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How to normalize choropleth maps of census information?

How to normalize choropleth maps of census information?


I'm trying to improve on the census-based maps on this site, which shows census values at 5 scales from Collection Districts through to State.

Currently the maps show whole numbers (eg total population per polygon) which is not optimal. How should they be normalized?

Option 1: % of Total

I was advised to normalize by % of the total. This results in legend ranges such as "0.0004% to 0.0006% of the Total Population" which (IMO) make the legend hard to decipher - it's not an intuitive fraction, and is hard to put into perspective.

Option 2: Population Density

Another option could be to normalize based on area. Since the datasets are stored in meters, this also results in values like "0.000000019 to 0.000000035 persons per square meter".

I could flip this to show "area / person" - but this wouldn't make sense for datasets such as housing. And would it be offensive when applied to datasets such as religion (1km2 per X person)?

Is there a standard methodology to convert either of these values into something more meaningful? What do other people do on their census maps?

Thanks, Steve


Going with the % option, you could simply say "4 to 6 people in every million (1,000,000)" which means the same thing, but deals with nice round numbers (well, you're normalising to 1,000,000).


Usually, choropleth maps display densities and not populations. Displaying population makes the result too dependent on the subdivision. So, option 2 is certainly the most appropriate one.

To define the density classes, this document describes common methodologies. The quantile method is usually applied for choropleth maps.


It depends on what you want to convey. Percent of total is useful if you want to show the relation to the whole country. Density is useful if you intend to show how the population is distributed on in relation to the physical geography. You could normalize by number of schools if education was important.

I frequently favor standard deviations when making comparisons across many geographic areas particularly when the mean and/or median is significant indicator. They can be intimidating to average users but can be expressed in terms of average, below average and above average. Another advantage is that it retains meaning at different levels of geography.

I agree with jul that density is more meaningful for your map of 'where Australians live'. But, I would take a page from Tufte's book and show the data. Label the areas with the population count while using the chloropleth to show density. A bunch of blues shapes of various shades can give a quick impression of the distribution and the legend will tell them how the shades relate. But, people get a lot more out of seeing the actual number. Nobody can make an intuitive leap from dark blue and big to really high density very quickly. Give them a the number and it will tie it all together.

If you can't work out the labeling, pop-ups or a separate table can work, too.


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Distribution

Distribution refers to the way something is spread out or arranged over a geographic area. The concept of distribution can be applied to nearly everything on Earth, from animal and plant species, to disease infections, weather patterns, and man-made structures.

Many of the things geographers study are found in some places, but not in the others. This means these patterns occur in certain distributions over the Earth's surface. Geographers look for and try to explain any patterns that may occur. "Distribution" refers to the way something is spread out or arranged over an area. Recognizing distributions on a map is a starting point for many geographic studies. Geographers look for and try to explain any patterns that may occur.

Some distributions can be seen visually. The number of barns in a farming community can be seen from an airplane, for example.

Visual information is not always accurate or available, however. Areas may be too big to see, and some areas are not visible at all. These patterns of distribution need to be put on a map. World population is a good example of information that has to be mapped. Geographers can’t count the number of people in an area from the air. They rely on many types of information, such as census data, to determine the distribution of people in a certain area.

To understand distribution patterns, it is important to understand other factors, such as climate, landforms, and vegetation. For example, the human population distribution shows very few people living in Asia’s arid Gobi Desert. The desert offers few resources important for survival.

Conflict and economy can also influence distribution patterns. Thousands of Iraqi citizens left their country after the Iraq War began in 2003. Population distribution shows many Iraqis now living in Syria and Jordan.

The poor economy in rural areas of China have led millions of people to seek employment in huge urban areas such as Beijing and Shanghai. Working in factories and the service industry (hotels, restaurants) is often more profitable than farming. The distribution of rural and urban populations in China has become much more dramatic as a result.

Distribution is the way something is spread out over an area—it does not tell geographers why or how it is spread out.

One topic doctors and biologists study is distribution of the disease malaria. Malaria is found mainly in parts of the world that are tropical and humid. Malaria is common in these areas because the mosquitoes that carry and transmit the disease thrive in hot, humid climates. The distribution of human malarial infections shows high concentrations in tropical regions, and low concentrations in non-tropical regions.

Map courtesy of National Geographic Maps

Distribution and the Economy
Distribution is an important part of economics, as well as geography. In the economic sense, distribution is the process where the producer of a good or service makes it available to consumers. A farmer may grow a crop, and then distribute it to stores or supermarkets.


HERO Collaboratory

A core goal of the HERO project is to develop the technical and conceptual infrastructure to support long-term scientific research on local and regional human implications of global environmental change. A central part of our approach to achieving this goal is to develop a suite of methods and tools that facilitate synchronous and asynchronous joint work by small communities of scientists distributed around a network of sites across the United States. These methods and tools attempt to merge exploratory geovisualization tasks, during which concepts are constructed from data, with knowledge representation systems that capture the structure of relations between concepts, data, tools, and people.

