This data set is no longer being updated and is historical, last update 10/10/2022.A summary of reported data by age groups from Contact Tracking and Tracking (CTT) for Jefferson County, KY. This data provides weekly counts as reported on the MMWR week by age groups for both total cases and cases of non-congregate setting only. Fieldname Definition REPORTED date/time stamp indicating when the data was reported FORMRECEIVED_WEEK_ENDING last date of the week for the aggregate grouping AGE_GROUP lower limit of the age group identified CONFIRMED_by_age number of confirmed cases in age group during week identified non_CongregateSetting number of confirmed cases in non-congregate setting by age group during week identified CongregateSetting number of confirmed cases in congregate setting by age group during week identified Note: This data is preliminary, routinely updated, and is subject to change.For questions about this data please contact Angela Graham (Angela.Graham@louisvilleky.gov) or YuTing Chen (YuTing.Chen@louisvilleky.gov) or call (502) 574-8279.

The evolution of a software system can be studied in terms of how various properties as reflected by software metrics change over time. Current models of software evolution have allowed for inferences to be drawn about certain attributes of the software system, for instance, regarding the architecture, complexity and its impact on the development effort. However, an inherent limitation of these models is that they do not provide any direct insight into where growth takes place. In particular, we cannot assess the impact of evolution on the underlying distribution of size and complexity among the various classes. Such an analysis is needed in order to answer questions such as 'do developers tend to evenly distribute complexity as systems get bigger?', and 'do large and complex classes get bigger over time?'. These are questions of more than passing interest since by understanding what typical and successful software evolution looks like, we can identify anomalous situations and take action earlier than might otherwise be possible. Information gained from an analysis of the distribution of growth will also show if there are consistent boundaries within which a software design structure exists. The specific research questions that we address in Chapter 5 (Growth Dynamics) of the thesis this data accompanies are: What is the nature of distribution of software size and complexity measures? How does the profile and shape of this distribution change as software systems evolve? Is the rate and nature of change erratic? Do large and complex classes become bigger and more complex as software systems evolve? In our study of metric distributions, we focused on 10 different measures that span a range of size and complexity measures. In order to assess assigned responsibilities we use the two metrics Load Instruction Count and Store Instruction Count. Both metrics provide a measure for the frequency of state changes in data containers within a system. Number of Branches, on the other hand, records all branch instructions and is used to measure the structural complexity at class level. This measure is equivalent to Weighted Method Count (WMC) as proposed by Chidamber and Kemerer (1994) if a weight of 1 is applied for all methods and the complexity measure used is cyclomatic complexity. We use the measures of Fan-Out Count and Type Construction Count to obtain insight into the dynamics of the software systems. The former offers a means to document the degree of delegation, whereas the latter can be used to count the frequency of object instantiations. The remaining metrics provide structural size and complexity measures. In-Degree Count and Out-Degree Count reveal the coupling of classes within a system. These measures are extracted from the type dependency graph that we construct for each analyzed system. The vertices in this graph are classes, whereas the edges are directed links between classes. We associate popularity (i.e., the number of incoming links) with In-Degree Count and usage or delegation (i.e., the number of outgoing links) with Out-Degree Count. Number of Methods, Public Method Count, and Number of Attributes define typical object-oriented size measures and provide insights into the extent of data and functionality encapsulation. The raw metric data (4 .txt files and 1 .log file in a .zip file measuring ~0.5MB in total) is provided as a comma separated values (CSV) file, and the first line of the CSV file contains the header. A detailed output of the statistical analysis undertaken is provided as log files generated directly from Stata (statistical analysis software).

Australia Tourism Accommodation: By Class: Upscale & Upper Mid Classes with 10 or More Rooms: Property Count data was reported at 1,636.000 Unit in 2017. Australia Tourism Accommodation: By Class: Upscale & Upper Mid Classes with 10 or More Rooms: Property Count data is updated yearly, averaging 1,636.000 Unit from Jun 2017 (Median) to 2017, with 1 observations. Australia Tourism Accommodation: By Class: Upscale & Upper Mid Classes with 10 or More Rooms: Property Count data remains active status in CEIC and is reported by Tourism Research Australia. The data is categorized under Global Database’s Australia – Table AU.Q018: Tourism Accommodation Statistics: Annual.

