Why does ST_Area return different results for geog vs geom?
I have this simple query that returns very different values for a geom vs a geog:
SELECT ST_Area(the_geom::geography) as area1, st_area(the_geom) from neighborhood where city_id="ny-new-york-city" +----------------+--------------------+ |area1 | area | +----------------+--------------------+ |1646766.08995329| 0.00017589982948304| +----------------+--------------------+
The SRID of
the_geomcolumn is 4326. I don't get why geog column makes such a difference.
Which measurement is correct?
Geography uses a spheroidal model and always measures in meters though coordinates are expressed in degrees. Geometry is a planar model always even if you store your data in long lat coordinates (in that case your longitude is projected to X and latitude to Y -- what is known as a Platte-Carree projection. As such don't rely on measurement functions in geometry for long/lat data. Use a measure preserving spatial ref sys like one of the UTM ones, or use geography if you don't have the time to learn about spatial reference systems.
The downside is speed of geography is slower than geometry and has much fewer functions, the upside is you don't need to care about where you are in the world to use it.
Why does ST_Area return different results for geog vs geom? - Geographic Information Systems
The consultant for this unit was Professor Ronald Knapp of the State University of New York (SUNY) at New Paltz. Professor Knapp is a geographer who specializes on China.
This unit begins with a set of maps, both general and outline, and then divides discussion of China's geography into four topical areas. The discussion refers to the maps and other visuals imbedded in the text. Suggested Questions for Discussion that can be used to guide students through the material in all four topics are grouped together at the end of the units.
- (China in Asia)
(For classroom activity)
RIVERS, BORDERS, and CIVILIZATIONS
- Major Rivers
- Huang He (Yellow River)
- Chang Jiang (Yangzi River)
- Zhu Jiang (Pearl River) Delta
- Autonomous Regions and China's Minorities
- Special Administrative Regions (SARs)
- Huang He (Yellow River). China's second longest river, the Huang He rises in Qinghai province and flows some 5464 km to the Yellow Sea. Crystal clear lakes and sluggish meandering are characteristic in its upper reaches. Along the Great Bend of the Huang He in its middle course, the unruly river carves its way through the loessial plateau with substantial erosion taking place. As the river erodes the loess, it becomes a "river of mud" (Loessial soil is called huang tu or "yellow earth" in Chinese and it is the color of this suspended loess in the river that has given the Huang He its name "Yellow River.") Carrying 40% sediment by weight in summer (for other rivers in the world 3% would be considered a heavy sediment load), the river deposits vast amounts of alluvium as it courses across the North China Plain. Over the centuries, deposition has raised the bed of the Huang He so that it is in some ways "suspended" precariously above the lower surrounding agricultural areas, contained by levees and embankments built to control what historically was "China's Sorrow"— the bringer of flood and famine.
- Chang Jiang (Yangzi River). As China's "main street," this artery courses over 6300 km through several of China's most economically developed regions. Excellent river ports — Shanghai, Zhenjiang, Nanjing, Wuhan, Yichang, and Chongqing — are located near or along the Chang Jiang, making it one of the world's busiest inland waterways. As much of 40% of the country's total grain production, 70% of the rice output, and more than 40% of China's population are associated with its vast basin that includes more than 3,000 tributaries. The flow of the Chang Jiang is some 20 times greater than that of the Huang He. With its numerous tributaries, the Chang Jiang drains nearly 20% of China's total area. Its upper reaches tap the uplands of the Tibetan Plateau before sweeping across the enormous and agriculturally productive Sichuan Basin that supports nearly 10% of China's total population. It is in the middle course of the Chang Jiang that the controversial Three Gorges Dam project is being constructed.
- Zhu Jiang (Pearl River) Delta. Situated in Guangdong province just to the north of Hong Kong and Macao, the delta of the Zhu Jiang is the most significant farming area in southeastern China. Some regard it as one of the most productive and sustainable ecosystems in the world because of its integrated dike-rice paddy-fish pond agricultural system. Between 1988 and 1995, land reclamation along the banks of the river and along the coast added farm land and space for fish ponds as well as created space for rapidly expanding settlements.
