introduction
①The ArcGIS geostatistical analysis module builds a bridge between geostatistics and GIS, making complex geostatistical methods easy to implement in the software, reflecting the trend of people-oriented and visual development. This combination is groundbreaking because, for the first time, GIS applications can quantify the model quality of a predicted surface by measuring the statistical error of the predicted surface.
②The functions of merging, merging and intersecting of ArcGIS software enable us to better deal with the analysis, quantification and numericalization of land use change types, making the results clearer.
Summary
①The impact of human activities on urban environmental quality is becoming more and more prominent with the rapid development of urban economy and the continuous increase of urban population. This time, eight heavy metals, As, Cd, Cr, Cu, Hg, Zn, Pb, and Ni, were sampled and analyzed, and pollution sources and causes were analyzed through Kriging interpolation and geoaccumulation index.
②Use the land use data of Jilin Province in 1980 and 2000 to understand the temporal and spatial changes of land use and master the basic process of land use change research, so as to deepen the understanding of the basic theory of land use and focus on cultivating our ability to analyze and solve problems .
Table of contents
1. Design content and requirements
2. Design content and requirements
2. Curriculum Design Environment
(3) Analyze the changes of cultivated land and construction land in this region from 1980 to 2000.
1. Design content and requirements
1. Course design task content
①: Using geostatistical analysis to complete the analysis of heavy metal pollution in urban surface soil
②: Using ArcGIS to analyze the land use change in some areas of Jilin Province from 1980 to 2000
2. Design content and requirements
①Task 1
(1) What interpolation model is used for spatial interpolation of pollutant concentration? Explain why.
(2) Give the spatial distribution of 8 main heavy metal elements in the urban area, and analyze the pollution degree of heavy metals in different areas of the urban area.
(3) Analyze the propagation characteristics of heavy metal pollutants, and then establish a model to determine the location of the pollution source. And analyze the possible main reasons of heavy metal pollution.
(4) Analyze the advantages and disadvantages of the model you have built, and what information should be collected in order to better study the evolution mode of the urban geological environment? With this information, how to build a model to solve the problem?
(5) Please classify the degree of land pollution according to the risk screening value and risk control value in the "Soil Quality Standard for Construction Land GB36600-2018", and make statistics on the area, quantity and distribution area of the land at each level of pollution degree, and propose corresponding management and control measures. Repair measures.
②Task 2
(1) Find the land use data of all counties and districts in one of Jilin's cities, and merge all the layers in the area by age (1980 and 2000).
(2) Extract the information of cultivated land and construction land (including residential and industrial land) in the area (for the code of land use, see the file "Land Class Code.DOC", the extraction of land use type needs to be the field "**" in the attribute table of the merged layer ***—ID" to find).
(3) Analyze the changes of cultivated land and construction land in this region from 1980 to 2000.
(4) The same method can be used to analyze the changes of other land types (such as cultivated land, grassland, etc.) in the area.
(The codes of each city in Jilin can be found on the Internet. For the meaning of the land codes, see the file "Land Class Code.DOC")
2. Curriculum Design Environment
ArcGIS10.6 and Excel software
3. To achieve the goal
1. Use geostatistical analysis to complete the analysis of heavy metal pollution in urban surface soil, obtain the spatial distribution of heavy metal elements in the urban area and the degree of heavy metal pollution in different areas of the urban area, analyze the reasons, and find out the pollution sources. Propose corresponding control and remedial measures.
2. Analyze the land use change of a certain city in Jilin Province from 1980 to 2000, find problems from the change trend and propose solutions to the problems.
4. Specific design process
Task 1 :
( 1 ) What interpolation model is used for spatial interpolation of pollutant concentration? Explain why.
Interpolation using Ordinary Kriging because by comparing various interpolation methods including Inverse Distance Weighting, Global Polynomial Interpolation, Radial Basis Function Interpolation, Local Polynomial Interpolation, Ordinary Kriging, Simple Kriging The method, Universal Kriging, found that the RMS value of Ordinary Kriging is the smallest and the effect is the best.