HERO scientists are engaged in a variety of research programs, from developing protocols for data collection, through building theories and models to explicate multiscale processes of change, to developing policies to mitigate change. The mechanism used to make these methods and tools accessible to scientists and to enable joint knowledge construction in a spatially and temporally distributed context is a scientific collaboratory (defined below).

Scientific Collaboratories: An Overview. The challenge of building national collaboratories was detailed in a 1993 National Research Council report (40). This report characterizes a collaboratory as a �nter without walls, in which the nation's researchers can perform research without regard to geographical location—interacting with colleagues, accessing instrumentation, sharing data and computational resources, and accessing information from digital libraries.” Considerable progress has been made toward the report goals (e.g., refs. 41-45). Emphasis thus far, however, has been on collaboratories that facilitate research in physical or medical sciences and on real-time data collection or control of experiments. Only limited progress has been made in application of the collaboratory concept to the study of human-environment interaction (46) or to fusing collaboratory concepts with work in collaborative geographic information systems (47) or collaborative geovisualization (48) see ref. 49 for more on map- and geographic information system-based collaboration. Also, little work has been done on application of knowledge representation methods, within collaboratories, to capture the semantic relationships between all the resources that a collaboratory may contain. Carroll et al. (50) and Chao et al. (51) describe the efforts of other science communities to use emerging knowledge management and portal technology to support knowledge construction in science.

The science establishment in the United States has recognized the need for what has been called “mega-collaboration” to address critical global problems (ref. 52 Zare was chair of the National Science Board at the time of this publication), and human vulnerability and responses to global environmental change is exactly the kind of problem where such megacollaboration is required. As noted by Finholt (44), barriers to interaction across distributed research sites will slow the construction and integration of the knowledge required to resolve challenging research questions. The goal of a distributed network, such as that being developed by HERO, is to bridge place and time by bringing researchers, the visual concept representation and sharing tools they use and the knowledge they build, to a single virtual environment.

Electronic Notebooks: A Vehicle for Acquiring, Constructing, and Sharing Knowledge. One component of the HERO collaboratory is a Web portal that integrates knowledge representation and information visualization tools in an electronic implementation of a traditional scientific notebook. Whereas paper notebooks were commonly used to record the development of an individual's ideas, our collaboratory notebooks are designed with the sharing and collective exploration of scientific information in mind. The notebook takes the form of an online workspace that gives investigators access not just to the digital data and tools they use (e.g., digital libraries, portals such as these are already becoming common) but to the abstract concepts constructed by using these data and methods. HERO workspaces provide a capacity to do more than just encode elements of scientific conversations that are easily 𠇍igitized.” They also facilitate expressing and storing some of the reflection and reasoning that is usually tacit in the mind of the researcher. Rather than being stored in the form of a narrative, as might be common in a paper notebook, this reflection can be described visually through concept-graphing tools the notebook system translates the resulting diagrams into a description logic-based knowledge representation language for storage and sharing.

Fig. 2 shows the home page of a user's workspace, providing access to the people he or she collaborates with, tasks that describe case studies or analysis procedures, concepts that define categories and ideas, data files used to create or reflect concepts, and online tools that can be used to visualize data and concepts. By using this portal system to describe elements of scientific investigations, researchers allow their electronic notebook to capture the evolution of their ideas and those of the communities of other users. Such a notebook allows common questions to be answered in new ways, and even some new questions to be asked, facilitating a dynamic process of concept and method development, extension, and application. For instance,


DETAILED DESCRIPTION

Generally, the present disclosure is directed to systems and methods that determine a semantic location of a map and/or determine proximity between maps. In particular, the systems and methods described herein can identify and select one or more semantic entities that convey a semantic understanding or semantic summarization of the location of items of content included in a map. The selected semantic entities can be designated as semantic locations for such map. For example, for a map that shows the location of bonsai nurseries throughout England, Scotland, and Northern Ireland, the locations of the items of content can be summarized by selecting and using the semantic entity of “the United Kingdom” as a semantic location for such map. Thus, given a set of content locations associated with items of content included in a map, the systems and methods of the present disclosure can determine one or more semantic locations that provide a semantic understanding or semantic summarization of such set of content locations.

As one example, a computing system of the present disclosure can select or otherwise identify a smallest semantic entity that includes greater than a threshold amount of a plurality of content locations associated with a map. The computing system can associate the identified semantic entity with the map as a semantic location for the map. Semantic locations selected for a map can be used to enable search engines to identify the map in response to relevant search criteria. In another aspect, the systems and methods of the present disclosure can determine a proximity score between a first map and a second map. For example, the systems and methods of the present disclosure can determine the proximity score between the first map and the second map based at least in part on a comparison of at least one first semantic location associated with the first map to at least one second semantic location associated with the second map. The proximity score can be used to determine whether to provide the second map as a related map for the first map.