- SRID in calculations, give your points a different SRID and you'll get different values back. This is prop Aggregate functions: to the best of my knowledge MySQL offers no spatial aggregates functions
POPULATION and AGRICULTURE
GEOGRAPHY and REGIONS
General Maps (China in Asia)
Visit the following sites to view and select maps as well as other general background information about China.
Produced by the National Geographic Society, this satellite image of China has borders and cities superimposed on it and reveals the striking regional differences in China's topography.
Outline Maps (For classroom use)
The outline maps included below are designed to be used as transparencies that can be overlaid on an overhead projector to demonstrate the diversity of China's physical and cultural geography. Copies can be printed out and reproduced also for student use. Many of the descriptive sections below utilize the maps in ways to sharpen student's understanding of China's geography. They may all be printed out now or printed as they are introduced below.
Rivers, Borders and Civilization
China's two major rivers, the Huang He (Yellow River) and the Chang Jiang (Yangzi or Yangtze River), as well as the Pearl River (Zhu Jiang) delta system marked by the Xi Jiang (West River) in southeastern China, have provided the framework for agricultural development and population growth throughout China's history. Another river, the Heilong Jiang (known also as the Amur River, its Russian name) marks the border between China and Russia at times in the past, this area was one of confrontation between the neighbors. The drainage basins of China's rivers differ in terms of extent and topography, offering varying opportunities for agricultural development. Because some of China's largest rivers have their source regions on the high Qinghai-Tibetan Plateau and drop great distances over their middle and lower courses, China is rich in hydroelectric resources.
The lower course of the Huang He has changed 26 times in China's history, most notably nine times including major floods in 1194 AD and again in 1853, that brought untold disaster to the villages and towns of the North China Plain. (See Map of Course Changes of the Huang He.) What was once a scourge that plagued the Chinese people throughout much of their history continues to be one of China's great natural challenges — preventing both flooding and drought in a region with more than 100 million people. Siltation at the mouth of the Huang He extended the length of the river by about 35 km (20 miles) between 1975 and 1991. The North China Plain is indeed a "gift" of the Huang He.
Throughout the loessial uplands, some 40 million Chinese still live in cave-like or subterranean dwellings that are an especially appropriate response to the peculiar nature of loess and the absence of alternative building materials such as timber.
Looking at the map of historical borders and the map showing the major rivers highlights the important fact that the earliest hearths of Chinese civilization developed along its river valleys. One of the cradles of Chinese civilization, the Neolithic site called Banpo, was located along a tributary of the Huang He not too far from the present-day city of Xi'an in Shaanxi province. Hemudu, on the southern shores of Hangzhou Bay that lies to the south of the Yangzi River delta, is another of China's important Neolithic sites. The Shang dynasty (c. 1600-1027 BC) was also situated around the Huang He (Yellow River), and eventually spread southward to the Chang Jiang (Yangzi River) and Xi Jiang.
Mountains and Deserts
The west of China is comprised of mountains and deserts as well as plateaus that do not provide much arable land for agriculture. Throughout most of history, the civilization that grew up to the east in what is today China was not surrounded by other nearby major civilizations. To this extent the Chinese were "isolated" from competing civilizations although there was a broad and fluid frontier zone on the western margins. This geographical fact is important to remember when discussing the Western encroachment on China from the sea during the late imperial period.
Although the mountains and deserts of the west limited contact between early imperial dynasties and other centers of civilization in the Inner Asia, Middle East, South Asia, and Europe, there were some important and notable exchanges of culture. The legendary Silk Road facilitated the exchange of goods and ideas between China and each of these areas.
Like many other countries, the historical borders of china have varied over time. Under the Han dynasty (202 BC-202 AD), China's great historical empire, these early boundaries were significantly expanded, as the series of historical maps of China shows. The extent of China's territory was greatest under the last dynasty, called the Qing (Ch'ing) or Manchu dynasty between 1644-1912. China's territory was more extensive under the Qing empire than it is today.