( 2 ) Give the spatial distribution of 8 main heavy metal elements in the urban area, and analyze the pollution degree of heavy metals in different areas of the urban area.
1. Import the data of the sampling point into ArcMap and export it as a shapefile.
The result is shown in the figure:
2. Use [Explore Data] of [Geostatistical Analyst] to create [Voronoi Map], and perform classification and statistical fusion.
3. Using the [Geostatistical Wizard] of [Geostatistical Analyst], the ordinary kriging method is used to interpolate the 8 metal elements to obtain the spatial distribution map.
As (arsenic)
The figure shows:
The As content was the highest in the industrial areas in the southwest, higher in the living areas and traffic areas in the west, lower in the living areas, traffic areas, and park green areas in the central and eastern regions, and the lowest in the mountainous areas.
Cd (cadmium)
The figure shows:
The Cd content in the industrial areas and living areas in the Southwest is the highest, the living areas in the middle, and the traffic areas are high, the Cd content in the park green areas in the Middle East is low, and the Cd content in the mountainous areas is the lowest.
Cr (chromium)
The figure shows:
The Cr content in the living area, industrial area and park green area in the southwestern region is the highest, the Cr content in the living area, traffic area, and park green area in the central and eastern regions is low, and the Cr content in the mountainous area is the lowest.
with (copper)
The figure shows:
The Cu content in the living area and industrial area in the southwest is the highest, the Cu content in the living area and traffic area in the central area is higher, the Cu content in the park green area and the living area in the eastern area is lower, and the Cu content in the mountainous area is the lowest.
Hg (mercury)
The figure shows:
The traffic area, industrial area, and living area in Southwest China had the highest Hg content, the living areas in the central part had higher Hg content, and the rest areas had lower Hg content.
Ni
The figure shows:
The living areas, industrial areas, traffic areas, and park green areas in the southwestern region have higher Ni content, the traffic areas, living areas, and park green areas in the central and eastern regions have higher Ni content, and the mountainous areas have the lowest Ni content.
Pb (lead)
The figure shows:
The industrial areas, traffic areas, living areas and park green areas in the southwest region have the highest Pb content, the middle east and southeastern traffic areas, living areas, and park green areas have higher Pb content, and the eastern park green areas and mountainous areas have lower Pb content .
Zn (zinc)
The figure shows:
The industrial area, living area, park green area, and traffic area in the southwest have the highest Zn content, the central living area, traffic area have low Zn content, and the eastern park green area and mountainous area have the lowest Zn content.
( 3) Analyze the transmission characteristics of heavy metal pollutants, and thus establish a model to determine the location of the pollution source. And analyze the possible main reasons of heavy metal pollution.
1. From the above spatial distribution map of the 8 metal elements, it can be seen that the pollutants in this urban area are mainly distributed in the southwest region and some parts of the central region, decreasing from southwest to northeast, and the extreme value is in the southwest region, and the functional areas of heavily polluted areas are mainly traffic Most of the eastern regions are non-polluted areas, so it can be seen that the scope of pollution sources is locked in the southwestern region and some parts of the central region.
2. Use [IDW] in [Interpolation] in [Spatial Analyst Tools] to perform inverse distance weight spatial interpolation on the geoaccumulation index. The field selects the average geoaccumulation pollution index to obtain the spatial interpolation map of the geoaccumulation index, and layer according to the pollution The degree is divided into 5 categories.
3. Use [Focal Statistics] of [Neighborhood] in [Spatial Analyst Tools], use the focal statistics tool to find the maximum value Max within a certain range, select a circle, and select a radius of 30 pixels.
4. Use the [Raster Calculator] of [Map Algebra] in [Spatial Analyst Tools], subtract the interpolation Max of the geoaccumulation index from the original geoaccumulation index interpolation data, and the grid equal to 0 is the gray value (pixel value) of the area The largest grid, that is, the grid with the largest pollution index (that is, the pollution source), and the point where the grid is converted into a vector at the end, can be considered as the candidate point of the pollution source.