More particularly, certain existing products can enable a user to customize a map or create a new map, for example, by adding items of content to a base map. One or more locations can be selected or otherwise specified by the user for each item of content. For example, the one or more locations associated with each item of content can take the form of various shapes such as, for example, points, lines, polylines, polygons, or other forms. Thus, a user can create a new map that shows the locations of various user-specified items of content in a particular geographic region. As noted above, the systems and methods of the present disclosure can be applied to determine one or more semantic locations that convey a semantic summarization of the locations of the items of content included in such a user-generated map.

In some implementations, to determine one or more semantic locations for a map, a computing system can obtain information descriptive of a plurality of content locations respectively associated with a plurality of items of content included in the map. For example, the items of content can include one or more of: a point item of content, a line item of content, and a polygon item of content. As one example, a user can add an item of content to a map that describes a location of a bonsai nursery store. Such item of content can be a point item of content since it has a single location associated therewith. As another example, a user can add an item of content to a map that describes a navigational route from an origin to a destination. Such item of content can be a line item of content since it includes one or more lines that form the route. As yet another example, a user can add an item of content to a map that describes the extent to which a wildfire is currently burning. Such item of content can be a polygon item of content since it has an area of locations associated therewith which can described with a polygon.

Thus, a map can have a number of items of content of various types, with each item of content having one or more content locations associated therewith. A database that stores the map can include data that describes each item of content and the content locations respectively associated with such item of content. As one example, the content locations can be stored as pairs of latitude and longitude. As such, a computing system implementing the present disclosure can access such database to obtain information descriptive of the content locations associated with a particular map.

According to an aspect of the present disclosure, the computing system can identify a set of content cells based at least in part on the plurality of content locations obtained for the map. For example, the content cells can be cells that include one or more of the content locations.

More particularly, in some implementations of the present disclosure, at least a portion of the world can be geographically divided into a number of cells that respectively correspond to particular geographic areas. In some implementations, the cells can be organized into a data structure that includes a number of layers of cells, with each cell in each layer corresponding to a number of cells from a higher layer that are of smaller geographic size. For example, in an example quadtree tessellation scheme, each cell can include or otherwise correspond to the same geographic area as four child cells that are included in the next higher layer. Likewise, in such example quadtree tessellation scheme, each cell can be one of four child cells that are included in or otherwise correspond to the same geographic area as a parent cell included in the next lower layer.

Thus, a computing system implementing the present disclosure can include or otherwise have access to a database that describes a plurality of cells organized according to such a data structure. The computing system can designate or otherwise select certain of such cells as content cells based at least in part on the plurality of content locations obtained for the map, thereby forming a set of content cells for the map. Stated differently, the computing system can convert the content locations for the map into a set of content cells, where each cell in the set of content cells corresponds to a particular geographic area that includes one or more of the content locations.

In some implementations, the content cells can be selected from cells within a range of layers between a minimum layer and a maximum layer. For example, the maximum layer and the minimum layer can be variables that are adjustable by a system operator based on desired semantic location size, processing time, or other parameters. In some implementations, the computing system first adds cells (e.g., from the maximum layer) to the set of content cells according to a set of selection principles and then adds all parent cells of any cells that have previously been designated as content cells into the set of content cells.

In particular, in some implementations, the computing system can identify the set of content cells by implementing various different selection principles respectively for the different types of content items. As an example, in some implementations, for each point item of content included in the map, the computing system can designate the cell in at least one cell layer that includes the location associated with the point item of content as a content cell. For example, for each point item of content, the cell at the maximum layer that includes the point item of content's latitude and longitude can be selected as a content cell.

As another example, for each line item of content, the computing system can designate each cell in at least one cell layer that includes any of two or more locations respectively associated with two or more points of the line item of content as included in the set of content cells. For example, each endpoint of the line (and/or inflection point for polylines) can be treated the same as a point item of content as described above. Thus, in such example, cells of the maximum layer that enclose endpoints of the lines will be added to the set of content cells, but not the cells along the line between the two endpoints.

As yet another example, for each polygon item of content, the computing system can designate one or more cells that are entirely included within the polygon item of content as included in the set of content cells. For example, the computing system can determine an interior cell coverage of the polygon between the maximum layer and a minimum layer can be added to the set of content cells. In particular, in one example, the computing system can perform or cause to be performed an interior cell coverage algorithm that determines the interior cell coverage for the polygon. The interior cell coverage algorithm can be an optimization algorithm that minimizes the number of cells used and/or satisfies a maximum number of cells constraint while maximizing the percentage of the polygon that is included within the interior cell coverage. The selected cells can be constrained to be within the range between the maximum layer and the minimum layer. Additional or alternative constraints or objectives can be used as well. The interior coverage of the polygon does not include cells which include locations that are outside the polygon boundary.

