130113 os mworkshop_presentation
TRANSCRIPT
はじめに 解析手順 データの可視化 おわりに
RによるOSMのタグ情報解析と可視化
縫村崇行 (名古屋大学)
OpenStreetMapワークショップ#5(名古屋工業大学 2013/01/13)
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はじめに 解析手順 データの可視化 おわりに
自己紹介
専門：GISや RSによるヒマラヤの氷河の変動把握
所属：名大・環境学・雪氷圏変動研究室 (理農館)
OSGeo1財団日本支部運営委員
主な業務内容氷河の空間分布 DB作成
ヒマラヤでフィールド調査 (DGPS)
3Dモニターを使ってステレオ写真測量
研究室で GISや R言語の指導
1The Open Source Geospatial Foundation2 / 18
はじめに 解析手順 データの可視化 おわりに
自己紹介 (メイン業務以外の活動)
FOSS4Gツール勉強会＠名古屋を主催(第 1回 2011/12/11、第 2回 2012/4/14、第 3回 2012/12/15)
FOSS4Gaとは：QGIS、GRASS GIS、GMTや Rなど、オープンソースの空間解析ソフトウェア
URL：https://sites.google.com/site/foss4gnagoya/
aFree and Open Source Software for Geospatial
Nagoya.R (東海地方を中心とした Rの勉強会)
名大の言語系の研究者の方が主催
URL：http://corpus-study.info/nagoyar/
世界各地の都市で同様のイベント(ex. Tokyo.R、Tsukuba,R、London.R)
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はじめに 解析手順 データの可視化 おわりに
Rとは
http://cran.r-project.org/
コマンドラインベースで様々な統計解析が可能
多くの追加機能 (パッケージ)が開発されている
今回は拡張機能の osmarパッケージと ggplot2パッケージを使用。
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はじめに 解析手順 データの可視化 おわりに
OpenStreetMapデータの構成
基本要素
OSMデータは 3種類の基本要素 (Node、Way、Relation)で構成されており、それらには様々なタグ情報が付けられている
Node Way Relation
http://wiki.openstreetmap.org/wiki/JA:Data_Primitives
タグ
タグとは Keyと Valueの組み合わせとして付けられている情報。
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はじめに 解析手順 データの可視化 おわりに
Rの osmarパッケージについて
osmarパッケージを使うと OpenStreetMapデータをベクター形式のまま Rに読み込むことができる。
=⇒ベクター形式なので様々な空間解析や属性情報 (≃タグ)解析が可能
その他にも"OpenStreetMap"というパッケージがある
しかしこちらはラスター形式で読み込むため、属性情報は含まれず空間情報解析もできない。背景図の利用としては便利かも。
=⇒詳しくは OSMワークショップ#1での発表資料を参照下さいhttp://www.slideshare.net/nuimura/120630-os-mworkshoppresentation
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はじめに 解析手順 データの可視化 おわりに
データ読み込み
名古屋大学 (136.97◦ E, 35.154◦ N)を中心に、3000 m × 3000 mの範囲2をダウンロードして読み込む場合
1 library(osmar)23 nagoya_bb <- center_bbox(136.97, 35.154, 3000, 3000)4 nagoya <- get_osm(nagoya_bb, source=osmsource_api())
nagoyaという名前の変数としてデータが読み込まれる
2厳密にはその範囲に一部でも重なる地物7 / 18
はじめに 解析手順 データの可視化 おわりに
osmarオブジェクトのデータ構造
osmarオブジェクトは 3つのスロットをもつnodes
attrs: id,lat,lon,user,uid,visible,version,changeset,timestamptags: id,k,v
waysattrs: id,user,uid,visible,version,changeset,timestamptags: id,k,vrefs: id,ref
relationsattrs: id,user,uid,visible,version,changeset,timestamptags: id,k,vrefs: id,type,ref,role
地物と様々な情報を idで結びつけたリレーショナル DB
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はじめに 解析手順 データの可視化 おわりに
任意のデータの抽出の例
sourceタグの値が bingの地物 idをまずリストアップしてから、idにもとづいて該当地物の抽出
1 bing_id <- find(2 nagoya, way(tags(k == "source" & v == "bing")))34 bing_nagoya <- subset(5 nagoya, ids = find_down(nagoya, way(bing_id)))
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はじめに 解析手順 データの可視化 おわりに
任意のデータの抽出の例