China is at the core of a cultural sphere or region known as East Asia. Looking at the map of bordering nations, it is possible to identify China's neighbors, some of which received substantial cultural influence from China. China, Korea, Japan, and Vietnam historically form the East Asian or Sinitic cultural sphere.
The large number of countries with which China shares borders makes Chinese foreign policy especially complex (unlike the U.S., for example which shares borders only with Canada and Mexico).
Supplementing Geography: Great Wall, Grand Canal, Terracing and Irrigation
The Chinese attempted to correct perceived "deficiencies" in their physical geography by building massive civil engineering projects that would help bring about unity and provide defense as well as by countless smaller scale efforts at modifying their physical landscapes.
- Great Wall. What is known today as the Great Wall (see map of the Great Wall and the Grand Canal) was reputedly first completed during the Qin (Ch'in) dynasty (221-206 BC) when segments of the wall existing from earlier periods were connected. Early walled ramparts were constructed of rammed or tamped earth. The brick-faced walls seen today were built much later during the Ming dynasty (1368-1644). Although not a single continuous wall, the Great Wall and its associated military encampments and guard posts figured in attempts by many dynasties to manage the nomadic peoples, sometime referred to as "barbarians," who lived north of it on the grasslands or steppes. For the most part, the Great Wall should be viewed as a zone of transition — rather than a fixed border — between farming areas with sedentary villages and pasture lands with nomadic lifestyles.
- Creating level land through terracing of hill slopes. Throughout the rugged areas of northern and southern China, farmers over the centuries have sculpted the hilly land into step-like landscapes of terraces. Sometimes terraces are relatively natural features that need only be modified in order to produce level areas for planting, while in others extraordinary efforts must be carried out to move earth and rock, stabilize retaining walls, and create sluices for controlling the flow of water. Drainage control and water storage are as important as the level land itself.
Population and Agriculture
Population and Arable (Farming) Land
It is a well known fact that China is the most populous nation in the world. China's total population of 1,252,800,000 nearly exceeds the combined populations of Europe (579,700,000) and South America (311,500,000) and the United States (272,573,000) and Japan (125,200,000). By comparison, the population of the United States is equivalent to only 22% of China's population.
Such a huge population imposes substantial stress on the country's natural resources, including especially arable land. Although China ranks fourth in the world in terms of total arable land, the pressure of population on this precious available agricultural land is acute and makes China's struggle to increase its agricultural output to feed its population all the more difficult. Looking at the map of China's agricultural regions and crops, you will see that China's arable land is primarily in the eastern region, the same area where a majority of China's vast population is concentrated. In addition to extensive areas of western China which are relatively uninhabited, substantial portions of southern China are unfavorable for agriculture because of mountainous topography. There are significant variations from province-to-province in terms of cultivated land, multiple-cropping, and overall production of various crops.
China feeds somewhat less than one-quarter (25%) of the world's population on approximately 7% of the world's arable land.
Viewing the map showing the U.S. and China superimposed, it can be seen that China has only a slightly larger land area, 3.69 million square miles compared to the 3.68 million square miles of the United States. However, while approximately 40% of the U.S. land can be cultivated, only 11% of China's land is arable. Much of the arable land in the United States, of course, is actually not used for farming but instead is used for pasture or has been developed for other uses.
Like China, the U.S. has a densely populated east coast. Unlike the U.S., however, China's farmland is not concentrated in a relatively underpopulated central section of the country. Of the roughly 273 million population in the U.S., less than 3% are engaged in farming while the U.S. has about 80% more farmland than does China and 10 times more farmland per capita. The following map compares the densities of population in the United States and China:
Despite the high population density reflected on the map, China is not an urban society even though its total urban population (311,000,000) exceeds the actual total population of the United States. (The urban population of the U.S. is approximately 194,7000,000, some 75% of the country's total many Americans, of course, live in suburban communities.) Although some seventy-four per cent (74%) of China's population is still primarily engaged in agriculture and living in rural areas, these same farming areas have undergone substantial industrialization and commercialization in the past two decades since 1979.