One reason for using the interior coverage is to avoid adding neighbor locations in case the polygon item of content represents a certain semantic entity like a country, city, etc. As an example, consider a polygon item of content that follows the boundary of the city of Mountain View, Calif. The complete cell coverage of that polygon covers cells that include overlapping neighbor cities. Thus, simply selecting any and all cells which include any portion of the polygon will cause the content cells to include portions of neighbor cities such as Sunnyvale, Palo Alto, or other neighboring semantic entities. However, selecting only cells that correspond to the interior coverage of the polygon will result in content cells that only include the city of Mountain View.

As noted above, in some implementations, for all content cells identified using the above principles, the computing system can add all parent cells up to the minimum layer to the set of content cells. Since parent cells may end up containing multiple child cells, in some implementations the resulting table can be aggregated by Map ID to the following table: Cell ID→(Map ID, Count).

According to another aspect, a computing system implementing the present disclosure can determine a plurality of sets of feature cells respectively for a plurality of semantic entities. For example, the set of feature cells determined for each semantic entity can be descriptive of a geographic area associated with such semantic entity. Generally, the term semantic entity refers to an entity that has some human context or meaning and that has an associated geographic area. Example semantic entities can include political entities (e.g., cities, counties, states, countries, congressional districts, etc.) continents bodies of water regions (e.g., the San Francisco Bay Area, the 1-5 Corridor, the Cascadia Bioregion, the Columbia River Watershed, etc.) parks (e.g., city parks, state parks, national parks) neighborhoods property boundaries business entities (e.g., a mall or shopping center, a campus, a factory, a building) various segmentations of geographic area according to various criteria (e.g. according to population demographics) or other geographic areas or places that have some human meaning or context.

In some implementations, the plurality of semantic entities can be organized into a number of levels. For example, the semantic entity of “Utah” may be one level below the semantic entity of “United States of America.” In some implementations, the level of a semantic entity can correspond to or otherwise depend on its size. In some implementations, the level of a semantic entity can generally correspond to a type of semantic entity (e.g., a neighborhood may be one level below a city).

As noted above, the computing system can determine a set of feature cells for each of a number of semantic entities. As one example, in some implementations, the computing system can obtain a boundary associated with a semantic entity from a database. For example, a database associated with a geographic information system can provide information descriptive of boundaries for each of the plurality of semantic entities. For example, each boundary can be defined by one or more polygons. In some implementations, the computing system can generate a cell coverage between the minimum layer and the maximum layer for each location included in within the boundary of the semantic entity. In some implementations, the cell coverage can result in the following table: Cell ID→Semantic Entity ID. The cell coverage for a semantic entity can be used as the set of feature cells for such semantic entity.

In particular, in one example, the computing system can perform or cause to be performed a cell coverage algorithm that determines the cell coverage for the semantic entity. The cell coverage algorithm can be an optimization algorithm that minimizes the number of cells used and/or satisfies a maximum number of cells constraint while ensuring that the cell coverage covers an entire geographic area associated with the semantic entity. In addition, the cell coverage algorithm can attempt to minimize an amount of geographic area that is included in the cell coverage but not associated with the semantic entity. The cells selected for the cell coverage can be constrained to be within the range between the maximum layer and the minimum layer. Additional or alternative constraints or objectives can be used as well. The cell coverage of the semantic entity may include cells which include locations that are outside a boundary associated with the semantic entity.

According to another aspect, a computing system implementing the present disclosure can compare the set of content cells to the respective sets of feature cells for at least a portion of the plurality of semantic entities. For example, the computing system can determine, for each semantic entity, a percentage of the set of content cells that are included in the respective set of feature cells for such semantic entity. For example, if the set of feature cells for a particular semantic entity includes two content cells and there are eight content cells total, then the percentage determined for such semantic entity can be twenty-five percent. Measures other than the percentage described above can be used as well.

According to another aspect of the present disclosure, the computing system can select at least one of the plurality of semantic entities as a semantic location for the map based at least in part on the comparison of the set of content cells to the respective set of feature cells for such at least one semantic entity. For example, the computing system can select each semantic entity for which the percentage of the set of content cells that are included in the respective set of feature cells for such semantic entity exceeds a threshold percentage. To provide an example, if the set of feature cells for a particular semantic entity includes six content cells and there are eight content cells total, and if the threshold percentage is fifty percent, then such semantic entity can be selected as a semantic location for the map.

In some instances, maps can be spread across multiple locations. As such, in further implementations of the present disclosure, if there is a small number of semantic entities (e.g., three or less) at the same level that, when combined, exceed the threshold percentage, then each of those semantic entities can selected as a semantic location for the map. Thus, in some implementations, the computing system can identify a combination of two or more semantic entities that share a level and for which the percentage of the set of content cells that are included in a combined set of feature cells for such combination of two or more semantic entities exceeds a threshold value.

