPopulations.pdf

Éditeur(s) : HAL CCSD Wiley
Résumé : International audience
Questions: Several studies have described aggregated spatial patterns in tropical tree species. This study investigates aggregate dynamics of Vouacapoua americana (Aublet), a climax species whose spatial pattern is not simply related to light and soil conditions or to its short seed dispersal range. Location: Two rain forest sites: Nouragues and Paracou, in the Guiana Shield. Methods: We described the spatial pattern of tree locations and spatial autocorrelation of tree diameters, using statistics derived from Ripley's K. We particularly used methods to define analysis subplots according to local density or local mean diameter. We investigated relationships between spatial distributions of adults and saplings. Results: At both sites, populations of Vouacapoua demonstrated several nested levels of aggregation. Tree diameters were spatially autocorrelated, revealing the existence of clusters with similar diameters. In the largest aggregates, tree diameters declined from the centre to the edge. Regeneration was aggregated and occurred mainly at cluster edges and around rare isolated trees, and sapling densities and basal area of adults were negatively correlated. We show that long-distance dispersal events are rare. Conclusions: Environmental factors and seed dispersal only explain part of the observed spatial patterns. We provide two main hypotheses about Vouacapoua population dynamics. First, the lack of regeneration in aggregate centres results in the ageing of existing aggregates. We suggest that this lack of recruitment close to mature trees is due to a Janzen-Connell effect. However, aggregates can continue to grow along colonization fronts. Second, long-distance dispersal events allow the formation of new clusters and play a crucial role in the colonization process. We investigate aggregate dynamics of Vouacapoua americana (Aublet) whose spatial pattern is not simply related to environmental conditions or to its seed dispersal. Regeneration does not occur in centers of aggregate of adults. We suggest that rare long-distance dispersal events and density-dependence predation of seeds and seedlings play a crucial role in formation of new clusters and structuration of larger aggregates.
ISSN: 1100-9233

hal-01502642
https://hal-agroparistech.archives-ouvertes.fr/hal-01502642
DOI : 10.1111/jvs.12080

Éditeur(s) : HAL CCSD EDP Sciences
Résumé : International audience
Aggregation patterns are often visually detected in sets of location data. These clusters may be the result of interesting dynamics or the effect of pure randomness. We build an asymptotically Gaussian test for the hypothesis of randomness corresponding to a homogeneous Poisson point process. We first compute the exact first and second moment of the Ripley K-statistic under the homogeneous Poisson point process model. Then we prove the asymptotic normality of a vector of such statistics for different scales and compute its covariance matrix. From these results, we derive a test statistic that is chi-square distributed. By a Monte-Carlo study, we check that the test is numerically tractable even for large data sets and also correct when only a hundred of points are observed
ISSN: 1292-8100

hal-01502640
https://hal-agroparistech.archives-ouvertes.fr/hal-01502640
DOI : 10.1051/ps/2012027

Éditeur(s) : HAL CCSD Springer Verlag
Résumé : under revision for IJCV
International audience
The k-nearest neighbors (k-NN) classification rule has proven extremely successful in countless many computer vision applications. For example, image categorization often relies on uniform voting among the nearest prototypes in the space of descriptors. In spite of its good properties, the classic k-NN rule suffers from high variance when dealing with sparse prototype datasets in high dimensions. A few techniques have been proposed to improve k-NN classification, which rely on either deforming the nearest neighborhood relationship or modifying the input space. In this paper, we propose a novel boosting algorithm, called UNN (Universal Nearest Neighbors), which induces leveraged k-NN, thus generalizing the classic k-NN rule. We redefine the voting rule as a strong classifier that linearly combines predictions from the k closest prototypes. Weak classifiers are learned by UNN so as to minimize a surrogate risk. A major feature of UNN is the ability to learn which prototypes are the most relevant for a given class, thus allowing one for effective data reduction. Experimental results on the synthetic two-class dataset of Ripley show that such a filtering strategy is able to reject "noisy" prototypes. We carried out image categorization experiments on a database containing eight classes of natural scenes. We show that our method outperforms significantly the classic k-NN classification, while enabling significant reduction of the computational cost by means of data filtering.
ISSN: 0920-5691

hal-00481712
https://hal.archives-ouvertes.fr/hal-00481712
DOI : 10.1007/s11263-012-0539-2
ARXIV : 1001.1221

Éditeur(s) : HAL CCSD University of California, Los Angeles
Résumé : International audience
The dbmss package for R provides an easy-to-use toolbox to characterize the spatial structure of point patterns. Our contribution presents the state of the art of distance-based methods employed in economic geography and which are also used in ecology. Topographic functions such as Ripley’s K, absolute functions such as Duranton and Overman’s Kd and relative functions such as Marcon and Puech’s M are implemented. Their confidence envelopes (including global ones) and tests against counterfactuals are included in the package
ISSN: 1548-7660

hal-01498361
https://hal-agroparistech.archives-ouvertes.fr/hal-01498361
DOI : 10.18637/jss.v067.c03

Éditeur(s) : HAL CCSD Oxford University Press (OUP): Policy F
Résumé : JEL classification: C40, C60, L60, R12
We propose new methods for evaluating the spatial distribution of firms. To assess whether firms are concentrated or dispersed, economists have tradi-tionally used indices that analyse the heterogeneity of a spatial structure at a single geographic level. We introduce distance-based methods, Besag's L function (derived from Ripley's K function) and Diggle and Chetwynd's D function to describe simultaneously spatial distribution at different geographical scales. Our empirical applications consider the distribution of French manufacturing firms in the Paris area and in France generally. For some geographic levels, results show significant concentration or dispersion of firms according to their sector of activity.
ISSN: 1468-2702

halshs-00372646
https://halshs.archives-ouvertes.fr/halshs-00372646

Éditeur(s) : HAL CCSD Oxford University Press (OUP): Policy F
Résumé : International audience
We discuss a property of distance-based measures that has not been addressed with regard to evaluating the geographic concentration of economic activities. The article focuses on the choice between a probability density function of point-pair distances or a cumulative function. We begin by introducing a new cumulative function, M, for evaluating the relative geographic concentration and the co-location of industries in a non-homogeneous spatial framework. Secondly, some rigorous comparisons are made with the leading probability density function of Duranton and Overman (2005), Kd. The merits of the simultaneous use of Kd and M is proved, underlining the complementary nature of the results they provide.
Journal of Economic Geography

hal-01094082
https://hal-agroparistech.archives-ouvertes.fr/hal-01094082
DOI : 10.1093/jeg/lbp056

Éditeur(s) : HAL CCSD
Résumé : International audience
La statistique de Ripley a été introduite pour mesurer l'agrégation des points d'un processus ponctuel. Elle a été très fréquemment utilisée notamment par les écologues, mais peu de résultats théoriques ont été obtenus; nous étudions les propriétés (biais, variance) de cet estimateur sous l'hypothèse d'un processus de Poisson homogène et prouvons un théorème de la limite centrale lorsque les données sont obtenues sur un carré de taille nxn avec n tendant vers l'infini. Nous en déduisons un test asymptotiquement gaussien de l'hypothèse de processus de Poisson homogène.
41èmes Journées de Statistique, SFdS, Bordeaux
Bordeaux, France, France

inria-00386800
https://hal.inria.fr/inria-00386800
https://hal.inria.fr/inria-00386800/document
https://hal.inria.fr/inria-00386800/file/p243.pdf