- Crops. Wet rice or paddy rice agriculture is carried out particularly in fertile areas of southern and central China where a mild climate favors two and sometimes three crops per year. The growing of rice is frequently rotated with other crops such as winter wheat, sweet potatoes, corn, and vegetables of various types. Vegetable oil producing plants — specifically rape-seed (the oil of which is known in the U.S. as canola oil), peanuts, and sesame — are widely grown throughout this region on appropriate soils.
In addition to relatively mild winter temperatures and a long growing season, heavy and predictable summer monsoon rains and overall sufficient annual rainfall are the basis for substantial productive agriculture. It is important to recognize that China's southern and central rice-growing regions are quite diverse.
3 Answers 3
I can't speak to advantages/disadvantages vis-a-vis MySQL, but the PostGIS code is pretty widely regarded as one of the best (in terms of speed/functionality) and most mature (in terms of testing/real-world exposure) available.
By way of example, there was a talk at PGEast 2010 by some folks from the FAA on their converting their airport database (used by AeroNav and others to compile charts) to Postgres/PostGIS from Oracle.
The avationDB site is also built on top of Postgres (8.0).
If GIS-related queries are at the heart of what you're doing my suggestion would be to go with Postgres. It can certainly handle everything else you would normally do in a relational database as well.
In terms of making the switch from MySQL, the documentation behind Postgres is first-rate, and there's also a section of the Oostgres Wiki about switching from MySQL to Postgres.
The initial learning curve may be a bit steep and you may need to tweak your database and any stored procedures (if you've written them for MySQL already), but it is not an insurmountable task.
You should be able to pick up enough to make the switch in a couple of weeks time, and if you set up a development database you can probably be well versed in routine tasks within a month, and confident you know where to look in the manual for the not-so-routine ones.
Speaking of some very major things. Here is a list of things PostGIS supports that are totally absent in MySQL and MariaDB.
K nearest neighbor: Only PostGIS supports KNN. Find the nearest point to any point using just an index: no need to calculate the distance from all points!er. MySQL breaks spec and only checks that two values have the same SRID. PostGIS comes with a database of pro4j definitions to enable seamless SRID awareness. Setting an SRID and calling ST_Transform (a function MySQL lacks) will reproject your coordinates..
In MySQL, all computations are done assuming SRID 0, regardless of the actual SRID value. SRID 0 represents an infinite flat Cartesian plane with no units assigned to its axes. In the future, computations may use the specified SRID values. To ensure SRID 0 behavior, create geometry values using SRID 0. SRID 0 is the default for new geometry values if no SRID is specified.
Rasters: there are a ton of features here from raster generation to extraction. You can generate heatmaps and the like.
Geography, PostGIS supports an unprojected geography type that doesn't use Cartesian math at all. It has a whole slow of associated functions that operate on oblate sphereoids. MySQL conversely can not even creating a bounding box in a geographical SRS from two points.
Topology, distinct from vector geometries, topo geoms store nodes and relations. Move a node, the edge moves too and you get a new face. This also forces edges to be directed which makes them ideal for routing. As a subpoint 100% of what PgRouting does is unavailable to MySQL -- so you just can't create a Google Maps or the like on top of it.
Geocoding: there is geocoder exstension in the contrib directory that works off census data, and a loader to install that data.
Address standardization: there is an extension that handles normalizing addresses for easy parsing, storage, and comparison.
SQL-MM features, you simply won't find CIRCULARSTRING COMPOUNDCURVE CURVEPOLYGON MULTICURVE or MULTISURFACE in MySQL.
n-d cords: PostGIS can support 3dm, 3dz, and 4d shapes and points MySQL simply can't
MySQL only supports r-tree indexes. PostGIS supports r-tree (gist/gin) and BRIN (for large geometry tables)
Aggregate functions: to the best of my knowledge MySQL offers no spatial aggregates functions
K nearest neighbor: Only PostGIS supports KNN. Find the nearest point to any point using just an index: no need to calculate the distance from all points!