This data file contains school district-level data from the following two sources: (1) the National Institute of Education (NIE) Special Tabulations of 1970 Census data, retabulated to 1973-1974 school district boundaries, and (2) the 1970 Census of Population and Housing Fifth Count data. The data in this collection were extracted from the 1976-1977 Merged Federal File produced by AUI Policy Research. Since some districts on the 1976-1977 Merged Federal File had consolidated by 1978-1979, NIE Special Tabulations data for these districts were combined. The Census data file was created in three steps. First, a skeleton file was created, containing one record for each school district on the 1978-1979 Merged Federal File. Each record on the skeleton file contained those data items in the School District Identification segment on the Merged Federal File. Second, the NIE Special Tabulations data were merged by the Office of Education (OE) state code and Local Education Agency (LEA) code to the skeleton file. Finally, Census Fifth Count records were merged by OE state code and LEA code to the skeleton file.

Deer group locations and sizes are used in assessing deer populations living on the ‘open range’. ‘Open range’ generally means open areas of habitat used mainly by red deer (for example, heather moorland). From the outset it is important to be clear that although the terms ‘count’ or ‘census’ are used, open range counting enables a population estimate to be made, but with associated error margins. Research has shown that, normally, estimates will vary by between 5 and 16%. In other words if you count 415 deer then the population estimate is at best between 348 and 481 (or at very best between 394 and 435). Open range population counts (and their resulting estimates) are therefore most likely to be useful for setting broad targets or giving an index of deer numbers as opposed to very precise population models. They are also useful for indicating trends in a series of counts. Count information can be obtained by joining table DEER_COUNT_INDEX based on COUNT_ID columns. Both Helicopter and ground counts are included in the data. The majority of the data were collected in ‘white ground’ conditions where the contrast between deer and the background of snow is maximised enabling deer to be more easily spotted. Summer counts of 'Priority' sites are also included where sites have been counted more intensively. Attribute Name / Item Name / Description DIGI_CALVS / Digital Calves / DIGI = counted from a digital photo SUM_STAGS / SUM Stags / DIGI + VIS combined SUM_HINDS / SUM Hinds / DIGI + VIS combined SUM_CALVES / SUM Calves / DIGI + VIS combined SUM_UNCL / SUM Unclassified / DIGI + VIS combined UNCL = unclassified – so generally hinds and calves combined. SUM_TOTAL / SUM Total / Overall total for that group (not necessarily for the 1km2 as there may be 3 or 4 groups in the 1km2 at that point in time. COUNT_ID / COUNT_ID / Provides link to accompanying csv file. DIGI_HINDS / Digital Hinds / DIGI = counted from a digital photo VIS_TOTAL / Visual Total / VIS = counted visually during the count DIGI_UNCL / Digital Unclassified / DIGI = counted from a digital photo UNCL = unclassified – so generally hinds and calves combined. DIGI_TOTAL / Digital Total / DIGI = counted from a digital photo VIS_STAG / Visual Stag / VIS = counted visually during the count VIS_HINDS / Visual Hinds / VIS = counted visually during the count VIS_CALVS / Visual Calves / VIS = counted visually during the count VIS_UNCL / Visual Unclassified / VIS = counted visually during the count UNCL = unclassified – so generally hinds and calves combined. DIGI_STAG / Digital Stag / DIGI = counted from a digital photo

Pairwise comparisons, according to the Wilcoxon test, for the experiments run in this work (left) and the experiments from GS2016 when adopting AE as the evaluation measure. The symbol ‘>’ (resp. ‘’ and ‘

Deer group locations and sizes are used in assessing deer populations living on the ‘open range’. ‘Open range’ generally means open areas of habitat used mainly by red deer (for example, heather moorland). From the outset it is important to be clear that although the terms ‘count’ or ‘census’ are used, open range counting enables a population estimate to be made, but with associated error margins. Research has shown that, normally, estimates will vary by between 5 and 16%. In other words if you count 415 deer then the population estimate is at best between 348 and 481 (or at very best between 394 and 435). Open range population counts (and their resulting estimates) are therefore most likely to be useful for setting broad targets or giving an index of deer numbers as opposed to very precise population models. They are also useful for indicating trends in a series of counts.Count information can be obtained by joining table DEER_COUNT_INDEX based on COUNT_ID columns. Both Helicopter and ground counts are included in the data. The majority of the data were collected in ‘white ground’ conditions where the contrast between deer and the background of snow is maximised enabling deer to be more easily spotted. Summer counts of 'Priority' sites are also included where sites have been counted more intensively.Attribute NameItem NameDescriptionDIGI_CALVSDigital CalvesDIGI = counted from a digital photoSUM_STAGSSUM StagsDIGI + VIS combinedSUM_HINDSSUMHindsDIGI + VIS combinedSUM_CALVESSUM CalvesDIGI + VIS combinedSUM_UNCLSUMUnclassifiedDIGI + VIS combinedUNCL = unclassified – so generally hinds and calves combined.SUM_TOTALSUMTotalOverall total for that group (not necessarily for the 1km2 as there may be 3 or 4 groups in the 1km2 at that point in time.COUNT_IDCOUNT_IDProvides link to accompanying csv file.DIGI_HINDSDigital HindsDIGI = counted from a digital photoVIS_TOTALVisual TotalVIS = counted visually during the countDIGI_UNCLDigital UnclassifiedDIGI = counted from a digital photo UNCL = unclassified – so generally hinds and calves combined.DIGI_TOTALDigital TotalDIGI = counted from a digital photoVIS_STAGVisual StagVIS = counted visually during the countVIS_HINDSVisual HindsVIS = counted visually during the countVIS_CALVSVisual CalvesVIS = counted visually during the countVIS_UNCLVisual UnclassifiedVIS = counted visually during the count UNCL = unclassified – so generally hinds and calves combined.DIGI_STAGDigital StagDIGI = counted from a digital photo