sourceタグの値が surveyの地物 idをまずリストアップしてから、idにもとづいて該当地物の抽出
1 survey_id <- find(2 nagoya, way(tags(k == "source" & v == "survey")))34 survey_nagoya <- subset(5 nagoya, ids = find_down(nagoya, way(survey_id)))
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はじめに 解析手順 データの可視化 おわりに
タグ情報の可視化 (データソース)
source=bingを青、source=suveyを緑で地図の表示
1 plot(nagoya)2 plot_ways(bing_nagoya, col = "blue", add = TRUE)3 plot_ways(survey_nagoya, col = "green", add = TRUE)
136.92 136.94 136.96 136.98 137.00
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11 / 18
はじめに 解析手順 データの可視化 おわりに
タグ情報の可視化 (統計値)
ユーザー別の編集量 (ノード数)で並び替えて (3–7)、ggplotで可視化 (8–9)
1 library(ggplot2)23 tb <- table(nagoya$nodes$attrs$user)4 nagoya$nodes$attrs$user <- factor(5 nagoya$nodes$attrs$user,6 levels=names(tb[order(tb, decreasing = TRUE)]))78 ggplot(nagoya.usr$nodes$attrs, aes(x=user,fill=user))9 + geom_bar() + coord_flip()
12 / 18
はじめに 解析手順 データの可視化 おわりに
タグ情報の可視化 (統計値)
count
user
Tom_G3X
nuimura
futurumspes
TKE−waka
Kindmania
anonyex
ikiya
Wingspan
nishioka
Leonidas−from−XIV
watao
hea−kun
Wim L
dey
jimmy20403
yohama
higa4
Masaru Okaya
JaTrainWikipedia
nobichan
nal
azuazu
nkawa
toranosuke
Haaninjo
ichien
makjapan Gcjp
uiro
wheelmap_visitor
yo−sun
0 500 1000 1500 2000 2500 3000 3500
user
Tom_G3X
nuimura
futurumspes
TKE−waka
Kindmania
anonyex
ikiya
Wingspan
nishioka
Leonidas−from−XIV
watao
hea−kun
Wim L
dey
jimmy20403
yohama
higa4
Masaru Okaya
JaTrainWikipedia
nobichan
nal
azuazu
nkawa
toranosuke
Haaninjo
ichien
makjapan Gcjp
uiro
wheelmap_visitor
yo−sun
13 / 18
はじめに 解析手順 データの可視化 おわりに
タグ情報の可視化 (時系列)
ユーザーの編集履歴を時系列で表示、地物の編集バージョンを色で表現3
1 ggplot(nagoya$nodes$attrs,2 aes(x=as.Date(timestamp),y=user,color=version))3 + geom_point()
3version=1は新規作成地物を意味する14 / 18
はじめに 解析手順 データの可視化 おわりに
タグ情報の可視化 (時系列)
as.Date(timestamp)
user
Tom_G3Xnuimura
futurumspesTKE−wakaKindmania
anonyexikiya
Wingspannishioka
Leonidas−from−XIVwatao
hea−kunWim L
deyjimmy20403
yohamahiga4
Masaru OkayaJaTrainWikipedia
nobichannal
azuazunkawa
toranosukeHaaninjo
ichienmakjapan Gcjp
uirowheelmap_visitor
yo−sun
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2010 2011 2012 2013
version● 2
● 4
● 6
● 8
● 10
15 / 18
はじめに 解析手順 データの可視化 おわりに
タグ情報の可視化 (空間分布)
ノードの空間分布に user=nuimuraの地物の分布濃度を表示
1 ggplot(nagoya$nodes$attrs, aes(lon, lat))2 + geom_point(size=1)3 + stat_density2d(4 data=subset(nagoya$nodes$attrs,5 user=="nuimura"),6 aes(lon, lat, fill=..level..),7 contour=T, geom="polygon", alpha=0.3)
16 / 18
はじめに 解析手順 データの可視化 おわりに
タグ情報の可視化 (空間分布)
lon
lat
35.12
35.13
35.14
35.15
35.16
35.17
35.18
35.19
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はじめに 解析手順 データの可視化 おわりに
おわりに
OpenStreetMapデータの強みとしては自由に使えることはもちろん、とりわけ詳細な属性情報を含んでいることにある。
取り扱う際にはタグ情報の文字列のゆらぎなどに注意をする必要があるが、様々な分野への応用が期待される。
18 / 18