地理空間大數(shù)據(jù)

地理信息科學(xué)進(jìn)展地理空間大數(shù)據(jù)Big

Geo-Spatial

DataBig

DataGartner

Big

data

is

high

volume,

high

velocity,

and/or

high

varietyinformation

assets

that

require

new

forms

of

processing

to

enabenhanced

decision

making,

insight

discovery

and

processoptimization.(Douglas

2012)CharacteristicsVolumeVarietyVelocityVariabilityVeracityComplexityChallengesAnalysisCaptureData

curationSearchSharingStorageTransferVisualizationInformation

privacyTools

for

Big

DataSTACKELEMENTUSED

FOROPEN

SOURCEEXAMPLESCOTS

EXAMPLESVisualizationUser

InterfaceWeb-based

ToolsGephi,D3js,OzoneTableau,

Centrifuge,Visual

AnalyticsAnalyticsMachine

learningStatistical

toolsSAS,MapR,SPSS,PalanCrData

StoreData

&

MetadataSource

DataIndexesR,Titan,Spark,Hive,HDFS,Mahout,OpenCV,Lumify,PigAccumulo,MongoDB,

Cassandra,Titan,

Neo4j,MySQLOracle,YarcData,Marklogic,TeradataIngestTransformaCon

/

NormalizaConIngest

/

Streams

ProcessingStorm,Hadoop/MapReduceSplunk,

SAS,Oracle,

IBMInfrastructure(IaaS,

PaaS)CM,

Scheduling,

MonitoringApplication

Operating

SystemsComputers,

NetworksLinux,Puppet,Oozie,HDFS,JBoss,OpenShift,OpenStack,Zookeeper,Kafka,XymonAWS,

Azure,Cloudera,

Red

Hat,Rackspace,vendor

SpecificBig

Geospatial

DataGeography

and

big

data

(Graham

2013)

Much

of

big

data

are

geographic

in

nature

and

contaeither

explicit

or

implicit

spatial

information,

…fundamentally

new

ways

of

knowing,

enacting

and

beithe

world.

Big

data

allow

us

to

objectively

measure

and

map

thworld

as

it

actually

is

in

order

to

arrive

atfundamental

truths.Big

geospatial

data4V:

Volume,

Velocity,

Variety,

Valueassociated

with

an

individualwith

spatio-temporal

tag

taxi

trajectories,

mobile

phone

records,

social

medsocial

networking

data,

smart

card

records

in

publiSocial

SensingSocial

Sensing

(Liu

2015)

Social

sensing

refers

to

a

category

of

spatio-temporally

taggedbig

data

that

provide

an

observatory

for

human

behavior,

as

welas

the

methods

and

applications

based

on

such

big

data.

The

major

objective

of

social

sensing

is

to

detect

socio-economcharacteristics

in

geographical

space

and

thus

it

can

be

vieweda

complement

to

remote

sensing.urban

area

of

Shanghaicheck-in

pointsdrop-off

pointspick-up

pointspopulation

densityfrequency

distributionsglobal

temporal

variationslocal

temporal

variationsArchitecture

of

Social

SensingApplications

of

Social

Sensing

(1)ensitiesSensing

temporal

activity

variatPiicko-unps

and

drop-offs

of

Taxi

Trips,

Shanghai

(Liu

2012)Temporal

patternsDiurnal

rhythm

of

activity

dMobile

phonecalls,

Harbin(Kang

2012)Difference

of

pick-ups

and

drop-offs

of

Taxi

Trips,

Shanghai

(Lcheck-in

activitiesin

citiesof

ChinaApplications

of

Social

Sensing

(2)ns

or

citiesSensing

spatial

interactionsspatial

interactionindividual:

social

tiescollective:

interactions

between

regiospatially-embedded

network(Liu

2014)Spatial

interaction

inside

a

cityand

Community

detectionfrom

the

network

formed

by

taxi

flows(Liu

2012)Flows

between

sub-regionAnd

the

temporal

variatioApplications

of

Social

Sensing

(3)Sensing

place

semantics

and

sentimentsFlickrphotos

in

ParisThe

200

most

frequent

tagsThe

kernel

density

estimation

of

the

geo-tagged

photosassociated

with

Eiffel

Tower

and

Seine

RiverToponym

co-occurrence

in

Web

pages,

and

Regionalization

(Liu

2014)IdentifyinghotspotsfromsocialmediadUrban

ComputingUrban

Computing

(Zheng

2014)

Urban

computing

is

a

process

of

acquisition,

integration,

andanalysis

of

big

and

heterogeneous

data

generated

by

a

diversityof

sources

in

urban

spaces,

such

as

sensors,

devices,

vehicles,buildings,

and

human,

to

tackle

the

major

issues

that

cities

fae.g.

air

pollution,

increased

energy

consumption

and

trafficcongestion.

