地理空间大数据

地理信息科学进展地理空间大数据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)时空大数据的数据模型时空位置模型锚点建模静态位置、或时空过程中停留时间较长的位置P

=(x,y,T,W)或P

=(g,T,W)时空轨迹模型移动对象模型为主体，一组离散的时空点序列Tr

=

(o,

{x1,y1,t1}…

{xj,yj,tj}…)时空交互模型两个位置对象之间交互量的时间变化模型F(Pi→Pj)

=

(xi,

yi,

xj,

yj,

T,

WPi→Pj)F(Pi→Pj)

=

(gi,

gj,

T,

WPi→Pj)时空网络模型点对象之间的时态交互映射为图结构G

=

(V,

E)时空位置大数据Place

&

LocationPosition

DataPoint

of

Interest

(POI)Area

of

Interest

(AOI)Place

nameTextWebSearch

EngineGeotagged

DataTextPhotoSocial

media……百度定位大数据：热力图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?检索大数据：百度旅游检索《心花路放》热映火了“梧桐客栈”“新丝绸之路经济带”的发展带动西部旅游百度检索大数据：预测旅游每天消费者在百度平台的旅游类搜索超过2200万次覆盖旅游人群2.12亿其中无线端覆盖人群1.77亿。通过海量检索行为，预测旅游市场趋势国内旅游景点可以提前2天预测国内城市旅游可以提前45天预测出国旅游可以提前45天预测国内游检索大数据与市场数据呈现相同的增长趋势两者表现出强正相关出境游检索数据提前预知市场数据白色：检索数据橙色：市场数据12·31上海外滩踩踏事件分析12·31上海外滩踩踏事件2014年12月31日23时35分许，跨年夜活动外滩陈毅广场进入和退出的人流对冲发生踩踏事件大数据分析（百度研究院大数据实验室）当时的人流量大到什么程度？事发当时是否是当晚人流量最大的时候？当时人流的对冲到底是什么样的程度？群体聚集是突发情况，可以预警吗？人群热力图人群流量趋势图上海外滩踩踏事件大数据分析（1）中秋前夜（2）国庆当晚（3）跨年当晚人群分布热力图人群流动方向图人群流动方向分布外滩地图搜索与人群到达数量的互相关性互相关性曲线在-1.5小时的时候达到峰值：根据地图上相关地点搜索的请求量，至少可能提前几十分钟预测出人流量峰值的到来时空轨迹大数据按采样方式和驱动因素分类（李婷2014）基于等时间间隔采样的轨迹数据，如车载GPS数据基于位置采样的轨迹数据，如居民出行调查数据基于事件触发的轨迹数据，如手机定位数据、公交车刷卡数据按涉及的交通出行方式分类（周涛2013）单一出行方式数据，如出租车、公交车数据等混合出行方式数据，如手机、签到数据等按采集的位置信息格式分类基于GPS的轨迹数据，有精确的经纬度信息，如出租车、微博签到等

基于参考点的轨迹数据，如手机定位数据、公交车/地铁刷卡数据、wifi数据等Trajectory

Data

MiningSpatial

Trajectories

Computing

Framework

(Zheng

2015)人类移动性(human

mobility)面向人的活动模式研究每个人的活动如同分子运动,看似杂乱无序,实则存在潜在的模式发现这种模式并揭示其影响因素,需要采集海量的时空轨迹数据基于时空轨迹大数据的移动性研究特征量分析和特征建模时间空间研究对象个体群体驱动机制基于移动的模式基于活动的模式百度迁徙人类移动性：时间特征时间特征量：间隔时间人类行为在时间上具有惊人相似的统计规律(周涛2013)

间隔时间和等待时间的分布，在绝大多数情况下具有胖尾的特性，很多可以用幂律分布较好刻画(Barabasi

2005)人类行为在发生时间上具有“强阵发弱记忆”的特性(Goh

2008)阵发性：事件会在较短时间内密集发生，然后又会出现一个很长的空档期记忆性：长的时间间隔后容易跟着一个也较长的时间间隔，而短的时间间隔后面容易跟着一个也较短的时间间隔人类行为具有明显的波动性和周期性(Ahas

2010)时间特征建模基于优先级的排队论模型，可以很好解释等待时间的幂律分布(Barabasi

2005)

历史记忆特性(Vazquez

2007)、兴趣变化(Han

2008)以及生理周期和工作周期的影响(Hidalgo

2006)人类移动性：空间特征量（1）步长分布

基于多个城市的出租车数据发现，移动步长一般在城市尺度内表现为指数分布，在城市间或者更高尺度内表现为幂律分布(Liang

2013)

针对混合交通方式出行，移动步长服从幂律或截断型幂律分布；而对于单一交通方式，则服从指数或近似指数分布(陆锋2014)

利用手机数据发现，群体移动步长服从尾部截断的幂律分布。实际是个体的Levy移动和人群异质性卷积的效果(Gonzalez

2008)

群体水平上人类移动步长的幂律分布可能是移动模式各不相同的若干个体混合所致的，并不能据此推断每个个体的移动步长也服从相同的分布规律

在个体水平上，人类的移动步长分布呈现不规则的多样化特征，并不服从某种特定的分布形式角度分布随机游走模型中，假设角度分布是均匀的

由于受到道路网、地理环境的影响，角度分布会出现各向异性的特点

(Liu

2012,Jiang

2009)人类移动性：空间特征量（2）回转半径(ROG)

利用手机数据发现，人类空间运动具有高度的有界性特征(Gonzalez2008)每个个体都在一个以家和工作地点为中心的有限范围内活动距离家或工作地越近的地点被个体访问的频率就越高人类的空间运动范围具有局域化的特点无论是轨迹的均方位移、回转半径还是时空概率密度函数，增长速度都慢于具有相同参数的Lévy飞行模型熵个体轨迹序列的信息熵(Song

2010)平均意义上，手机用户在下一小时所在地点的不确定性只有20.8人类空间模式具有93%的可预测性人类移动性：空间特征建模移动模式建模考虑的因素(刘瑜2014)地理环境、距离衰减以及个体的空间行为特征重力模型对于地理环境和距离衰减的影响，通常采用重力模型以及其改进模型采用重力模型解释上海市出租车出行步长分布呈截断幂律特征非对等的重力模型，更好地解释了出租车移动步长的分布特征(Liang

2013)辐射模型(Simini

2012)：直接采用人口数作为区域规模来检查重力模型并不合个体空间行为特征建模偏好返回模型(Song

2010)人类同时具有探索未知地点和返回之前熟悉地点的倾向基于层次性交通系统的人类运动模型(Han

2011)人们进行日常的长途旅行时，经常通过大的交通中心进行中转带有返家机制的Lévy飞行模型(Hu

2011)基于个体在游走的过程中希望访问更多不同的地点，以获取尽可能多样的信息的假设，将其转为信息熵优化模型基于个体的活动转移模型(Wu

2014)集成个体活动链转移概率、地理环境异质性以及距离衰减等因素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