[Music] welcome this is dr cucamoli and in this lecture we are going to talk about how to map gis data and there are lots of different ways you can map the spatial objects and two different methods that we mainly focus on are by using different colors and symbologies so what are these map objects maps objects are spatial data sets such as point line and polygon that they can be represented on a map and the intention is to replicate what is going to happen on the real world they can be also shown by using different colors and symbologies focusing on the attributes of those spatial data for example if you have a location of your customer throughout the whole us what you can do is to make a map based on the amount of purchase that different customers have made over the past part or and that could be a really nice map to kind of see how your uh best customers are distributed so changes in category are expressed by varying symbol shape line type pattern color font so we can use all different types of map attributes to make a map much more meaningful if you have change in quantity they can indicate it by varying symbol size thickness or color for example if these are different symbols you can see that the circle could mean differently compared to a square another thing you can do is to look at the value of that symbol and use different sizes of that symbol same symbol to kind of express the multitude or the magnitude of that value you can also use different types of coloring if you have polygon data you can use different colors to kind of show different attributes or different categories of that attribute you can also for the line symbol you can use thickness or you can of course use the color as well and these are the main types of main ways you can make a map much more meaningful in reality you can use different types of maps to represent your data but that that is also based on the type of data you're dealing with for example if you have nominal data or if you have basically labels or textual data you would use single symbol map to kind of display those type of information and there is an example here you have fire locations and there is one category of fire location point data you use that symbol of course to express the location of the fighter but that sim single symbol is used for that nominal data uh if you have categorical or ordinal data you would use unique values maps and these are theories of different types of unique value maps that are used here so for the volcano types if you have different types of volcano you want to use different symbols as well as colors to express these values of volcano if you have different types of rows you may use the color and thickness to do that and finally for geological layers you may use a polygon layer but you can use unique value maps and every layer of the geology would be displayed by using different colors the way you can use the unique value maps could be determined by combining different values so that is a more advanced way of representing the map but if you just focus on one attribute these are good examples many types of maps are used for numeric data of course because you have value you can use different ways to express the strength or the proportion of the values if you use the graduated color maps that is based on a color ramp you have interest in and based on the value of that layer if you have different polygons some of the polygons they may have let's assume this is the income level some polygons they have lower income there's some zip codes they have higher incomes and you can use different colors to show those and then of course you can see these values uh which are shown by red they're all clustered in the middle you can also use different symbol maps graduated symbol map based on the size of the point value you can use those circle sizes and each one would be related to a specific value and the mosquitoes per trap are displayed on this map you can also use dot density maps basically for every let's say 10 000 value you would use one dot so let's say that's the population or that's the concentration of the population that is going to be a really nice representation of the values on the map but you don't want to color the map you can also use chart maps and basically what you have for the chart map is the series of values and that can be used to express how distribution of those values across different areas are going to be displayed you should know what is a thematic map and basically thematic maps displaying an attribute about the location using colors and that's very common type of maps but depending on the type of data you're dealing with the thematic map would be different for example if you have nominal data if you have text or names those are going to be unique values and it's different from numbers and the way you can use the thematic map is by using a single symbol map we just talked about and you can have some optional labels if you want to just show what is going on on that specific uh layer if you have categorical data that you have certain categories of let's say land use you can use a categorical map and for every single category you can use different color for example agriculture here is shown by yellow and forest is shown by a green color so that map is a category called thematic map if you have ordinal data basically you have ranking of values you can use different types of maps but again unique values would be a good way of representing that type of data for example here we have the snail habited rank and every ranking if you have four ranking here every ranking would be displayed by using different colors another type of data is internal data and that's where you have values along a regular numeric scale for example if you have the pay harsh scale for acidity that is going to be a natural skill that you have and that can be used for displaying your values or if you have population the population change percentage could be shown as a specific categorization of equal interval for example here we have from negative 5.5 to 0 for one category basically less than the population is the publishing change is negative if you have from 0 or 0.1 to 8 that's another category and so on we have from 24.1 to 31.8 and that's based on the data that you have you can have five interval values ratio data is another way of making much more meaningful maps because many times you need to normalize your values you may have lots of people living in los angeles but that doesn't mean that the ratio of one specific variable uh is going to be higher than a rotor area so this example talks about the income by county and then of course if you just look at the counties in the us in 2008 you will see some counties have much higher income but that would be misleading if you don't look at the population of those counties of course the higher the income the overall income that could be a function of more people living in that area what if we normalize those incomes based on the number of people living in those neighborhoods in those zip codes or counties and there we get much different result much different map and that makes a huge difference when you are trying to interpret the result so to map the quantities data you can use the numerical data such as interval ratio but they must be divided into classes before you map them out many map types are suitable for numerical data and that's the basic of creating much more meaningful maps but you can also use additional variables and additional map attributes to make your maps much more meaningful for example you can use symbol size thickness or even colors use different shades of the hue to show the changes or the annual precipitation there is a very famous example that when you aggregate data based on just one specific unit your result might be meaningless for example if you have zip codes not all every zip code in the u.s have the same uh formation and that could mislead your result so that problem known as modifiable aerial unit problem and for shortly called maup and it is basically going to change the result of your analysis and depending on the way you calculate the units the result would be totally different for example if you have the number of farms in the us you get much more farms in texas and california but if you also have the farms per square mile that could be totally different and that's very important because when you have much more land such as in texas and california you would get much more farms that's usually the case but that doesn't mean that you have more farms per square mile also you can get another thing for the housing units if you have more housing units in california and texas doesn't mean that you have more problem you have more vacancies but doesn't mean that you have more issues in terms of the housing units and the demand would be dependent on how many units are based on the top base the total number of units are vacant so you can create another map based on fraction of housing units that are vacant and that is going to give you much more insight so there are two types of maup and one is based on the arbitrary aggregation units like state and counties and if you do that you are going to get a confusing result which probably don't know how to handle that there is another thing is that when you use the visual maup or use just large polygons to display your data the the larger polygons would dominate your map and that's natural because when you have larger states they would have much more uh strength the variable usually are higher if the value is aggregated across the state so you can [Music] use the normalizations step you can make a proportion map and that's where you get much more inside [Music] in the next video we talk about raster data and types of raster data