Urban

computing

connects

unobtrusive

and

ubiquitous

sensingtechnologies,

advanced

data

management

and

analytics

models,

annovel

visualization

methods,

to

create

win-win-win

solutions

thimprove

urban

environment,

human

life

quality,

and

city

operatisystems.

Urban

computing

also

helps

us

understand

the

nature

of

urbanphenomena

and

even

predict

the

future

of

cities.Contents

of

Urban

ComputingUrban

sensing

and

data

acquisitionunobtrusively

and

continually

collect

data

in

a

citywide

scalea

variety

of

sensors:

mobile

phones,

vehicles,

cameras,

loops,

human

as

a

sensor:

user

generated

content

(check

in,

photos,tweets)trade

off

among

energy,

privacy

and

the

utility

of

the

dataloose-controlled

and

nonuniform

distributed

sensorsunstructured,

implicit,

and

noise

dataComputing

with

heterogeneous

data

sourceslearn

mutually

reinforced

knowledge

from

heterogeneous

databoth

effective

and

efficient

learning

abilitydata

management

+

mining

+

machine

LearningvisualizationHybrid

systems

blending

the

physical

and

virtual

worldsserving

both

people

and

cities

(virtually

and

physically)hybrid

systems:

mobile

+

cloud,

crowd

sourcing,

participatoryArchitecture

of

Urban

ComputingApplications

of

Urban

Computing

(1)Finding

the

underlying

problem

of

Beijing’s

road

network

using

taxi

trajectories

(Zheng

2011,

Yuan

2012)Identifying

functional

regions

in

a

city

using

human

mobility

and

POIs

(Yuan

&

Zheng

2012)Applications

of

Urban

Computing

(2)T-Drive:

driving

directions

based

on

taxi

trajectories

(Yuan

and

Zheng

2010,

2011,

2013)Improving

taxi

services.

T-Finder

(Yuan

and

Zheng

2011,

2014),

T-Share

(Ma

and

Zheng

2013])Applications

of

Urban

Computing

(3)Monitoring

real-time

and

fine-grained

air

qualityusing

big

data

(Zheng

2013,

2014)Diagnosing

the

noise

pollution

of

New

York

City

(Zheng

2014)Inferring

gas

consumption

and

pollution

emission

from

vehiclesbased

on

sparse

trajectories

(Shang

&

Zheng

2014)Applications

of

Urban

Computing

(4)Detecting

anomalies

from

urban

traffic

based

on

distance

(Liu

&

Zheng2011)時(shí)空大數(shù)據(jù)的數(shù)據(jù)模型時(shí)空位置模型錨點(diǎn)建模靜態(tài)位置、或時(shí)空過程中停留時(shí)間較長的位置P

=(x,y,T,W)或P

=(g,T,W)時(shí)空軌跡模型移動(dòng)對象模型為主體，一組離散的時(shí)空點(diǎn)序列Tr

=

(o,

{x1,y1,t1}…

{xj,yj,tj}…)時(shí)空交互模型兩個(gè)位置對象之間交互量的時(shí)間變化模型F(Pi→Pj)

=

(xi,

yi,

xj,

yj,

T,

WPi→Pj)F(Pi→Pj)