Indexing. PostgreSQL allows you to store any data on your spatial index (which is a gist/gin index). For example, you can store the year (or other non-spatial data) and the geom on the same index. See btree_gin and btree_gist for more information on how to do this.
Also, there are probably 200 or so more functions supported by PostGIS.
In short, MySQL doesn't hold its own to PostGIS and it knows it. PostGIS is a beast. Just wanted to explain some of this stuff.
2 Answers 2
Aggregation is substantively meaningful (whether or not the researcher is aware of that).
One should bin data, including independent variables, based on the data itself when one wants:
To hemorrhage statistical power.
To bias measures of association.
A literature starting, I believe, with Ghelke and Biehl (1934—definitely worth a read, and suggestive of some easy enough computer simulations that one can run for one's self), and continuing especially in the 'modifiable areal unit problem' literature (Openshaw, 1983 Dudley, 1991 Lee and Kemp, 2000) makes both these points clear.
Unless one has an a priori theory of the scale of aggregation (how many units to aggregate to) and the categorization function of aggregation (which individual observations will end up in which aggregate units), one should not aggregate. For example, in epidemiology, we care about the health of individuals, and about the health of populations. The latter are not simply random collections of the former, but defined by, for example, geopolitical boundaries, social circumstances like race-ethnic categorization, carceral status and history categories, etc. (See, for example Krieger, 2012)
Dudley, G. (1991). Scale, aggregation, and the modifiable areal unit problem. [pay-walled] The Operational Geographer, 9(3):28–33.
Lee, H. T. K. and Kemp, Z. (2000). Hierarchical reasoning and on-line analytical processing of spatial and temporal data. In Proceedings of the 9th International Symposium on Spatial Data Handling, Beijing, P.R. China. International Geographic Union.
Openshaw, S. (1983). The modifiable areal unit problem. Concepts and Techniques in Modern Geography. Geo Books, Norwich, UK.
Absolute rooting depths (Di) and lateral root spreads (Li) generally increased for plant growth forms as their size and life span increased (Figs 2 and 3, table in Appendix 2), with values greatest in trees and smallest in annuals. Perennial grasses and forbs did not differ in root dimensions, and shrubs had significantly larger Di and Li than semi-shrubs. Succulents had very shallow rooting depths but large lateral root spreads (Figs 2 and 3). There were also clear differences among growth forms in the shape of the root systems, with succulents having the largest ratios of lateral spread to rooting depth (Li : Di), a geometric mean of 4.5 (vs. c. 3 for trees, c. 1 for shrubs, c. 0.5 for semishrubs, and 0.3–0.35 for all herbaceous plants, see table in Appendix 2).
Maximum rooting depths of plant growth forms. Geometric means marked by different letters were significantly different according to one-way anova s (see table in Appendix 2 for statistical parameters).
5.6. Survey Control
Geographic positions are specified relative to a fixed reference. Positions on the globe, for instance, may be specified in terms of angles relative to the center of the Earth, the equator, and the prime meridian. Positions in plane coordinate grids are specified as distances from the origin of the coordinate system. Elevations are expressed as distances above or below a vertical datum such as mean sea level, or an ellipsoid such as GRS 80 or WGS 84, or a geoid.
Land surveyors measure horizontal positions in geographic or plane coordinate systems relative to previously surveyed positions called control points. In the U.S., the National Geodetic Survey (NGS) maintains aNational Spatial Reference System (NSRS) that consists of approximately 300,000 horizontal and 600,000 vertical control stations (Doyle,1994). Coordinates associated with horizontal control points are referenced to NAD 83 elevations are relative to NAVD 88. In a Chapter 2 activity you may have retrieved one of the datasheets that NGS maintains for every NSRS control point, along with more than a million other points submitted by professional surveyors.
Benchmark used to mark a vertical control point. (Thompson, 1988).