In addition, in some instances, semantic entities can be enclosed by parent semantic entities that are just slightly larger (or even identical). For example, the City of San Francisco is nearly the same size as the County of San Francisco. As such, in yet further implementations of the present disclosure, the computing system can identify a parent semantic entity for each semantic entity that has been designated as a semantic location for the map. A size factor can be determined for each identified parent semantic entity. The size factor can describe the size of the geographic area associated with such parent semantic entity relative to the size of the geographic area associated with one or more semantic entities that are children of such parent semantic entity and have been selected as semantic locations for the map. If the size factor for a particular parent semantic entity is less than a threshold factor value (e.g., 1.5), then such parent semantic entity can also be selected as a semantic location for the map.

According to another aspect of the present disclosure, in addition to semantic locations, a map parent location can be selected for a map. For example, a similar technique to that described above can be used to select the map parent location from semantic entities that are larger than the ones identified as semantic locations. As an example, the computing system can identify a map parent location that includes greater than a threshold percentage of the selected semantic locations. The threshold percentage can be the same or a different value than the percentage used to select the semantic locations.

According to yet another aspect, the systems and methods of the present disclosure can determine a proximity score for the map relative to one or more other maps. In particular, in some implementations, a map viewer application that enables a user to view a map can include a Related Maps section, in which one or more related maps are recommended or otherwise identified and/or displayed to the user. Thus, a computing system implementing or otherwise communicating with the map viewer application can use the proximity score for a pair of maps to determine whether to recommend one of the pair of maps to a user that is viewing the other of the pair of maps. For example, the computing system can identify maps to present within the Related Maps section.

In some implementations, the proximity score for a first map and a second map can be based at least in part on one or more first semantic locations associated with the first map and one or more second semantic locations associated with the second map. As described above, the proximity score can be used to determine whether to provide the second map as a related map for the first map.

In some implementations, the proximity score for the first and second maps can be based at least in part on a number of shared neighbor locations between the first semantic locations associated with the first map and the second semantic locations associated with the second map. For example, a neighbor table can define adjacent semantic entities of the same level or type for each semantic entity.

Thus, for proximity score calculation, some or all of the following locations per map can be considered: the semantic locations for each map the map parent location for each map and/or the neighbors of the semantic locations for each map. If needed or desired, this list can be extended to add, for example, parent neighbors, neighbor's neighbors, or other potential comparisons.

In particular, according to another aspect of the present disclosure, one example technique to calculate proximity between two maps using two respective sets of map locations (e.g., semantic locations, parent locations, and/or neighbor locations) is to determine a ratio of common locations (e.g., a size of the intersection of the two sets of map locations) over the total number of locations (e.g., a size of a union of the two sets of map locations). Use of such a ratio generates a score between zero and one, which can be useful in a number of circumstances. One possible variation on this technique is to normalize locations by inverse size and/or to weight locations by type (e.g., semantic locations can be weighted stronger than neighbor locations and/or parent locations).

With reference now to the Figures, example embodiments of the present disclosure will be discussed in further detail.


FAO involvement in information and surveillance systems development for Transboundary Animal Diseases

The preparation of this manual is testimony of FAO's commitment to assist developing countries with development of their own early warning systems. Via the EMPRES programme, FAO is involved at national, regional and global level with the development of disease early warning systems. The ultimate vision is a global network, linking member countries in an information network that will enable rapid disease reporting, and quick dissemination of information.

This network will be a part of the Global Early Warning System (GEWS) being established by FAO to cover all possible pests, diseases and natural disasters.

EMPRES is currently involved in the development of a three-tiered information system which will gather, process and disseminate information. It is essentially a computerised system, to be known as the Transboundary Animal Disease Information system (TADInfo). This will consist of three different software modules: TADInfo National, TADInfo Regional and TADInfo Global.

For countries lacking properly developed epidemiological software, TADInfo National will be available free of charge. Through the well-established TCP system, FAO will be able to assist with information system development and software installation. TADInfo National is designed to feed information upwards to TADInfo Regional, which will be installed at the level of collaborating regional organisations or projects (such as PARC, SADC or PANAFTOSA) or at regional/subregional FAO offices. Where countries already use their own internally developed software, provision can be made for feeding-in of information from these systems.

Finally, the regional TADInfo modules will feed information to the global module, located in FAO headquarters.

Basically, the functions of the different modules will be:

at national level: storage and analysis of disease information to facilitate local decision-making.

at regional level: regional early warning, regional support and co-ordination.

at global level: risk modelling, trend monitoring and global early warning.

FAO will also take the initiative of organising regional workshops for veterinary epidemiologists to share and disseminate information on disease surveillance.

At the Annual OIE General Session, held in Paris, France in May 1998, FAO was given the mandate, along with OIE and WHO, to build a global information system for disease early warning. This resolution (no. XIII of the 66 th General Session) supports an earlier mandate from the 1996 World Food Summit.

The FAO is fully committed to this ideal, and will continue to work towards it via:

Software (TADInfo) development
In-country support in the form of TCPs
Regional workshops
The EMPRES Livestock Website on the Internet
and electronic mail discussion groups.