=

(gi,

gj,

T,

WPi→Pj)時(shí)空網(wǎng)絡(luò)模型點(diǎn)對象之間的時(shí)態(tài)交互映射為圖結(jié)構(gòu)G

=

(V,

E)時(shí)空位置大數(shù)據(jù)Place

&

LocationPosition

DataPoint

of

Interest

(POI)Area

of

Interest

(AOI)Place

nameTextWebSearch

EngineGeotagged

DataTextPhotoSocial

media……百度定位大數(shù)據(jù)：熱力圖GeoTagTaggingTagging

is

a

classic

way

for

users

to

annotate

user-generated

conten

The

vocabulary

system

is

entirely

flat

and

directly

reflect

the

concand

linguistic

structure

of

the

users

and

their

diverse

geographicalcultural

backgrounds

(Guy

2006)Geotag

Geotag:

contains

geographic

coordinates,

extent,

shape,

or

feature

tinformation.Geotaggingassigns

geographic

locations

to

content

(Amitay

2004

)refers

to

“tagging”

georeferenced

metadata

to

a

document

or

other

contentWikipedia

Geotaguses

single

points

and

bounding

rectanglesgeoreferenced

information

is

embedded

into

articles

using

one

of

manymicroformats

and

extensions

to

Wikitext

,

Wikipedia’s

content

markup

languagGeotagged

Social

media

dataGeotagged

TweetsGeotagged

Flickr

photosGeotagged

YouTube

videosExploring

Urban

Areas

of

InterestUrban

AOIs/SP_DEMOS/UrbanAOIsUsing

Flickr

dataGeneral

metadatalocations,

time,

photo

id,

owner

id,

server

id,…Text

tagswhat

are

people

talking

about

here?Photoswhat

are

people

looking

at

here?檢索大數(shù)據(jù)：百度旅游檢索《心花路放》熱映火了“梧桐客?！薄靶陆z綢之路經(jīng)濟(jì)帶”的發(fā)展帶動(dòng)西部旅游百度檢索大數(shù)據(jù)：預(yù)測旅游每天消費(fèi)者在百度平臺(tái)的旅游類搜索超過2200萬次覆蓋旅游人群2.12億其中無線端覆蓋人群1.77億。通過海量檢索行為，預(yù)測旅游市場趨勢國內(nèi)旅游景點(diǎn)可以提前2天預(yù)測國內(nèi)城市旅游可以提前45天預(yù)測出國旅游可以提前45天預(yù)測國內(nèi)游檢索大數(shù)據(jù)與市場數(shù)據(jù)呈現(xiàn)相同的增長趨勢兩者表現(xiàn)出強(qiáng)正相關(guān)出境游檢索數(shù)據(jù)提前預(yù)知市場數(shù)據(jù)白色：檢索數(shù)據(jù)橙色：市場數(shù)據(jù)12·31上海外灘踩踏事件分析12·31上海外灘踩踏事件2014年12月31日23時(shí)35分許，跨年夜活動(dòng)外灘陳毅廣場進(jìn)入和退出的人流對沖發(fā)生踩踏事件大數(shù)據(jù)分析（百度研究院大數(shù)據(jù)實(shí)驗(yàn)室）當(dāng)時(shí)的人流量大到什么程度？事發(fā)當(dāng)時(shí)是否是當(dāng)晚人流量最大的時(shí)候？當(dāng)時(shí)人流的對沖到底是什么樣的程度？群體聚集是突發(fā)情況，可以預(yù)警嗎？人群熱力圖人群流量趨勢圖上海外灘踩踏事件大數(shù)據(jù)分析（1）中秋前夜（2）國慶當(dāng)晚（3）跨年當(dāng)晚人群分布熱力圖人群流動(dòng)方向圖人群流動(dòng)方向分布外灘地圖搜索與人群到達(dá)數(shù)量的互相關(guān)性互相關(guān)性曲線在-1.5小時(shí)的時(shí)候達(dá)到峰值：根據(jù)地圖上相關(guān)地點(diǎn)搜索的請求量，至少可能提前幾十分鐘預(yù)測出人流量峰值的到來時(shí)空軌跡大數(shù)據(jù)按采樣方式和驅(qū)動(dòng)因素分類（李婷2014）基于等時(shí)間間隔采樣的軌跡數(shù)據(jù)，如車載GPS數(shù)據(jù)基于位置采樣的軌跡數(shù)據(jù)，如居民出行調(diào)查數(shù)據(jù)基于事件觸發(fā)的軌跡數(shù)據(jù)，如手機(jī)定位數(shù)據(jù)、公交車刷卡數(shù)據(jù)按涉及的交通出行方式分類（周濤2013）單一出行方式數(shù)據(jù)，如出租車、公交車數(shù)據(jù)等混合出行方式數(shù)據(jù)，如手機(jī)、簽到數(shù)據(jù)等按采集的位置信息格式分類基于GPS的軌跡數(shù)據(jù)，有精確的經(jīng)緯度信息，如出租車、微博簽到等