In 1988 NGS established four orders of control point accuracy, which are outlined in the table below. The minimum accuracy for each order is expressed in relation to the horizontal distance separating two control points of the same order. For example, if you start at a control point of order AA and measure a 500 km distance, the length of the line should be accurate to within 3 mm base error, plus or minus 5 mm line length error (500,000,000 mm × 0.01 parts per million).
Four orders of control point accuracy
Order Survey activities Maximum base error(95% confidence limit) Maximum Line-length dependent error(95% confidence limit) AA Global-regional dynamics deformation measurements 3 mm 1:100,000,000 (0.01 ppm) A NSRS primary networks 5 mm 1:10,000,000
B NSRS secondary networks high-precision engineering surveys 8 mm 1:1,000,000
C NSRS terrestrial dependent control surveys for mapping, land information, property, and engineering requirements 1st: 1.0 cm
2nd-I: 2.0 cm
2nd-II: 3.0 cm
3rd: 5.0 cm
Control network accuracy standards used for U.S. National Spatial Reference System (Federal Geodetic Control Committee, 1988).
Doyle (1994) points out that horizontal and vertical reference systems coincide by less than ten percent. This is because
&hellip.horizontal stations were often located on high mountains or hilltops to decrease the need to construct observation towers usually required to provide line-of-sight for triangulation, traverse and trilateration measurements. Vertical control points however, were established by the technique of spirit leveling which is more suited to being conducted along gradual slopes such as roads and railways that seldom scale mountain tops. (Doyle, 2002, p. 1)
You might wonder how a control network gets started. If positions are measured relative to other positions, what is the first position measured relative to? The answer is: the stars. Before reliable timepieces were available, astronomers were able to determine longitude only by careful observation of recurring celestial events, such as eclipses of the moons of Jupiter. Nowadays geodesists produce extremely precise positional data by analyzing radio waves emitted by distant stars. Once a control network is established, however, surveyors produce positions using instruments that measure angles and distances between locations on the Earth&rsquos surface.
1 Answer 1
There should be a Combine Geometry parameter in the transformer. Have you got that set to Result Geometry Only?
If this is set then I believe the geometry comes out with the query, without having to specify the column name, like here:
A couple of other thoughts - maybe too obvious - but you are using the Oracle Spatial Object reader (not Oracle Non-Spatial)? And are you getting any results at all? Like is it returning attributes from Oracle but no geometry? It could be that there are no results being matched. Perhaps there are parameters for the Oracle format that need setting?
Also, are there any warnings in the log file? That could spell out what the problem is. As a last resort, run it again with Tools > FME Options > Translation > Log Debug set. That might return some extra messages to help with debugging (just don't leave it on all the time because it will return messages that could be misconstrued outside of debugging).
Human activity plays a vital role in understanding large-scale social dynamics (Nara, Tsou, Yang, & Huang, 2018 Zhang, Demšar, Rantala, & Virrantaus, 2014 Zhang, Rangsima, & Virrantaus 2010). There are several data sources available for modelling human activity and population dynamics. For instance, mobile geolocation data has been used in assessing the movement patterns of population (Bengtsson, Lu, Thorson, Garfield, & Schreeb, 2011 González, Hidalgo, & Barabási, 2008 Pedro, 2020). However, the major limitations arise due to privacy issues since mobile data is linked with users’ private information, including bank information, social network information, and home locations, which causes difficulties in obtaining mobile data for research purposes. In order to protect users’ privacy, the mobile data such as SafeGraph data (https://www.safegraph.com/) is only available at coarse spatial scales such as county level. Furthermore, mobile data has a low resolution, since mobile phone users’ locations are estimated relative to the nearest phone tower, which can be several kilometers away from a person’s actual location. Jiang, Ferreira, & González (2012) presented an analysis of individual activities based on travel surveys conducted in the Chicago metropolitan area from a representative population sample. Compared to other data sources, travel survey data has disadvantages due to the high cost, small sample size, and low update frequency. The spatial coverage of survey data is limited, since the spatial information is collected based on locations visited by participants, which may not cover the entire study area.