Interested CVOs and national epidemiologists should contact their nearest FAO office to enquire about the ways in which FAO can assist with the building of national and regional information systems.


Guide on How to Do GIS Mapping Homework

Even before looking at how to do GIS mapping, it is crucial first to define this term. A geographic information system (GIS) refers to a program or software that is used to capture, store, check, and display data that relates to various positions on the surface of the earth. A lot of data can be displayed on a single map by the GIS. Such data can comprise of the buildings, the streets, and the vegetation.

Differentiating between GIS and Spatial Analysis
In addition to seeking GIS homework help on how to do GIS mapping, most GIS students also seek online advice on how to differentiate between GIS and spatial analysis. Spatial analysis tends to confuse most people, just like how to do GIS mapping. As stated earlier, GIS is a computer that allows a user to capture, store check, and display data relating to various positions on the earth’s surface. Spatial analysis, on the other hand, refers to the evaluation focusing on the statistical analysis of different underlying patterns and processes.

It tends to answer the question ‘what could be the genesis of this observed spatial pattern?’ Spatial analysis is, therefore, an exploratory process that quantifies the observed pattern and then tries to explore the methods that are believed to have generated that specific pattern. The geographic information systems use spatial analysis to bring more insight into various geographical questions. Understanding the differences makes it easier for one to comprehend how to do GIS mapping.

Components of GIS

Before looking at how to do GIS mapping, the first step a student should take is understanding the elements that are required to make a GIS successful. These components include:

  1. Hardware-This refers to the computer device that the GIS operates on. Currently, the geographic information system tends to run on multiple types of hardware. These include desktop computers, centralized computers, among others.
  2. Software-The software entails the tools and functions required to store, evaluate, and display the captured geographic information.
  3. Data-This is probably the most significant component of the GIS. The geographic and tabular data can be gathered in-house, altered according to custom requirements and specifications, or bought from a data provider.
  4. People-Without people, the GIS technology would be of minimal to no value. It is because the people help in developing and implementing the changes and data to the existing real-world problems. The geographic information system users include a lot of people from various fields. For example, it entails technical specialists who design and ensure that the system is well maintained.
  5. Methods-This component of the GIS encompasses the various unique rules, models, and implementation plans that organizations use to operate their GIS successfully. It is a concept one must learn before learning how to do GIS mapping.

The Subsystems of the Geographic Information System

Another significant concept to understand before learning how to do GIS mapping is the subsystems of the GIS. They will come in handy when one is now practically being taught how to do GIS mapping. Since most students do not pay attention to these subsystems, they tend to seek GIS homework help when they are tested in this sector. If you want to ace how to do GIS mapping, then take note of the following GIS subsystems

  1. Data input-This is the subsystem that enables an individual to capture, collect, and also transform the thematic and spatial data into a digital form. This subsystem is acquired by combining aerial photographs, a couple of hard copy maps, reports, survey documents, remotely sensed images and many more.
  2. Data storage and retrieval-This GIS subsystem organize the attribute and spatial data in a manner that enables the user to retrieve the information for evaluation quickly. It also allows the user to update the database promptly. This subsystem requires the use of a database management system (DBMS) for one to maintain the attribute data. The spatial data is encoded and then maintained in a file format.
  3. Data manipulation and analysis-This GIS subsystem grants the user an opportunity to not only define but also execute attribute and spatial procedures, to acquire information that is derived. The data manipulation and analysis subsystem is regarded as the heart of the GIS.
  4. Data output-The data output is another subsystem of the geographic information system that allows an individual to generate maps, graphic displays, and tabulated reports that represent the derived information outcome.

GIS Data Types

Learning how to do GIS mapping also requires one to understand the various GIS data types. The geographic information system uses two types of data. These are:

  1. Spatial data-This kind of data tends to explain the relative and absolute location of several geographic features thoroughly.
  2. Attribute data-This type of data discusses the distinct characteristics of the spatial features. These aspects can either be qualitative or quantitative. In most cases, this type of data is known as tabular data.

GIS Data Models

When one is learning the concept of how to do GIS mapping, they are also required to understand the models they need to use. Therefore, it is essential to understand the existing GIS data models. Only one model can be chosen at a time. The GIS data models refer to the constructs or the set of rules that are used to explain and represent the geographic aspects of the real world in the computer. The data models for the geographic information system include:

Raster Data Models

This data model is widely used not only in the geographic information systems but also in other applications. It is, for instance, used in digital photography. These data models embody the usage of a grid-cell structure. In this structure, geographic information is split into cells that are identified by columns and rows. Such a data structure is what is referred to as a raster. Although this term refers to a more regularly spaced grid, other data structures that are tessellated also exist in the GIS grid-based systems.
For one to encode raster data from nothing or from scratch, one can use several techniques. These models or methods include:

  1. Cell-by-cell raster encoding-This technique encodes a raster merely through the creation of records for each cell value and by column and row.
  2. Run-length raster encoding-Using this technique, cell values are encoded in runs containing similarly valued pixels, an aspect that can bring about a highly compressed picture file.
  3. Quad-tree raster encoding-This technique entails the division of a raster into a hierarchy of quadrants, which are then split based on the similarly valued pixels,

There are various advantages of using the raster data model. These include

  1. There is ample access to the technology needed to create the raster graphics since it is inexpensive and pervasive.
  2. The data structure of the raster graphics is very straightforward, making it a great model to use due to the absence of complexities.
  3. The relative simplicity of the raster graphics makes performing of the overlay analyses also very easy.