基于參考點(diǎn)的軌跡數(shù)據(jù)，如手機(jī)定位數(shù)據(jù)、公交車/地鐵刷卡數(shù)據(jù)、wifi數(shù)據(jù)等Trajectory

Data

MiningSpatial

Trajectories

Computing

Framework

(Zheng

2015)人類移動(dòng)性(human

mobility)面向人的活動(dòng)模式研究每個(gè)人的活動(dòng)如同分子運(yùn)動(dòng),看似雜亂無序,實(shí)則存在潛在的模式發(fā)現(xiàn)這種模式并揭示其影響因素,需要采集海量的時(shí)空軌跡數(shù)據(jù)基于時(shí)空軌跡大數(shù)據(jù)的移動(dòng)性研究特征量分析和特征建模時(shí)間空間研究對象個(gè)體群體驅(qū)動(dòng)機(jī)制基于移動(dòng)的模式基于活動(dòng)的模式百度遷徙人類移動(dòng)性：時(shí)間特征時(shí)間特征量：間隔時(shí)間人類行為在時(shí)間上具有驚人相似的統(tǒng)計(jì)規(guī)律(周濤2013)

間隔時(shí)間和等待時(shí)間的分布，在絕大多數(shù)情況下具有胖尾的特性，很多可以用冪律分布較好刻畫(Barabasi

2005)人類行為在發(fā)生時(shí)間上具有“強(qiáng)陣發(fā)弱記憶”的特性(Goh

2008)陣發(fā)性：事件會(huì)在較短時(shí)間內(nèi)密集發(fā)生，然后又會(huì)出現(xiàn)一個(gè)很長的空檔期記憶性：長的時(shí)間間隔后容易跟著一個(gè)也較長的時(shí)間間隔，而短的時(shí)間間隔后面容易跟著一個(gè)也較短的時(shí)間間隔人類行為具有明顯的波動(dòng)性和周期性(Ahas

2010)時(shí)間特征建?；趦?yōu)先級的排隊(duì)論模型，可以很好解釋等待時(shí)間的冪律分布(Barabasi

2005)

歷史記憶特性(Vazquez

2007)、興趣變化(Han

2008)以及生理周期和工作周期的影響(Hidalgo

2006)人類移動(dòng)性：空間特征量（1）步長分布

基于多個(gè)城市的出租車數(shù)據(jù)發(fā)現(xiàn)，移動(dòng)步長一般在城市尺度內(nèi)表現(xiàn)為指數(shù)分布，在城市間或者更高尺度內(nèi)表現(xiàn)為冪律分布(Liang

2013)

針對混合交通方式出行，移動(dòng)步長服從冪律或截?cái)嘈蛢缏煞植?；而對于單一交通方式，則服從指數(shù)或近似指數(shù)分布(陸鋒2014)

利用手機(jī)數(shù)據(jù)發(fā)現(xiàn)，群體移動(dòng)步長服從尾部截?cái)嗟膬缏煞植?。?shí)際是個(gè)體的Levy移動(dòng)和人群異質(zhì)性卷積的效果(Gonzalez

2008)

群體水平上人類移動(dòng)步長的冪律分布可能是移動(dòng)模式各不相同的若干個(gè)體混合所致的，并不能據(jù)此推斷每個(gè)個(gè)體的移動(dòng)步長也服從相同的分布規(guī)律

在個(gè)體水平上，人類的移動(dòng)步長分布呈現(xiàn)不規(guī)則的多樣化特征，并不服從某種特定的分布形式角度分布隨機(jī)游走模型中，假設(shè)角度分布是均勻的

由于受到道路網(wǎng)、地理環(huán)境的影響，角度分布會(huì)出現(xiàn)各向異性的特點(diǎn)

(Liu

2012,Jiang

2009)人類移動(dòng)性：空間特征量（2）回轉(zhuǎn)半徑(ROG)

利用手機(jī)數(shù)據(jù)發(fā)現(xiàn)，人類空間運(yùn)動(dòng)具有高度的有界性特征(Gonzalez2008)每個(gè)個(gè)體都在一個(gè)以家和工作地點(diǎn)為中心的有限范圍內(nèi)活動(dòng)距離家或工作地越近的地點(diǎn)被個(gè)體訪問的頻率就越高人類的空間運(yùn)動(dòng)范圍具有局域化的特點(diǎn)無論是軌跡的均方位移、回轉(zhuǎn)半徑還是時(shí)空概率密度函數(shù)，增長速度都慢于具有相同參數(shù)的Lévy飛行模型熵個(gè)體軌跡序列的信息熵(Song