Digital footprints within urban environments have become increasingly accessible to researchers due to the massive amounts of geo-tagged information shared via social media platforms such as Twitter (Li, Chaudhary, & Zhang, 2020). These new data sources provide important information about population dynamics within a city, and how the population is distributed across the urban infrastructure. In this regard, analysis of spatiotemporal patterns within Twitter data shows a distinct relationship between users’ activities and urban infrastructure types (Soliman, Yin, Soltani, Padmanabhan, & Wang, 2015 Wakamiya, Lee, & Sumiya, 2011). Tweets (or blogs) can be viewed as “personal journals” that describe people’s lives to others by telling stories nearly in “real-time”. These platforms can be efficient ways to inform others where users have been, where they are, and where they are going. Zhao and Rosson (2009) used interviews to discuss various forms of social activity enacted through Twitter. The results showed that interviewees use Twitter for a variety of social purposes, such as keeping in touch with one’s friends, sharing interesting things to one’s social networks, gathering useful information for one’s professional or other personal interests, seeking help and others’ opinions, and releasing emotional stress. Nardi, Schiano, and Gumbrecht (2004) also stated, people blog to provide a record of events in their lives for keeping track of what they have been doing. Many of these social activities are strongly related to location information. People are more likely to tweet in places such as restaurants, hotels, leisure places, and sport centers, since they are doing activities in these locations. Twitter data can be considered to represent an individual’s temporal location inside a specific type of building, therefore human activity dynamics can be modeled by using the relative number of tweets at a certain location and time. For example, Lin and Cromley (2015) evaluated the effectiveness of Twitter data as single ancillary information combined with other control variables in areal interpolation of population. They found that using geo-located tweets to enhance the process of population disaggregation could help to map the urban population under the age of 65. The statistics showed that nearly 70% of the Finnish population aged 18 to 64 participated in social networks. According to Statista’s Digital Market Outlook forecast, the number of social media users in Finland is projected to exceed 3.1 million users in 2018 and increase annually thereafter. In the year 2019, 25% of Finns used Twitter several times a day, 18% of Finns were on Twitter every day, and 8% used the service once a month (Statista, 2020). However, there is still 30% of the Finnish population does not use social media, especially for the elderly and children. Therefore, modeling human dynamics for the building types such as daycare centers and elderly homes cannot be estimated only based on Twitter data since the likelihood of a representative number of people inside these places using Twitter is difficult to measure.
Urban infrastructure registry data has been used as another data source to model human dynamics (Zhang, Rangsima, & Virrantaus, 2010). In the Finnish urban infrastructure registry data, there is information about how many people registered each building as a home address, which can be used to estimate the maximum number of residents inside a residential building. Business information such as business type, company name, and a number of employees is included in the business information section of the Finnish urban infrastructure registry data. This study aims to explore whether social media data combined with urban infrastructure data can be used to cost-effectively assess human activity patterns across spatiotemporal scales for various built environment types. In particular, we aim to develop an object class-oriented space-time composite model to analyze human dynamics for different types of built environment. Finally, the model’s performance was evaluated by comparing the estimated population for the Helsinki Metropolitan area at six moments of time to the registered population dataset.
The rest of the article is organized as follows. Section 2 gives an overview of the foundations and methods relevant to the development of the spatio-temporal population model. The results and their associated validation methodologies are illustrated in Section 3. Sections 4 and 5 present the discussions and conclusions.
Can we predict volcanic eruptions?
Volcanoes give some warning of pending eruption, making it vital for scientists to closely monitor any volcanoes near large population centers. Warning signs include small earthquakes, swelling or bulging of the volcano's sides, and increased emission of gasses from its vents. None of those signs necessarily mean an eruption is imminent, but they can help scientists evaluate the state of the volcano when magma is building.
However, it's impossible to say exactly when, or even if, any given volcano will erupt. Volcanoes don't run on a timetable like a train. This means it's impossible for one to be “overdue” for eruption—no matter what news headlines say.
- Huang He (Yellow River)