There are also disadvantages of using the raster data model. They entail

  1. The presence of huge raster files. It results in added pressure in keeping up with the quality and quantity of the computer resources required to support these large files.
  2. When the output images recovered using the raster model are compared to their vector counterparts, those of this model tend to be less appealing or pretty. The less attractive aspect is noticed, particularly when one zooms or enlarges the image.

Vector Data Models
The vector data models utilize the vectors, which are directional lines to represent a specific geographic feature. The vector data is therefore characterized by the use of several vertices or sequential points in the definition of a linear segment. Thus, each vertex comprises a Y and X coordinate.

The vector lines are popularly known as arcs, which consist of a series of vertices that are terminated by a node. A node can be explained to be a vertex starting or ending an arc segment.
The vector data models are preferred for various reasons. These benefits include

  1. Most data, for example, the hard copy maps, are present in vector form. Therefore, no data conversion is needed.
  2. The vector data model enables efficient encoding of topology. Due to this, there tend to be more practical operations requiring topological information such as network analysis and proximity.
  3. Unlike the raster data model, the vector data models tend to be more realistic representations of the real world due to their precision of lines, points, and polygons.
  1. When comparing it to the relatively simple raster data model, the vector model tends to be very complicated. This model has no shortcuts when it comes to storing data, making it very complicated to use.
  2. The algorithms required for the analysis and manipulative functions are very complicated and can be intensive. As a result, the complexity tends to significantly limit the functionality of massive data sets, such as a large number of geographic features.
  3. It is impossible to conduct a spatial evaluation and filtering within the polygons using these data models.
  4. It is hard to represent continuous data like the elevation data in vector form. In most cases, interpolation or the generalization of substantial data is needed for these layers of data.

Attribute Data Models
Besides how to do mapping, most individuals also seek GIS homework help on identifying the various attribute data models. The attribute data models are located within the GIS software in an external database management software. The most known attribute data models include

  1. Tabular model-This model stores attribute data in sequential data files that have fixed formats. The tabular model is obsolete in the GIS area of interest due to various reasons. One is because there is no manner of checking data integrity.
  2. The hierarchical model-This model tends to organize data in a tree structure. Data tends to be structured downward in a hierarchy of tables.
  3. Network model-This model arranges data in a plex or network structure. In this model, any column in the plex can be associated with any other.
  4. The relational model-This model classifies data in tables. Each table is given and identified by a particular table name and is split into columns and rows. Each column in the table is also presented a specific identification name. In the table, rows tend to represent one record.

List of GIS Data Formats
There are a couple of GIS data formats that one can use after learning how to do GIS mapping. These include

  1. Vector GIS file formats- These file formats are used to store vector data. Vector data does not comprise of grids of pixels but rather vertices and paths. The three significant symbol types for vector data include polygons, points, and lines.
  2. Raster GIS file formats-These formats contain raster data that comprises of pixels. The pixels are square and are regularly-spaced. They are regularly used by people who have just learnt how to do GIS mapping due to their simplicity.
  3. Compressed raster file formats-These file formats reduce the size of the file by permanently deleting redundant information.
  4. Geographic database file formats-These formats are used to store geographic data.
  5. LiDAR file formats-These GIS formats allow users who have learnt how to do GIS mapping an opportunity to use Light Detection and Ranging (LiDAR) technology to view the earth’s surface.

The GIS Mapping Software –open source and commercial (pay for)
A user learning how to do GIS mapping but be familiar with the software to use. The GIS software allows an individual to produce maps and any other graphic image of geographic information for presentation and analysis. The software is very beneficial because it helps in visualizing the spatial data, which helps in building credibility and supporting decision making in the organization. A GIS typically stores the data on various geographical features and their distinct characteristics.
The features tend to be indicated as either lines, points, or as raster images. Therefore, in the case of the map of a specific city, the data can be stored in the form of points. The road data, however, can be saved as lines, with the boundaries being stored as areas, and the aerial images or scanned maps being stored as raster images. There are plenty of GIS software. These are classified into either open or commercial mapping software.

Examples of the best GIS Software
There have been questions that have been asked regarding the best software to use when mapping. One of the best software to use is QGIS. It is one of the highly ranked open mapping software and is considered by many as a jackpot. This is because the software allows one to automate the production of maps, quickly process geospatial data, and efficiently generate the drool-worthy cartographic figures. It makes mapping work much more manageable, making it a favorite of many.