2010)平均意義上，手機(jī)用戶在下一小時(shí)所在地點(diǎn)的不確定性只有20.8人類空間模式具有93%的可預(yù)測性人類移動(dòng)性：空間特征建模移動(dòng)模式建模考慮的因素(劉瑜2014)地理環(huán)境、距離衰減以及個(gè)體的空間行為特征重力模型對于地理環(huán)境和距離衰減的影響，通常采用重力模型以及其改進(jìn)模型采用重力模型解釋上海市出租車出行步長分布呈截?cái)鄡缏商卣鞣菍Φ鹊闹亓δＰ停玫亟忉屃顺鲎廛囈苿?dòng)步長的分布特征(Liang

2013)輻射模型(Simini

2012)：直接采用人口數(shù)作為區(qū)域規(guī)模來檢查重力模型并不合個(gè)體空間行為特征建模偏好返回模型(Song

2010)人類同時(shí)具有探索未知地點(diǎn)和返回之前熟悉地點(diǎn)的傾向基于層次性交通系統(tǒng)的人類運(yùn)動(dòng)模型(Han

2011)人們進(jìn)行日常的長途旅行時(shí)，經(jīng)常通過大的交通中心進(jìn)行中轉(zhuǎn)帶有返家機(jī)制的Lévy飛行模型(Hu

2011)基于個(gè)體在游走的過程中希望訪問更多不同的地點(diǎn)，以獲取盡可能多樣的信息的假設(shè)，將其轉(zhuǎn)為信息熵優(yōu)化模型基于個(gè)體的活動(dòng)轉(zhuǎn)移模型(Wu

2014)集成個(gè)體活動(dòng)鏈轉(zhuǎn)移概率、地理環(huán)境異質(zhì)性以及距離衰減等因素NetworksNetworknodes

–

links

structurerandom

networkComplex

Networka

graph

(network)

with

non-trivial

topological

featuresfeatures

that

do

not

occur

in

simple

networks

(lattices

or

random

graphs),but

often

occur

in

graphs

modelling

real

systemsScale-free

networkspower-law

degree

distributionsSmall-world

networksshort

path

lengths

(small

diameter),

and

high

clusteringsmall-world

phenomenon

(six

degrees

of

separation)Social

Network

a

social

structure

made

up

of

a

set

of

social

actors

(such

as

individor

organizations)

and

a

set

of

the

dyadic

ties

between

these

actors.Spatial

NetworkSpatialNetwork

(Barthélemy

2011)

a

network

for

which

the

nodes

are

located

in

a

space

equippedwith

a

metric.space

is

the

two-dimensional

space

and

the

metric

is

the

usualEuclidean

distancelinks

are

not

necessarily

embedded

in

space

the

probability

of

finding

a

link

between

two

nodes

will

decreawith

the

distancethe

connection

probability

between

two

individuals

usually

decreawith

the

distance

between

themSpatial

network

typologyplanar

networksa

network

with

no

crossing

componentsfor

many

applications,

planar

spatial

networks

are

the

most

imporroads,

rail,

rail,

and

other

transportation

networks.it

does

not

imply

that

a

spatial

network

is

always

planarspatial,

non-planar

networksairline

network,

the

cargo

ship

network,

or

the

InternetCategoreis

of

Spatial

NetworksTransportation

networksairline

networksbus,

subway,

and

railway,

networkscargo

ship

networkscommuters

networksInfrastructure

networksroad

and

street

networkspower

grids

networkswater

distribution

networksInternetgeography

in

social

networksOrigin-destination

matrix

and

mobility

networksMobile

phone

and

GPSRFIDs

(Radio

Frequency

Identification)Gravity

Law

for

Spatial

NetworksThe

gravity

law

the

number

of

trips

from

location

i

to

location

j

follows

the‘Gravity’

law

(Erlander

1990)dij

is

the

Euclidean

distance

between

these

two

locationsPi(j)is

the

population

at

location

i(j)σ

is

an

exponent

whose

value

act