The result is shown in the figure:
5. Use the [Extract by Attributes] tool of [Extraction] in [Spatial Analyst Tools], choose to use the SQL statement to select the Value value equal to 1, and obtain the pollutant source point.
The result is shown in the figure:
6. Use the [Raster to Point] tool of [From Rater] in [Conversion Tools] to get the vector point data .
The result is shown in the figure:
7. Symbolize pollution sources to make their spatial distribution more intuitive, and then compare with the spatial interpolation map of the geoaccumulation index.
8. From the previous analysis, it can be seen that the scope of pollution sources is locked in the southwest region and some parts of the central region. From the analysis of the comparison chart, it can be seen that the candidate points of pollution sources generated in areas outside the range of the measurement points obviously do not meet the conditions, so they are deleted, and the candidate points belonging to or close to non-polluted areas and light-moderately polluted areas can only be used as the propagation process. The salient point should be deleted after consideration. The final pollution sources are as follows:
( 4) Analyze the advantages and disadvantages of the model you have built, and what information should be collected in order to better study the evolution mode of the urban geological environment? With this information, how to build a model to solve the problem?
1. Advantages: Intuitive, clear, relatively concise, the method used has a more accurate theoretical basis, and with a large number of images, it gives a more accurate answer intuitively and theoretically
2. Disadvantages: There is still a lack of data for the rationalization of the model, and it cannot be fully three-dimensional; the method used to deal with the problem may have certain errors in some areas, and a large number of approximate calculations are used for interpolation.
3. Information that should also be collected: wind direction, water flow, heavy metal pollution in the city over a period of time (for example, 5 to 10 years), information on changes in functional areas of the city over a period of time.
4. Build a model to solve the problem: add consideration factors, such as wind direction, water flow, etc., so that we can find pollution sources more accurately, and add information on the situation within a period of time (such as 5 to 10 years) and changes in several functional areas of the city Information, we can more accurately analyze the transmission process of heavy metals, in order to solve the problem of pollution control.
(5 ) Please classify the degree of land pollution according to the risk screening value and risk control value in the "Soil Quality Standard for Construction Land GB36600-2018", and count the area, quantity and distribution area of land with pollution levels at all levels, and propose corresponding management and control measures . Repair measures.
1. Add fields [Functional Area] and [Pollution Degree] in the sampling point attribute table, and set the type to text, which is used to store the Chinese character level and pollutant level level corresponding to the functional area code.
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2. Right-click the newly created field and use [Field Calculator] to write a Python script to assist in the calculation
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code show as below:
#计算功能区代码:
def A(a):
if(a==1):
return "生活区"
if(a==2):
return "工业区"
if(a==3):
return "山区"
if(a==4):
return "交通区"
if(a==5):
return "公园绿地区"
#计算污染度代码:
def A(a):
if(a<=0):
return "无污染"
if(a>5):
return "极严重污染"
if(a>4):
return "强-极严重污染"
if(a>3):
return "强污染"
if(a>2):
return "中等-强污染"
if(a>1):
return "中等污染"
if(a>0):
return "轻度-中等污染"The result is shown in the figure:
3. Use the [Create Thiessen Polygons] tool of [Proximity] in [Analysis Tools] to generate Thiessen polygons with points to generate surfaces to show the spatial distribution of pollution.
4. Right-click the properties of the Thiessen polygon, select [Grade Color] under [Number] under the display box of the symbol system, set the field value to the average pollution accumulation index, and select 7 for the class
Then select the [Classification] function to enter the classification setting, select the classification method as manual, and enter the corresponding pollution level division standard in the cutoff value on the right
Enter the corresponding classification level in the label column
The result is shown in the figure:
5. Right-click in the pollution degree field of the attribute table of the Thiessen polygon and use the [Statistics] function to count the area, quantity and distribution area of land with pollution degrees at all levels
Convert the obtained table to xls format
The result is shown in the figure:
Control and remedial measures:
In accordance with the Soil Law, the state implements a registration system for soil pollution risk control and restoration of construction land. If the use is changed to residential, public management and public service land, soil pollution status investigations shall be conducted in accordance with regulations before the change. For construction land plots with pollutant content exceeding the soil pollution risk management and control standards, and after the soil pollution risk assessment, risk control and restoration need to be implemented, it shall be included in the list of construction land soil pollution risk management and control and restoration.