Another highly preferred commercial software is ArcGIS. It is also known as Esri and came into the geospatial scene back in the 1970s. Esri ArcGIS is considered the most innovative and cutting-edge geographic information science software due to its fantastic features. Its extensions are efficient, and it has an exceptional network analyst that makes it unbeatable by other commercial GIS software. Maptitude GIS is another popular software that students learn when they are learning how to do GIS mapping.

Specific Geography
This mapping software grants an individual the tools, maps, and data they require to analyze and comprehend how the specific geography in question affects them and their firm. Currently, Maptitude is the most efficient and cheap, full-featured software available. It has been specially designed to allow an individual to visualize the data in question. It also allows them to analyze the geography. Maptitude comes hand in hand with a comprehensive library of nationwide and worldwide maps, comprising of in-depth United States demographics, street maps, and boundaries like the ZIP Codes and Census Tracts.

This software executes a relational and professional-strength database, a critical aspect for GIS software. When using the Maptitude software, attribute data can be freely attached to and detached from tables and geographic layers. The relational data manipulation aspect is integrated with powerful and robust geoprocessing for polygon overlay, spatial queries, and other location-based analysis. All these exceptional features of Maptitude make it the best GIS software currently.

Uses of the GIS
If you learn how to do GIS mapping, you get to use this program in the following ways
1. To map the exact location of things
Learning how to do GIS mapping helps the user to map a specific region. The computer device can be used to pinpoint the exact spatial location of particular features in the world. It also allows an individual to visualize the existing spatial relationships in these features.
2. To map quantities
People who have mastered how to do GIS mapping get to map portions to discover which areas have the most and least features. As they look at the quantity distribution of such features, they also get to determine the existing relationships between these areas.
3. To map densities
Sometimes, a person who has learnt how to do GIS mapping may be required to map the concentrations or quantities that are normalized by a specific location or by their total number. In such a case, the best software to use is the GIS.
4. To determine the features inside a specific area
When one learns how to do GIS mapping, they get to learn about the elements within a particular region. The GIS program helps to identify what is inside a specific location. It determines the features in that locality and also the distinct characteristics. The characteristics are identified through the creation of specific criteria that define that particular area of interest (AOI).
5. To identify what is near your area of interest
Learning how to do GIS mapping also helps one to discover their surroundings. It is possible to determine the proceedings within your set AOI by using geoprocessing tools such as BUFFER.
6. To map change
When you learn how to do GIS mapping, you get to identify how to allocate change. One needs to map change since it helps one to anticipate future conditions. The GIS identifies specific changes in the determined geographic region, to help an individual determine the course of action or to validate the use of the results in the decision making process.

7. To discover brewing relationships
GIS technology can also be utilized to identify comparisons in various locations. For example, by using GIS, one can map an area that has a manufacturing company and a river nearby. The mapping can help identify if the pollution produced at the manufacturing company affects the river nearby, or whether the river faces any risk.
It is undeniable that the GIS program comes in handy in various ways. Due to this, Geographic Information Science students must know how to do GIS mapping. In most cases, students struggle with this concept. The good news is, today, students can acquire ideal GIS Homework help from expert writers at GIS homework. Therefore do not miss out on attaining all these benefits. Learn how to do GIS mapping at GIS homework.

GIS Careers
Students who are majoring in Geographic Information Science or Geography and specialize in Spatial Analysis can undertake any of the following careers:

  • Cartographer for the National Geospatial-Intelligence Agency or the United States of America Geological Survey
  • Project Manager for Woolpert, Inc.
  • Solutions Engineer
  • Shipper/Route Delivery Manager
  • Market Researcher/Analyst
  • Computer Systems Manager
  • Geospatial Analyst/Consultant for a metropolitan planning organization
  • Manager or Planner: transportation, health services, urban development, land use
  • Real Estate Appraiser/Researcher
  • Information Technology
  • GIS Software Developer for Esri
  • Web Developer or Designer
  • Location/Site Selection Expert

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Geography, GIS and all that - PowerPoint PPT Presentation

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Tool combination model based on task sequence using an optimized orientation genetic algorithm

As an application field of service science, cloud manufacturing (CM) has been widely studied in vertical and horizontal services. At present, CM’s vertical service research is limited to the technical research of specific processing and manufacturing and gives less consideration to the background of horizontal integration services. The research on horizontal service judges the effect of the combination only according to quality of service (QoS) attributes. In the actual processing process, the combination’s effect depends not only on QoS but also on the geographical aspects, company, and other attributes. Based on this, we propose a tool combination model based on the task sequence (TCMbTS). The TCMbTS establishes the association ontology model of tool resources and tasks and proposes a mapping method to provide vertical service for CM. Then proposed optimized orientation genetic algorithm (OPOGA) to solve the combination optimization and recommendation problem of TCMbTS and provide horizontal services for CM. Finally, an experiment is performed to verify OPOGA with validity and efficiency.

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