2023 Tract-level Indicators of Potential Disadvantage for the DVRPC Region Title VI of the Civil Rights Act states that "no person in the United States, shall, on the grounds of race, color, or national origin be excluded from participation in, be denied the benefits of, or be subjected to discrimination under any program or activity receiving federal financial assistance.” Under Title VI of the Civil Rights Act, Metropolitan Planning Organizations (MPOs) are directed to create a method for ensuring that Title VI compliance issues are investigated and evaluated in transportation decision-making. There is additional guidance from the FHWA’s Title VI and Additional Nondiscrimination requirements (2017), and FTA’s Title VI requirements and guidelines (2012). The Indicators of Potential Disadvantage (IPD) analysis is used throughout DVRPC to demonstrate compliance with Title VI of the Civil Rights Act.

This assessment, called the Indicators of Potential Disadvantage Methodology, is utilized in a variety of DVRPC plans and programs. DVRPC currently assesses the following population groups, defined by the U.S. Census Bureau:

Youth

Older Adults

Female

Racial Minority

Ethnic Minority

Foreign-Born

Disabled

Limited English Proficiency

Low-Income Census tables used to gather data from the 2019-2023 American Community Survey 5-Year Estimates Using U.S. Census American Community Survey data, the population groups listed above are identified and located at the census tract level. Data is gathered at the regional level, combining populations from each of the nine counties, for either individuals or households, depending on the indicator. From there, the total number of persons in each demographic group is divided by the appropriate universe (either population or households) for the nine-county region, providing a regional average for that population group. Any census tract that meets or exceeds the regional average level, or threshold, is considered an EJ-sensitive tract for that group. Census tables used to gather data from the 2019-2023 American Community Survey 5-Year Estimates. For more information and for methodology, visit DVRPC's website:http://www.dvrpc.org/GetInvolved/TitleVI/ For technical documentation visit DVRPC's GitHub IPD repo: https://github.com/dvrpc/ipd Source of tract boundaries: 2020 US Census Bureau, TIGER/Line Shapefiles Note: Tracts with null values should be symbolized as "Insufficient or No Data". Data Dictionary for Attributes: (Source = DVRPC indicates a calculated field)