Land plots included in the list of soil pollution risk management, control and restoration of construction land shall not be used as residential, public management and public service land. For construction land plots that have not reached the risk control and restoration goals determined in the soil pollution risk assessment report, it is prohibited to start construction on any projects that are not related to risk control and restoration. Through the above-mentioned construction land access management system, soil control and restoration are ensured.
Task 2 :
( 1 ) Find out the land use data of all counties and districts in one of Jilin's cities, and merge all the layers in this area by age ( 1980 and 2000 ).
I choose Siping City, Jilin
Find the land use data of all counties and districts in Siping City, Jilin Province
The codes of all counties in Siping City are as follows:
| 220302 | Tiexi District |
| 220303 | Tiedong District |
| 220322 | Lishu County |
| 220323 | Yitong Manchu Autonomous County |
| 220382 |
Load the land use data of Siping City in 1980
Combined Land Use Data of Siping City in 1980
The result is shown in the figure:
Load the land use data of Siping City in 2000
Combined Land Use Data of Siping City in 1980
The result is shown in the figure:
( 2 ) Extract the information of cultivated land and construction land (including residential and industrial land) in the area (see the file "Land Class Code.DOC" for the land use code , and the extraction of land use type needs to be the field " ** in the attribute table of the merged layer *** — ID ").
1. Add field ID1980 to the attribute table of Siping 1980 data, add field ID2000 to the attribute table of Siping 2000 data, and use the field calculator to extract the land type codes of each county
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2. Extract cultivated land
Use the [Select] function of [Extract] in [Analysis Tools] to extract the cultivated land area in the area in 1980 and 2000
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The result is shown in the figure:
1980 arable land
Arable land in 2000
3. Extract woodland
The result is shown in the figure:
1980 Woodland
2000 Woodland
4. Extract grass
1980 Meadows
2000 Meadows
5. Extract waters
1980 Waters
2000 Waters
6. Extract urban and rural areas, industrial and mining land, and residential land
Urban and rural areas, industrial and mining land, and residential land in 1980
Urban and rural areas, industrial and mining land, and residential land in 2000
7. Extraction of unused land
1980 unused land
Unused land in 2000
( 3 ) Analyze the changes of cultivated land and construction land in this area from 1980 to 2000 respectively.
1. Add the first-level object field ID in the attribute table of Siping 1980 and Siping 2000 data, and use the field calculator to solve
The result is shown in the figure:
2. Use the [Dissolve] function of [Generalization] in [Data Management Tools] to merge all the first-level land types with the same code
The result is shown in the figure:
3. Use the [Intersect] tool of [Overly] in [Analysis Tools] to count the intersecting areas, and obtain the changes in land use types from 1980 to 2020. According to the field [Shape_Area], the area changes of each area can be known.