Field	Alias	Description	Source
year	IPD analysis year		DVRPC
geoid20	11-digit tract GEOID	Census tract identifier	ACS 5-year
statefp	2-digit state GEOID	FIPS Code for State	ACS 5-year
countyfp	3-digit county GEOID	FIPS Code for County	ACS 5-year
tractce	Tract number	Tract Number	ACS 5-year
name	Tract number	Census tract identifier with decimal places	ACS 5-year
namelsad	Tract name	Census tract name with decimal places	ACS 5-year
d_class	Disabled percentile class	Classification of tract's disabled percentage as: well below average, below average, average, above average, or well above average	calculated
d_est	Disabled count estimate	Estimated count of disabled population	ACS 5-year
d_est_moe	Disabled count margin of error	Margin of error for estimated count of disabled population	ACS 5-year
d_pct	Disabled percent estimate	Estimated percentage of disabled population	ACS 5-year
d_pct_moe	Disabled percent margin of error	Margin of error for percentage of disabled population	ACS 5-year
d_pctile	Disabled percentile	Tract's regional percentile for percentage disabled	calculated
d_score	Disabled percentile score	Corresponding numeric score for tract's disabled classification: 0, 1, 2, 3, 4	calculated
em_class	Ethnic minority percentile class	Classification of tract's Hispanic/Latino percentage as: well below average, below average, average, above average, or well above average	calculated
em_est	Ethnic minority count estimate	Estimated count of Hispanic/Latino population	ACS 5-year
em_est_moe	Ethnic minority count margin of error	Margin of error for estimated count of Hispanic/Latino population	ACS 5-year
em_pct	Ethnic minority percent estimate	Estimated percentage of Hispanic/Latino population	calculated
em_pct_moe	Ethnic minority percent margin of error	Margin of error for percentage of Hispanic/Latino population	calculated
em_pctile	Ethnic minority percentile	Tract's regional percentile for percentage Hispanic/Latino	calculated
em_score	Ethnic minority percentile score	Corresponding numeric score for tract's Hispanic/Latino classification: 0, 1, 2, 3, 4	calculated
f_class	Female percentile class	Classification of tract's female percentage as: well below average, below average, average, above average, or well above average	calculated
f_est	Female count estimate	Estimated count of female population	ACS 5-year
f_est_moe	Female count margin of error	Margin of error for estimated count of female population	ACS 5-year
f_pct	Female percent estimate	Estimated percentage of female population	ACS 5-year
f_pct_moe	Female percent margin of error	Margin of error for percentage of female population	ACS 5-year
f_pctile	Female percentile	Tract's regional percentile for percentage female	calculated
f_score	Female percentile score	Corresponding numeric score for tract's female classification: 0, 1, 2, 3, 4	calculated
fb_class	Foreign-born percentile class	Classification of tract's foreign born percentage as: well below average, below average, average, above average, or well above average	calculated
fb_est	Foreign-born count estimate	Estimated count of foreign born population	ACS 5-year
fb_est_moe	Foreign-born count margin of error	Margin of error for estimated count of foreign born population	ACS 5-year
fb_pct	Foreign-born percent estimate	Estimated percentage of foreign born population	calculated
fb_pct_moe	Foreign-born percent margin of error	Margin of error for percentage of foreign born population	calculated
fb_pctile	Foreign-born percentile	Tract's regional percentile for percentage foreign born	calculated
fb_score	Foreign-born percentile score	Corresponding numeric score for tract's foreign born classification: 0, 1, 2, 3, 4	calculated
le_class	Limited English proficiency percentile class	Classification of tract's limited english proficiency percentage as: well below average, below average, average, above average, or well above average	calculated
le_est	Limited English proficiency count estimate	Estimated count of limited english proficiency population	ACS 5-year
le_est_moe	Limited English proficiency count margin of error	Margin of error for estimated count of limited english proficiency population	ACS 5-year
le_pct	Limited English proficiency percent estimate	Estimated percentage of limited english proficiency population	ACS 5-year
le_pct_moe	Limited English proficiency percent margin of error	Margin of error for percentage of limited english proficiency population	ACS 5-year
le_pctile	Limited English proficiency percentile	Tract's regional percentile for percentage limited english proficiency	calculated
le_score	Limited English proficiency percentile score	Corresponding numeric score for tract's limited english proficiency classification: 0, 1, 2, 3, 4	calculated
li_class	Low-income percentile class	Classification of tract's low income percentage as: well below average, below average, average, above average, or well above average	calculated
li_est	Low-income count estimate	Estimated count of low income (below 200% of poverty level) population	ACS 5-year
li_est_moe	Low-income count margin of error	Margin of error for estimated count of low income population	ACS 5-year
li_pct	Low-income percent estimate	Estimated percentage of low income (below 200% of poverty level) population	calculated
li_pct_moe	Low-income percent margin of error	Margin of error for percentage of low income population	calculated
li_pctile	Low-income percentile	Tract's regional percentile for percentage low income	calculated
li_score	Low-income percentile score	Corresponding numeric score for tract's low income classification: 0, 1, 2, 3, 4	calculated
oa_class	Older adult percentile class	Classification of tract's older adult percentage as: well below average, below average, average, above average, or well above average	calculated
oa_est	Older adult count estimate	Estimated count of older adult population (65 years or older)	ACS 5-year
oa_est_moe	Older adult count margin of error	Margin of error for estimated count of older adult population	ACS 5-year
oa_pct	Older adult percent estimate	Estimated percentage of older adult population (65 years or older)	ACS 5-year
oa_pct_moe	Older adult percent margin of error	Margin of error for percentage of older adult population	ACS 5-year
oa_pctile	Older adult percentile	Tract's regional percentile for percentage older adult	calculated
oa_score	Older adult percentile score	Corresponding numeric score for tract's older adult classification: 0, 1, 2, 3, 4	calculated
rm_class	Racial minority percentile class	Classification of tract's non-white percentage as: well below average, below average, average, above average, or well above average	calculated
rm_est	Racial minority count estimate	Estimated count of non-white population	ACS 5-year
rm_est_moe	Racial minority count margin of error	Margin of error for estimated count of non-white population	ACS 5-year
rm_pct	Racial minority percent estimate	Estimated percentage of non-white population	calculated
rm_pct_moe	Racial minority percent margin of error	Margin of error for