4. In order to better see the changes in the area of each region, we use the [Pivot Table] of [Table] in [Data Management Tools] to make a pivot table, which is the feature code of 1980 and listed as the feature of 2000 code, the attribute field is area
The result is shown in the figure:
Use the [Table To Excel] tool in [Excel] in [Conversion] to export the table to xls format, and adjust the format in Excel to get a more detailed land matrix
as the picture shows:
5. Analysis of changes in land use types from 1980 to 2000
①Changes in cultivated land from 1980 to 2000
The cultivated land area increased by 10722－10407＝315 km²
25.29 km² of cultivated land became forest land, 5.09 km² of cultivated land became grassland, 4.58 km² of cultivated land became water, 24.50 km² of cultivated land became construction land, and 0.13 km² of cultivated land became unused land
② Changes in forest land from 1980 to 2000
Forest area decreased by 1703－1842＝﹣139 km²
163 km² of forest land has been converted into cultivated land, 1.32 km² of forest land has been converted into construction land, and 0.26 km² of forest land has been converted into unused land
③ Changes in grassland from 1980 to 2000
Grass area reduced by 299－416＝﹣117 km²
124 km² of grassland has been turned into arable land, 0.16 km² of grassland has been turned into forest land, 1.24 km² of grassland has been turned into water, and 0.72 km² of grassland has been turned into construction land
④ Changes in the water area from 1980 to 2000
The water area is reduced by 301－304＝﹣3 km²
8.58 km² of water area has been turned into cultivated land, and 0.19 km² of water area has been turned into grassland
⑤ Changes in construction land from 1980 to 2000
Construction land increased by 1092－1065＝27 km²
0.05 km² of construction land turned into forest land
⑥Changes in unused land from 1980 to 2000
The area of unused land decreased by 249－332＝－83 km²
77.91 km² of unused land has been turned into cultivated land, 0.24 km² of unused land has been turned into forest land, 4.74 km² of unused land has been turned into grassland, and 0.09 km² of unused land has been turned into construction land
( 4 ) The same method can be used to analyze the changes of other land types (such as cultivated land, grassland, etc.) in this area.
It has been analyzed in question (2) and question (3)
5. Results and analysis
Task 1:
(1) In the living area, the pollution of Cu and Zn reaches medium pollution, Ni pollution is temporarily absent, and the rest is light-moderate pollution; heavy metal pollution is the most serious in industrial areas, Hg reaches strong pollution, Cu reaches medium-strong pollution, and Cd , Pb, and Zn were moderately polluted, and the rest were mildly to moderately polluted; mountainous areas were not polluted by heavy metals; the pollution degree of Hg in the traffic area also reached strong pollution, Cu and Zn reached moderately polluted, Ni was not polluted for the time being, and the rest were mildly polluted Moderate pollution; except that there is no Cr and Ni pollution in the park green area, the rest are mild to moderate pollution.
(2) Main causes of heavy metal pollution: It can be seen from the pollution degree table that the main pollution in this city is Hg, Zn, Cu, Cd, and Pb. Except for mountainous areas, Hg is distributed in other areas, the highest in industrial areas and traffic areas, which may be due to the high degree of Hg pollution in these two areas caused by vehicle exhaust and industrial waste water, while in other areas it may be discarded waste batteries. Zn, Cu pollution is mainly in living areas, industrial areas and traffic areas, which may be automobile exhaust, waste batteries, waste water, etc. Pb and Cd are mainly in industrial areas, probably in industrial wastewater.
Task 2:
① Cultivated land: the area of cultivated land increased by 10722-10407＝315 km², 25.29 km² of cultivated land became forest land, 5.09 km² of cultivated land became grassland, 4.58 km² of cultivated land became water area, and 24.50 km² of cultivated land became construction land , 0.13 km² of arable land became unused land
②Forest land: The area of forest land decreased by 1703－1842＝﹣139 km², 163 km² of forest land became cultivated land, 1.32 km² of forest land became construction land, and 0.26 km² of forest land became unused land
③Grassland: the grassland area decreased by 299－416＝﹣117 km², 124 km² of grassland became cultivated land, 0.16 km² of grassland became forest land, 1.24 km² of grassland became water area, and 0.72 km² of grassland became construction the land
④Water area: The area of water area decreased by 301－304＝﹣3 km², 8.58 km² of water area became cultivated land, and 0.19 km² of water area became grassland
⑤Construction land: construction land increased by 1092－1065＝27 km², and 0.05 km² of construction land became forest land
⑥Unused land: The area of unused land decreased by 249－332＝﹣83 km², 77.91 km² of unused land became cultivated land, 0.24 km² of unused land became forest land, and 4.74 km² of unused land became Grassland, 0.09 km² of unused land turned into construction land
6. Conclusion
Through this internship, I learned how to use ArcGIS for spatial analysis. At the same time, I also performed practical operations to better understand the essence of spatial analysis.